Blog Archives

Smart Supply Chain Management with AIoT

May 17, 2024May 17, 2024 by editor Comment Closed

AIOT supply chain management

The primary focus of the discussion was on smart supply chain management utilizing AI and IoT. This topic was presented by Mr. CC Lee, our technology trainer and technology public speaker, along with Mr. Tan Kien Leong, the AIoT trainer and solution architect from AIoTmission Sdn Bhd.

The Fourth Industrial Revolution (IR4) brings numerous advanced technologies that can significantly enhance smart supply chain management. Key technologies include:

Internet of Things (IoT): Smart Sensors: Enable real-time tracking of goods and assets, monitoring conditions like temperature and humidity. RFID Tags: Facilitate automated inventory management and asset tracking.

Artificial Intelligence (AI) and Machine Learning (ML): Predictive Analytics: Improve demand forecasting and inventory management. Automated Decision-Making: Enhance supply chain efficiency by optimizing routes, scheduling, and resource allocation.

Blockchain: Transparency and Traceability: Provide an immutable record of transactions, ensuring authenticity and reducing fraud. Smart Contracts: Automate and enforce contractual agreements without intermediaries.

Big Data Analytics: Data Integration: Combine data from various sources for comprehensive insights. Trend Analysis: Identify patterns and trends to improve supply chain strategies.

Cloud Computing: Scalability: Easily scale up or down computing resources based on demand. Collaboration: Facilitate seamless collaboration and information sharing across the supply chain.

Robotics and Automation: Automated Warehouses: Utilize robotic systems for picking, packing, and sorting goods. Autonomous Vehicles: Employ drones and self-driving trucks for delivery and transportation.

Vision AI for sushi detection is an example of shop floor AI that automatically counts the consumption of different types of sushi in retail restaurants. This provides real-time data to the central kitchen, enabling accurate stocking based on actual consumption patterns from various outlets. Since different outlets may have distinct eating or buying preferences for different types of sushi, this data helps tailor stock levels accordingly.

When AI analytics is applied over a period with sufficient data, it can effectively forecast future demand from different areas, ensuring better preparation for upcoming needs.

The above case demonstrated how AI is able to assist in the supply chain management process.

AIOT gender and age detection with AI in targeted advertisement

We showcased Vision AI’s capability to determine a person’s gender and age, which can be extracted at unattended kiosks or vending machines. These machines display videos or images that target the buying audience at remote locations.

if you want to watch this live click at the link below:-

AIoTmission Sdn Bhd, established in 2022 as a subsidiary of Axiomtek (M) Sdn Bhd, is a leading provider of technological training and consultancy services specializing in Artificial Intelligence (AI) and Industrial Internet of Things (IIoT) solutions. Our mission is to drive the Fourth Industrial Revolution (IR4.0) and facilitate digital transformation across Southeast Asia, including Malaysia, Singapore, Indonesia, the Philippines, Thailand, Vietnam, and Myanmar. At AIoTmission, we are dedicated to advancing research and development in AI and IIoT technologies, with a focus on industrial applications such as sensors, gateways, wireless communications, machine learning, AI deep learning, and Big Data cloud solutions. Through collaboration with our valued clients and partners, we deliver innovative solutions tailored to industry needs, enhancing technological capabilities and operational efficiency.

5G for Smart manufacturing

May 10, 2024May 10, 2024 by editor Comment Closed

Welcome everyone to our “Sembang AIoT” live session, where today, on the 10th of May 2024, we delve into the dynamic realm of 5G infrastructure within the sphere of Smart Manufacturing. As the world continues its rapid march towards digitization, the integration of Artificial Intelligence and the Internet of Things (AIoT) into manufacturing processes has become not just a trend but a necessity for staying competitive in today’s market. With 5G technology revolutionizing connectivity, the possibilities for enhancing efficiency, productivity, and agility in manufacturing are boundless. In this session, we will explore the transformative potential of 5G infrastructure, its applications, challenges, and the promising future it holds for the manufacturing landscape. So, let’s embark on this journey together and uncover the intricacies of 5G in Smart Manufacturing!

The picture above show how 5G is able to have its redundant path in avoiding signal breakage due to the obstable.

While 5G technology is not an absolute necessity for Smart Manufacturing, its integration can significantly enhance the possibilities within this domain. Smart Manufacturing primarily revolves around the efficient utilization of data, automation, and connectivity to optimize processes and drive innovation. Here’s how 5G can play a pivotal role in enhancing the possibilities of Smart Manufacturing:

Ultra-Reliable Low-Latency Communication (URLLC): One of the key features of 5G is its ultra-low latency, which enables near-real-time communication between devices and systems. In Smart Manufacturing, where split-second decision-making is crucial, URLLC ensures that critical data is transmitted instantly, allowing for rapid responses to dynamic production environments.

Massive Machine-Type Communication (mMTC): 5G’s ability to support a massive number of connected devices per square kilometer is instrumental in scaling up the Internet of Things (IoT) infrastructure in manufacturing facilities. This enables seamless connectivity between a myriad of sensors, machines, and devices, facilitating comprehensive data collection and analysis for informed decision-making.

High Bandwidth and Throughput: With its significantly higher bandwidth and throughput compared to previous generations of mobile networks, 5G enables the transfer of large volumes of data at lightning speeds. This capability is invaluable in Smart Manufacturing scenarios where high-definition video streams, augmented reality (AR), virtual reality (VR), and other data-intensive applications are utilized for tasks such as remote monitoring, predictive maintenance, and quality control.

Network Slicing: 5G introduces the concept of network slicing, allowing for the creation of virtualized, isolated network segments tailored to specific use cases. In Smart Manufacturing, this enables the allocation of network resources based on the requirements of different production processes, ensuring optimal performance, security, and reliability.

Enhanced Mobility Support: In manufacturing environments where mobility is essential, such as in the case of autonomous mobile robots (AMRs) or wearable devices worn by workers, 5G’s seamless handover capabilities and support for high-speed mobility ensure uninterrupted connectivity and communication.

While 5G is not indispensable for Smart Manufacturing, its adoption can unlock new levels of efficiency, flexibility, and innovation within manufacturing processes. By leveraging the unique capabilities of 5G, manufacturers can create agile, interconnected ecosystems that empower them to adapt to evolving market demands, optimize resource utilization, and drive sustainable growth.

Ultra-low latency offered by 5G technology revolutionizes robotic applications and AI vision in Smart Manufacturing by enabling instantaneous communication and decision-making. In robotic applications, where precision and agility are paramount, ultra-low latency ensures that commands from central control systems reach robots in real-time, allowing for swift adjustments to changing production conditions. This instantaneous responsiveness enhances the efficiency of tasks such as pick-and-place operations, assembly, and material handling, leading to optimized throughput and reduced downtime. Similarly, in AI vision applications, ultra-low latency facilitates rapid data transmission between cameras, edge computing devices, and AI algorithms, enabling real-time analysis of visual data streams. This capability enables AI vision systems to detect defects, anomalies, or safety hazards with unparalleled speed and accuracy, empowering manufacturers to implement proactive quality control measures and ensure compliance with stringent safety standards. Overall, ultra-low latency provided by 5G technology unlocks new possibilities for robotics and AI vision applications in Smart Manufacturing, paving the way for enhanced productivity, quality, and operational efficiency.

AIoTmission Sdn Bhd, established in 2022 as a subsidiary of Axiomtek (M) Sdn Bhd, is a leading provider of technological training and consultancy services specializing in Artificial Intelligence (AI) and Industrial Internet of Things (IIoT) solutions. Our mission is to drive the Fourth Industrial Revolution (IR4.0) and facilitate digital transformation across Southeast Asia, including Malaysia, Singapore, Indonesia, the Philippines, Thailand, Vietnam, and Myanmar.

At AIoTmission, we are dedicated to advancing research and development in AI and IIoT technologies, with a focus on industrial applications such as sensors, gateways, wireless communications, machine learning, AI deep learning, and Big Data cloud solutions. Through collaboration with our valued clients and partners, we deliver innovative solutions tailored to industry needs, enhancing technological capabilities and operational efficiency.

To watch our AIoT sharing on Youtube :
https://youtube.com/live/WAjXbrFXHM0

Industrial Robot for Smart Manufacturing

May 4, 2024May 4, 2024 by editor Comment Closed

Industrial Robots for Smart Manufacturing

Smart manufacturing integrates advanced technologies, including robotics, to optimize processes, improve efficiency, and enhance flexibility. Several types of robots are utilized in smart manufacturing to achieve these goals. Here are some key types:

Industrial Robots: These robots are versatile and programmable machines used for various manufacturing tasks, such as welding, assembly, material handling, and packaging. They are equipped with sensors, vision systems, and sometimes AI algorithms to adapt to changing conditions and interact with other machines or humans.

Collaborative Robots (Cobots): Cobots are designed to work alongside humans in a shared workspace safely. They typically feature advanced safety features and are used for tasks that require close collaboration between humans and machines, such as assembly, inspection, and testing.

Mobile Robots: Mobile robots, including Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs), navigate autonomously within a manufacturing facility to transport materials, components, or finished products between different locations. They optimize logistics processes and increase flexibility in material handling.

3D Printing Robots: Also known as additive manufacturing robots, these machines create three-dimensional objects by depositing material layer by layer. They are used in smart manufacturing for rapid prototyping, customization, and production of complex geometries with reduced material waste.

AGVs with Manipulators: Some AGVs are equipped with robotic arms or manipulators to handle tasks such as picking, placing, or manipulating objects within a manufacturing facility. These hybrid systems combine the mobility of AGVs with the flexibility of robotic manipulation.

Industrial robots play a crucial role in improving productivity and efficiency in manufacturing and industrial processes in several ways:

Automation of Repetitive Tasks: Industrial robots excel at performing repetitive tasks with high speed and precision, reducing the need for manual labor. By automating these tasks, robots can operate continuously without breaks, leading to increased productivity.

Consistent Quality: Robots can execute tasks with consistent precision and accuracy, ensuring uniform quality in manufacturing processes. This consistency reduces the likelihood of defects or errors, leading to higher-quality products and fewer rework or scrap rates.

Increased Throughput: Robots can work at a faster pace than human workers in many cases, leading to higher throughput and production rates. This acceleration in production helps meet demand more efficiently and reduces lead times.

Improved Safety: Robots can be deployed in hazardous or physically demanding environments where it may be unsafe for humans to work. By taking on these tasks, robots enhance workplace safety and reduce the risk of accidents or injuries to human workers.

24/7 Operation: Unlike human workers who require rest periods, robots can operate continuously, enabling round-the-clock production schedules. This capability maximizes equipment utilization and minimizes idle time, thereby optimizing resource efficiency.

Flexibility and Adaptability: Many industrial robots are programmable and can be easily reconfigured or reprogrammed to perform different tasks or adapt to changes in production requirements. This flexibility allows manufacturers to quickly respond to market demands and product variations.

Reduced Cycle Times: Industrial robots can streamline manufacturing processes by minimizing cycle times for tasks such as assembly, machining, or material handling. This efficiency improvement leads to faster production cycles and shorter lead times for delivering products to customers.

Optimized Resource Utilization: By automating processes with robots, manufacturers can optimize resource utilization, including raw materials, energy, and labor. Robots are often more energy-efficient than traditional machinery, and their precise movements minimize material waste.

Data Collection and Analysis: Many modern industrial robots are equipped with sensors and connectivity features that enable data collection during operation. This data can be analyzed to identify opportunities for further efficiency improvements, predictive maintenance, or process optimization.

Overall, industrial robots contribute significantly to improving productivity and efficiency in manufacturing by automating tasks, ensuring consistent quality, increasing throughput, enhancing safety, and enabling flexibility in production processes

Sembang AIOT session this round brought to you by Mr. CC Lee and Tan Kien Loeng brought up an interview with a company who specialize in Palletizing Robotic Arm.

You may click the link below to find out more about it.

https://youtube.com/live/U8SFlQrcXvM

AIoTmission Sdn Bhd, established in 2022 as a subsidiary of Axiomtek (M) Sdn Bhd, is a leading provider of technological training and consultancy services specializing in Artificial Intelligence (AI) and Industrial Internet of Things (IIoT) solutions. Our mission is to drive the Fourth Industrial Revolution (IR4.0) and facilitate digital transformation across Southeast Asia, including Malaysia, Singapore, Indonesia, the Philippines, Thailand, Vietnam, and Myanmar.

At AIoTmission, we are dedicated to advancing research and development in AI and IIoT technologies, with a focus on industrial applications such as sensors, gateways, wireless communications, machine learning, AI deep learning, and Big Data cloud solutions. Through collaboration with our valued clients and partners, we deliver innovative solutions tailored to industry needs, enhancing technological capabilities and operational efficiency.

Understanding Data Security for Digital Transformation

April 26, 2024April 26, 2024 by editor Comment Closed

What are challenges in digital transformation in the building of smart manufacturing.

The digital transformation in manufacturing brings numerous benefits, but it also presents significant challenges to data security. Here are some key challenges:

Increased Attack Surface: As manufacturing processes become more interconnected through digital technologies like IoT devices, cloud computing, and automation systems, the attack surface for potential cyber threats expands. Each new endpoint or connection represents a potential entry point for attackers.

Complexity of Systems: Modern manufacturing facilities often comprise a complex ecosystem of interconnected systems and devices, including legacy equipment that may not have been designed with security in mind. Managing the security of such a complex environment can be challenging.

Data Protection: Manufacturing involves the collection and processing of sensitive data, including intellectual property, trade secrets, and personally identifiable information (PII). Ensuring the confidentiality, integrity, and availability of this data is crucial to protecting business interests and complying with regulations.

Supply Chain Risks: Manufacturers often rely on extensive supply chains involving multiple vendors and partners. Each entity in the supply chain represents a potential security risk, as vulnerabilities in one part of the chain can have ripple effects throughout the entire ecosystem.

Insider Threats: Insider threats, whether intentional or unintentional, pose a significant risk to data security. Employees, contractors, or partners with access to sensitive systems and information may inadvertently compromise security through negligence or deliberately engage in malicious activities.

Rapid Technological Advancements: The pace of technological innovation in manufacturing is accelerating, introducing new tools and capabilities that may outpace existing security measures. Keeping up with these advancements and ensuring that security measures remain effective can be a challenge.

Regulatory Compliance: Manufacturing companies are subject to various regulatory requirements regarding data security and privacy, such as GDPR in Europe or CCPA in California. Compliance with these regulations adds an additional layer of complexity to data security efforts.

Cybersecurity Skills Gap: The shortage of skilled cybersecurity professionals poses a significant challenge for manufacturing companies. Recruiting and retaining qualified personnel to design, implement, and manage effective security measures can be difficult.

Addressing these challenges requires a multi-faceted approach that includes implementing robust security technologies, establishing clear policies and procedures, providing ongoing training and awareness programs for employees, collaborating with partners and suppliers to enhance security throughout the supply chain, and staying abreast of evolving threats and regulatory requirements.

Cybersecurity threats are diverse and constantly evolving, but here’s an overview of some common ones:

Phishing Attacks: Deceptive attempts to obtain sensitive information (such as usernames, passwords, and credit card details) by impersonating a trustworthy entity.

Malware: Malicious software designed to disrupt, damage, or gain unauthorized access to computer systems. This includes viruses, worms, trojans, ransomware, and spyware.

Social Engineering: Manipulating individuals into divulging confidential information or performing actions that compromise security, often through psychological manipulation or impersonation.

Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks: Overloading a system or network with excessive traffic to disrupt its normal functioning, making it inaccessible to legitimate users.

Man-in-the-Middle (MitM) Attacks: Intercepting and possibly altering communication between two parties without their knowledge, allowing attackers to eavesdrop or manipulate data.

SQL Injection: Exploiting vulnerabilities in web applications to inject malicious SQL code, enabling attackers to access, modify, or delete data stored in databases.

Credential Theft: Unauthorized acquisition of usernames, passwords, or other authentication credentials, often through phishing, keylogging, or exploiting weak authentication mechanisms.

Cryptojacking: Illicit use of someone else’s computing resources to mine cryptocurrency without their consent, often accomplished through malware or compromised websites.

IoT (Internet of Things) Vulnerabilities: Exploiting security weaknesses in connected devices and systems, including smart home appliances, industrial sensors, and medical devices, to gain unauthorized access or disrupt operations.

Data Breaches: Unauthorized access, disclosure, or theft of sensitive or confidential data, which can result in financial loss, reputational damage, and regulatory penalties.

Protecting Operational Technology (OT) networks with firewalls involves implementing specialized firewall configurations tailored to the unique requirements and characteristics of OT environments. Here’s how firewalls can be used to enhance security in OT networks:

Segmentation: Firewalls can be deployed to segment the OT network into zones based on logical groupings of devices or functions. For example, separating critical infrastructure devices (such as industrial control systems) from less critical systems (such as employee workstations). This segmentation helps contain the impact of security incidents and restrict unauthorized access to sensitive OT assets.

Access Control: Firewalls enforce access control policies to regulate traffic flow between different zones within the OT network. By defining rules based on source and destination IP addresses, ports, protocols, and application-layer information, firewalls can permit or deny communication as necessary to prevent unauthorized access, limit exposure to external threats, and ensure compliance with security policies.

Intrusion Detection and Prevention: Next-generation firewalls (NGFWs) equipped with intrusion detection and prevention system (IDPS) capabilities can monitor OT network traffic in real-time for signs of suspicious or malicious activity. These systems use signature-based detection, anomaly detection, and behavioral analysis techniques to identify and block known and unknown threats, such as malware, exploits, and unauthorized access attempts.

Deep Packet Inspection: Firewalls with deep packet inspection (DPI) capabilities can inspect the content of network packets at the application layer to detect and block malicious payloads, command-and-control communications, and other security threats hidden within encrypted traffic. This helps protect OT systems from advanced threats that may evade traditional security mechanisms.

VPN and Remote Access Security: Firewalls can secure remote access to OT networks by providing Virtual Private Network (VPN) services and enforcing strong authentication, encryption, and access control policies for remote users and devices. This helps protect sensitive OT assets from unauthorized access and cyber attacks originating from external networks, including the internet.

Logging and Monitoring: Firewalls generate logs and alerts that provide visibility into network activity, security events, and policy violations within the OT environment. By monitoring firewall logs and analyzing security events in real-time, organizations can identify potential security incidents, investigate suspicious behavior, and respond promptly to mitigate risks and minimize the impact of cyber attacks on OT systems.

Overall, firewalls play a crucial role in safeguarding OT networks by providing network segmentation, access control, threat detection and prevention, secure remote access, and monitoring capabilities tailored to the unique requirements of industrial control systems and critical infrastructure environments.

AIoTmission Sdn Bhd, established in 2022 as a subsidiary of Axiomtek (M) Sdn Bhd, is a leading provider of technological training and consultancy services specializing in Artificial Intelligence (AI) and Industrial Internet of Things (IIoT) solutions. Our mission is to drive the Fourth Industrial Revolution (IR4.0) and facilitate digital transformation across Southeast Asia, including Malaysia, Singapore, Indonesia, the Philippines, Thailand, Vietnam, and Myanmar.

At AIoTmission, we are dedicated to advancing research and development in AI and IIoT technologies, with a focus on industrial applications such as sensors, gateways, wireless communications, machine learning, AI deep learning, and Big Data cloud solutions. Through collaboration with our valued clients and partners, we deliver innovative solutions tailored to industry needs, enhancing technological capabilities and operational efficiency.

To watch our live session on youtube channel:-

Utilizing AIoT to enhance Digital Twin capabilities

April 19, 2024April 19, 2024 by editor Comment Closed

Aiotmission sembang AIOT digital twin

How does AI and IoT facilitate the Digital Twin modeling? How well the Digital Twin is able to help the industries in cost reduction?

Above the main topic in the discussion in the live session recently help by AIoTmission.

What exactly is the Digital Twin?

Imagine having an imaginary friend who’s exactly like you, knows everything you know, and does everything you do—but they live in a magical world inside a computer. That’s kind of what a digital twin is for things like machines or buildings! It’s a virtual copy that looks and behaves just like the real thing, and it helps us understand how the real thing works and how we can make it better. So, engineers and scientists use digital twins to explore ideas, test out changes, and even predict what might happen in the real world without having to actually touch or change the real thing. It’s like having a secret double that helps you understand and improve stuff without any real-life consequences!

In manufacturing, digital twins are like having a crystal ball that shows us exactly what’s happening inside our machines and processes, but in a virtual world. Here’s how they help optimize things:

Understanding Current State: Imagine you have a digital twin of a production line. It mirrors what’s happening in real-time, showing you every little detail of how machines are running and how materials are flowing. This deep understanding helps identify bottlenecks, inefficiencies, or areas for improvement.

Testing Changes: Let’s say you want to try a new production layout or adjust machine settings. Instead of making these changes in the real world and risking disruptions, you can test them out on the digital twin first. It’s like running a simulation to see how things would play out without any real-world consequences.

Predictive Insights: Digital twins can analyze historical data to predict future outcomes. For example, they can forecast machine failures based on patterns in sensor data. By knowing when a machine might break down, you can schedule maintenance proactively, minimizing downtime and maximizing productivity.

Optimizing Resources: With a digital twin, you can experiment with different scenarios to find the most efficient use of resources like energy, materials, and time. This might involve tweaking production schedules, adjusting inventory levels, or optimizing supply chain logistics to reduce waste and costs.

Continuous Improvement: Digital twins provide a platform for ongoing optimization. As you gather more data and learn from past experiences, you can fine-tune processes, iterate on designs, and drive continuous improvement across your manufacturing operations.

In essence, digital twins act as your virtual eyes and ears in the manufacturing world, helping you see what’s happening, predict what might happen next, and make smarter decisions to optimize processes and improve outcomes.

SCADA IIOT

Remote IOT sernsor the RTD temperature sensors being monitor at the SCADA Station via the lora Transceiver the radio connection that provide up to 1.5KM of radius coverage. That ease our the integration of shop floor data collection without any hard wiring.

overall architecture of IIOT data acquistion

Temperature sensors data was collected at the Axiomtek IIoT edge gateway which is hosting the data over web API server. Data retrieval is done via the Adistra SCADA Server.

Supervisory Control and Data Acquisition (SCADA) software plays a crucial role in feeding data to digital twins by acting as a bridge between physical systems and virtual representations. Here’s how it works:

Data Acquisition: SCADA systems are designed to collect real-time data from various sensors, instruments, and control devices deployed across industrial processes. These sensors measure parameters such as temperature, pressure, flow rates, and energy consumption, among others.

Data Processing and Visualization: SCADA software processes the raw data collected from sensors, aggregates it, and presents it in a format that is easily understandable to operators and engineers. This processed data is typically displayed in the form of graphs, charts, and dashboards within the SCADA interface.

Data Transmission: Once the data is processed, SCADA systems transmit it to other software applications or platforms, including digital twins. This transmission can occur through various communication protocols such as OPC (Open Platform Communications), MQTT (Message Queuing Telemetry Transport), or RESTful APIs (Representational State Transfer Application Programming Interfaces).

Integration with Digital Twins: Digital twin platforms receive the data transmitted by SCADA systems and use it to update the virtual representations of physical assets and processes in real-time. This data includes information about the current state, performance, and operational parameters of the physical systems, enabling the digital twin to accurately reflect their behavior.

Feedback Loop: Digital twins may analyze the data received from SCADA systems to simulate different scenarios, predict future behavior, or optimize operations. Any insights or recommendations generated by the digital twin can be fed back to the SCADA system or directly to operators for action, creating a closed-loop feedback mechanism for continuous improvement.
In summary, SCADA software serves as a critical data source for digital twins by collecting, processing, and transmitting real-time data from physical systems, enabling virtual representations to accurately mirror their real-world counterparts and support data-driven decision-making and optimization efforts.

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To watch the session live, please click the link below:-

AIoTmission Sdn Bhd, established in 2022 as a subsidiary of Axiomtek (M) Sdn Bhd, is a leading provider of technological training and consultancy services specializing in Artificial Intelligence (AI) and Industrial Internet of Things (IIoT) solutions. Our mission is to drive the Fourth Industrial Revolution (IR4.0) and facilitate digital transformation across Southeast Asia, including Malaysia, Singapore, Indonesia, the Philippines, Thailand, Vietnam, and Myanmar. At AIoTmission, we are dedicated to advancing research and development in AI and IIoT technologies, with a focus on industrial applications such as sensors, gateways, wireless communications, machine learning, AI deep learning, and Big Data cloud solutions. Through collaboration with our valued clients and partners, we deliver innovative solutions tailored to industry needs, enhancing technological capabilities and operational efficiency.

Reduce downtime with AIoT predictive analytic

April 12, 2024April 13, 2024 by cclee Comment Closed

The manufacturer’s potential losses due to unplanned downtime and possible solutions to mitigate them were the central topics discussed during the live sharing session on April 12, 2024, in the “Sembang AIoT” live talk hosted by AIoTmission and Axiomtek Malaysia.

Below are some of the potential losses:

Production Losses: The most immediate impact is the loss of production output during the downtime period. This directly affects the ability to fulfill orders and meet customer demand, potentially leading to missed sales opportunities and dissatisfied customers.

Revenue Losses: With decreased production comes reduced revenue. The longer the downtime persists, the greater the financial impact on the manufacturer’s bottom line.

Wasted Materials: During downtime, raw materials may sit unused or partially processed, leading to wasted resources and increased material costs.

Labor Costs: Even though production may halt during downtime, labor costs often continue as employees may still need to be paid despite not actively working on production tasks.

Overtime and Recovery Costs: Once production resumes, manufacturers may need to implement overtime hours or expedited processes to catch up on missed production targets, resulting in additional labor costs.

Equipment Repair or Replacement: Depending on the cause of the downtime, there may be repair or replacement costs for damaged machinery or equipment, further adding to the financial burden.

Reputation Damage: Extended or frequent downtime can damage the manufacturer’s reputation among customers and suppliers, leading to potential long-term impacts on relationships and future business opportunities.

Overall, the losses incurred during unplanned downtime can be significant, not only in terms of immediate financial impact but also in terms of long-term consequences for the manufacturer’s competitiveness and reputation in the market.

Introducing the AI Data analytics to the to the Plastic injection machine as a solution to reduce or avoid unplanned downtime.

An AI predictive analytics solution for a plastic injection machine could involve the following components:

Data Collection: Gather data from various sources within the manufacturing process, including IIoT sensor data from the injection machine itself (e.g., Vibration,temperature, pressure, cycle time), historical performance data, maintenance records, environmental conditions, and quality control data.

Data Preprocessing: Clean and preprocess the collected data to remove noise, handle missing values, and normalize the data for analysis. This step may also involve feature engineering to extract relevant features from the raw data.

Predictive Modeling: Develop machine learning models to predict potential issues or failures in the plastic injection machine. This could include regression models to predict machine performance metrics (e.g., cycle time, defect rate), classification models to detect anomalies or impending failures, and time series forecasting models to predict future machine behavior.

Feature Selection and Engineering: Identify the most relevant features that contribute to the predictive accuracy of the models. This may involve techniques such as correlation analysis, feature importance ranking, and domain expertise to select the most informative features for prediction.

Model Training and Evaluation: Train the predictive models using historical data and evaluate their performance using appropriate metrics (e.g., accuracy, precision, recall, F1-score). Iteratively refine the models to improve their accuracy and generalization to new data.

Real-time Monitoring and Alerting: Deploy the trained models to a real-time monitoring system that continuously analyzes incoming data from the plastic injection machine. The system can generate alerts or notifications when it detects abnormal patterns or potential issues that require attention from operators or maintenance personnel.

Integration with Maintenance Systems: Integrate the predictive analytics solution with the manufacturer’s existing maintenance management systems to schedule preventive maintenance activities proactively based on the predictions generated by the models. This can help minimize unplanned downtime and reduce the risk of equipment failures.

Continuous Improvement: Continuously monitor the performance of the predictive models in production and collect feedback to refine the models further. This may involve retraining the models periodically with new data to adapt to changing operating conditions and improve predictive accuracy over time. When predictive analytics is applied together with the OEE tracking system, it will ensure the process is running with full efficiency and maximum capacity.

By implementing an AI predictive analytics solution for the plastic injection machine, manufacturers can enhance operational efficiency, reduce downtime, optimize maintenance schedules, and ultimately improve the overall productivity and profitability of their manufacturing processes.

To watch the detail live detail. you may visit our youtube channel at

AIoTmission Sdn Bhd, established in 2022 as a subsidiary of Axiomtek (M) Sdn Bhd, is a leading provider of technological training and consultancy services specializing in Artificial Intelligence (AI) and Industrial Internet of Things (IIoT) solutions. Our mission is to drive the Fourth Industrial Revolution (IR4.0) and facilitate digital transformation across Southeast Asia, including Malaysia, Singapore, Indonesia, the Philippines, Thailand, Vietnam, and Myanmar.

At AIoTmission, we are dedicated to advancing research and development in AI and IIoT technologies, with a focus on industrial applications such as sensors, gateways, wireless communications, machine learning, AI deep learning, and Big Data cloud solutions. Through collaboration with our valued clients and partners, we deliver innovative solutions tailored to industry needs, enhancing technological capabilities and operational efficiency.

Energizing Smart Manufacturing with AI Data Analytic

April 6, 2024April 6, 2024 by editor Comment Closed

Energizing Smart Manufacturing with AI Data analytic

A live session in April presented by AIoTmission AIoT Team began with a discussion on how to energize smart Manufacturing with AI Data Analytics.

Data analytics plays a crucial role in smart manufacturing for several reasons:

Optimizing Processes: Data analytics allows manufacturers to collect, process, and analyze large amounts of data from various sources within the manufacturing process. This enables them to identify inefficiencies, bottlenecks, and areas for improvement, leading to optimized processes and increased productivity.

Predictive Maintenance: By analyzing data from sensors embedded in machinery and equipment, manufacturers can predict when maintenance is needed before a breakdown occurs. This proactive approach reduces downtime, lowers maintenance costs, and extends the lifespan of machinery.

Quality Control: Analyzing data from production processes can help identify defects or anomalies in real-time, allowing manufacturers to take corrective action immediately. This ensures that products meet quality standards and reduces the likelihood of defects reaching customers.

Supply Chain Optimization: Data analytics can provide insights into supply chain dynamics, such as demand forecasting, inventory management, and supplier performance. By optimizing the supply chain based on data-driven insights, manufacturers can reduce costs, improve efficiency, and enhance overall competitiveness.

Energy Efficiency: Analyzing data related to energy consumption can help identify opportunities for reducing energy usage and optimizing energy efficiency in manufacturing operations. This not only lowers operational costs but also contributes to sustainability efforts by reducing environmental impact.

Customization and Personalization: Smart manufacturing enables greater customization and personalization of products to meet individual customer needs. Data analytics can help manufacturers gather and analyze customer data to understand preferences and trends, allowing them to tailor products and services accordingly.

Continuous Improvement: By continuously analyzing data from various aspects of the manufacturing process, manufacturers can identify areas for improvement and implement iterative changes to drive continuous improvement. This data-driven approach fosters innovation and agility in adapting to changing market conditions.

Overall, data analytics is essential in smart manufacturing because it empowers manufacturers to make informed decisions, improve efficiency, enhance quality, and remain competitive in today’s dynamic business environment.

We presented a simulated case with the real live data and system that perform the Data Analytic on predictive maintenance during the session.

Vibration analysis can be one important source of detecting if the process or equipment is starting to behave abnormally and lead to complete failure later on that cause the down time in the manufacturing process. In an example of motor or pump used in the process, the abnormality of the acceleration, velocity and amplitude can be a source of early warning that some parts need to be repaired or replaced.

We used a cooler fan to generate the standard vibration data and fed that to the IIOT edge gateway or 4G IOT gateway router, in this case data are being published to the cloud. The local SCADA in this case , ADISRA SCADA software is used to manage the source of the data, training of the AI model of the data and presenting the inference result from the ML analytic result which is performed at the Cloud level. In the previous session, we mentioned about the the Fog Computing where in order to run the AI or data analytic more efficiently, the fog computer or nodes is used.

Vibrational data collected and presented by SCADA Dashboard.

SCADA Dashboard was created to allow the anomaly detection AI to operate and perform the live data analytic. You can see from the right hand side of the picture above, the green color normal state is shown when the AI detected the current data are within the range. In this case, we are talking about the the group of data detected by the AI engine and it is not the single piece of data. The session ended with the wishes to all Muslims ” Selamat Hari Raya” drive safe to the home town during this celebration.

There are several sub topic discussed in the session like the challenges faced by the manufacturing in implementing of AI and others examples of AI being used in the manufacturing sectors as well.

AIoTmission Sdn Bhd, established in 2022 as a subsidiary of Axiomtek (M) Sdn Bhd, is a leading provider of technological training and consultancy services specializing in Artificial Intelligence (AI) and Industrial Internet of Things (IIoT) solutions. Our mission is to drive the Fourth Industrial Revolution (IR4.0) and facilitate digital transformation across Southeast Asia, including Malaysia, Singapore, Indonesia, the Philippines, Thailand, Vietnam, and Myanmar.

At AIoTmission, we are dedicated to advancing research and development in AI and IIoT technologies, with a focus on industrial applications such as sensors, gateways, wireless communications, machine learning, AI deep learning, and Big Data cloud solutions. Through collaboration with our valued clients and partners, we deliver innovative solutions tailored to industry needs, enhancing technological capabilities and operational efficiency.

to watch the session in AioTmission Youtube channel :-

https://youtube.com/live/qd83Oaol8HM

Fog computing vs Cloud Computing

March 30, 2024March 30, 2024 by editor Comment Closed

Fog computing Vs Cloud

The recent live session, held on March 29th, delved into the comparison between Fog computing and Cloud computing. A dedicated team from AIoTmission highlighted the significance of the fog computing layer within the Edge-to-Cloud computing architecture.

In typical AI and IoT architecture diagrams, the Fog computing layer often remains inconspicuous. This is primarily because fog computing devices or systems are typically integrated into the existing infrastructure, with the exception of new features like data analytics, which are more apparent due to advancements in AI technology.

The key differences among Edge, Fog, and Cloud computing were discussed during the session. Additionally, the main role of Fog computing in enhancing both edge and cloud computing was examined in detail.

These topics were thoroughly explored in episode 42 of the “Sembang AIoT” series.

Edge, Fog, and Cloud computing are three distinct paradigms in the realm of distributed computing, each serving unique purposes and offering specific advantages. Here are the main differences between them:

Location and Proximity:

Edge Computing: Edge computing involves processing data locally on devices that are closer to the data source, such as sensors, IoT devices, or end-user devices. The processing occurs at or near the data source, reducing latency and bandwidth usage.

Fog Computing: Fog computing extends the capabilities of edge computing by introducing an intermediate layer of computing resources between edge devices and the cloud. Fog nodes are placed closer to the edge devices within the same network infrastructure, providing additional processing, storage, and networking capabilities.

Cloud Computing: Cloud computing involves the centralized provision of computing resources over the internet. Data processing, storage, and other computing tasks occur on remote servers maintained by cloud service providers, typically located in data centers.

Latency and Response Time:

Edge Computing: Edge computing offers the lowest latency since data processing occurs locally, allowing for real-time or near-real-time responses to events.

Fog Computing: Fog computing reduces latency compared to cloud computing by processing data closer to the edge devices. While not as low-latency as edge computing, it offers faster response times compared to sending data to centralized cloud servers.

Cloud Computing: Cloud computing may introduce higher latency due to data transmission to and from remote servers, especially for applications requiring real-time processing.

Scalability and Resource Availability:

Edge Computing: Edge devices typically have limited computing resources, making scalability challenging for resource-intensive applications.

Fog Computing: Fog computing provides more scalability compared to edge computing by adding an additional layer of computing resources. Fog nodes can dynamically allocate resources based on demand, offering greater scalability for distributed applications.

Cloud Computing: Cloud computing offers virtually unlimited scalability, with cloud service providers managing large data centers equipped with massive computing and storage capacities. Cloud resources can be easily scaled up or down based on demand.

Data Security and Privacy:

Edge Computing: Since data processing occurs locally, edge computing may offer enhanced data security and privacy by reducing the need to transmit sensitive data over networks.

Fog Computing: Fog computing introduces additional security considerations compared to edge computing, as data may be processed and stored on intermediate fog nodes. However, proper security measures can be implemented to mitigate risks.

Cloud Computing: Cloud computing raises concerns regarding data security and privacy, especially when transmitting sensitive data over the internet to remote servers. Cloud service providers implement various security measures to protect data, but data breaches remain a potential risk.

In summary, the main differences between Edge, Fog, and Cloud computing lie in their location, latency, scalability, and security characteristics. Each paradigm offers distinct advantages and trade-offs, and the choice between them depends on the specific requirements and constraints of the application or use case.

We demonstrated how SCADA in this case, The ADisra SCADA package is used to perform the data exchange between the Edge and the cloud. We have power meters data and virbation data being collected by the edge IOT gateway. Data is then push to the Fog nodes, in this case the Adisra SCADA server. The data is presented and analyzed by Adisra SCADA before publishing this to the Cloud.

In this type of setup, predictive maintenance can be performed at the SCADA level with the assist of additional ML AI computing and the decision can be made upon the data analytic is completed. This demonstrate the power of fog computing in the real world.

SCADA (Supervisory Control and Data Acquisition) servers can be configured to act as fog nodes in smart manufacturing environments, providing additional processing, storage, and networking capabilities at the edge of the network. Here’s how SCADA servers can serve as fog nodes:

Local Data Processing: SCADA servers are typically equipped with computing resources capable of processing data locally. In smart manufacturing, SCADA servers can analyze data from sensors, PLCs (Programmable Logic Controllers), and other devices in real-time, without needing to transmit all data to centralized cloud servers.

Real-time Control and Monitoring: SCADA systems are designed for real-time control and monitoring of industrial processes. By acting as fog nodes, SCADA servers can execute control algorithms, perform data filtering, and generate immediate responses to events occurring on the factory floor.

Edge Analytics: SCADA servers can host analytics software capable of running advanced algorithms for predictive maintenance, anomaly detection, and optimization of manufacturing processes. These analytics can be performed locally on the SCADA server, leveraging historical data and machine learning models to generate insights at the edge.

Integration with Edge Devices: SCADA servers can communicate directly with edge devices such as PLCs, HMIs (Human-Machine Interfaces), and sensors. This integration allows SCADA servers to collect data from distributed devices, coordinate control actions, and exchange information with other fog nodes or cloud servers as needed.

Redundancy and Fault Tolerance: SCADA systems often incorporate redundancy and fault tolerance mechanisms to ensure continuous operation in industrial environments. SCADA servers acting as fog nodes can provide local redundancy, fault detection, and failover capabilities, minimizing disruptions to manufacturing processes.

Secure Communication: SCADA servers implement secure communication protocols to exchange data with edge devices and other components of the industrial control system. This ensures the integrity, confidentiality, and availability of data transmitted between fog nodes and other networked devices.

In summary, SCADA servers can effectively serve as fog nodes in smart manufacturing environments, extending the capabilities of edge computing by providing local processing, control, analytics, and communication functions. By distributing computing resources closer to the edge of the network, SCADA servers help optimize industrial processes, enhance real-time decision-making, and improve overall system performance and resilience.

To watch our live at the youtube channel follow the link below:-

https://youtube.com/live/eKB90TcVi2c

AIoTmission Sdn Bhd, established in 2022 as a subsidiary of Axiomtek (M) Sdn Bhd, is a leading provider of technological training and consultancy services specializing in Artificial Intelligence (AI) and Industrial Internet of Things (IIoT) solutions. Our mission is to drive the Fourth Industrial Revolution (IR4.0) and facilitate digital transformation across Southeast Asia, including Malaysia, Singapore, Indonesia, the Philippines, Thailand, Vietnam, and Myanmar.

At AIoTmission, we are dedicated to advancing research and development in AI and IIoT technologies, with a focus on industrial applications such as sensors, gateways, wireless communications, machine learning, AI deep learning, and Big Data cloud solutions. Through collaboration with our valued clients and partners, we deliver innovative solutions tailored to industry needs, enhancing technological capabilities and operational efficiency.

Industry 3.0 to 4.0 standardization with AIoT

March 23, 2024March 23, 2024 by editor Comment Closed

Industrial transformation with AIoT

During our live session on March 22nd, 2024, hosted by AioTmission, the focus was on the potential commoditization or monetization of datasets. As data increasingly drives decision-making processes to enhance efficiency and predictability with AI, standardizing the dataspace becomes imperative.

Transitioning from Industry 3.0 to 4.0 necessitates a standardized framework to facilitate a smooth transformation, ensuring manageability throughout the process.

There is this standard framework in the transformation, the ISA-95.

ISA-95, or the International Society of Automation Standard 95, is a widely recognized standard in the realm of manufacturing and automation. It provides guidelines for integrating enterprise and control systems in industrial environments. Originally developed in the 1990s, ISA-95 has been instrumental in defining the interface between enterprise resource planning (ERP) systems and manufacturing execution systems (MES) or even SCADA ( supervisory control and Data acquisition).

In the context of the transition from Industry 3.0 to Industry 4.0, ISA-95 plays a crucial role in facilitating interoperability and data exchange between different systems and layers within a digital factory including the top layer of Cloud integration.. Here’s how it helps in this transformation:

Interoperability: ISA-95 defines standardized interfaces and communication protocols, enabling seamless integration between various components of the manufacturing process. This interoperability is essential for transitioning from siloed systems of Industry 3.0 to interconnected, data-driven systems of Industry 4.0.

Data Integration: By providing a common framework for data exchange, ISA-95 allows for the integration of data from disparate sources across the manufacturing enterprise. This integrated data is foundational for implementing advanced analytics, machine learning, and other Industry 4.0 technologies.

Visibility and Control: ISA-95 facilitates real-time visibility into manufacturing operations by standardizing the exchange of production data between different levels of the manufacturing hierarchy, such as the shop floor, supervisory control, and enterprise levels. This visibility is essential for implementing agile and responsive manufacturing processes characteristic of Industry 4.0.

Scalability: As manufacturing systems evolve from traditional hierarchical architectures to more distributed and modular architectures in Industry 4.0, ISA-95 provides a scalable framework for managing the complexity of interconnected systems. This scalability ensures that digital factories can adapt and grow in line with evolving business needs and technological advancements.

In summary, ISA-95 serves as a foundational standard for enabling the seamless integration, interoperability, and data exchange necessary for the transition from Industry 3.0 to Industry 4.0 in digital factories. By adopting ISA-95 guidelines, manufacturers can streamline their operations, improve efficiency, and unlock the full potential of Industry 4.0 technologies.

How ISA-95 frame helps in digital transformation

There is this UNS ( Unified NameSpace) in the context of industrial transformation.

A unified namespace refers to a single, standardized system for naming and organizing data across an organization or within an industrial ecosystem. In the context of industrial transformation, particularly in the transition to Industry 4.0, a unified namespace plays a crucial role in enabling seamless data integration, interoperability, and collaboration among various systems, devices, and stakeholders. Here’s how:

Data Integration: Industrial environments typically consist of diverse systems, equipment, and sensors from different vendors, each with its own data formats and naming conventions. A unified namespace provides a standardized approach to representing and organizing data, making it easier to integrate information from disparate sources into a cohesive digital infrastructure.

Interoperability: With a unified namespace, different components within the industrial ecosystem can communicate and exchange data more effectively. Standardized naming conventions and data formats ensure that systems can understand and interpret information correctly, facilitating interoperability between devices, machines, and software applications.

Collaboration: In modern industrial settings, collaboration between different departments, teams, and even organizations is essential for driving innovation and efficiency. A unified namespace provides a common language for describing and accessing data, fostering collaboration among stakeholders across the entire value chain.

Scalability: As industrial systems grow and evolve, a unified namespace provides a scalable foundation for managing the increasing volume and complexity of data. By establishing standardized data structures and naming conventions upfront, organizations can adapt more easily to changing requirements and technologies without sacrificing interoperability or data consistency.

Data Analytics and Insights: A unified namespace simplifies data management and analysis, enabling organizations to derive valuable insights and intelligence from their industrial data. By establishing a consistent framework for organizing and accessing data, organizations can more effectively apply advanced analytics, machine learning, and other data-driven technologies to optimize operations, improve decision-making, and drive continuous improvement.

Overall, a unified namespace is a fundamental enabler of industrial transformation, providing the infrastructure needed to integrate, standardize, and leverage data effectively across the entire industrial ecosystem. By adopting a unified approach to data naming and organization, organizations can unlock the full potential of Industry 4.0 technologies and realize the benefits of increased efficiency, agility, and innovation.

MQTT (Message Queuing Telemetry Transport) is a lightweight messaging protocol designed for the efficient exchange of data between devices in constrained environments, such as those with low bandwidth, high latency, or limited processing power. Here’s how MQTT works:

Client-Server Architecture: MQTT follows a client-server architecture where clients (devices or applications) connect to a central server, known as the MQTT broker. The broker is responsible for routing messages between clients.

Publish-Subscribe Model: MQTT uses a publish-subscribe messaging pattern. Clients can publish messages to specific topics, and other clients can subscribe to these topics to receive messages. This decoupling of senders and receivers allows for flexible and efficient communication between devices.

Topics: Topics are hierarchical strings used to categorize messages. They are represented as a series of segments separated by forward slashes (/), similar to a file path. Clients can publish messages to specific topics or subscribe to topics to receive messages. For example, a topic could be sensors/temperature to represent temperature sensor data.

Quality of Service (QoS): MQTT supports different levels of message delivery assurance through its Quality of Service levels:

QoS 0 (At most once): Messages are delivered at most once, with no guarantee of delivery.

QoS 1 (At least once): Messages are guaranteed to be delivered at least once, but duplicates may occur.

QoS 2 (Exactly once): Messages are guaranteed to be delivered exactly once, but this level of assurance comes with increased overhead.

Connection Establishment: Clients establish a connection with the MQTT broker using TCP/IP or other transport protocols. The client can specify its client identifier and may also provide authentication credentials if required by the broker.

Keep-Alive Mechanism: Once connected, clients maintain their connection to the broker using a keep-alive mechanism. Clients periodically send PINGREQ messages to the broker, and the broker responds with PINGRESP messages to indicate that the connection is still active.

Message Transmission: When a client publishes a message to a topic, it sends the message to the broker, which then forwards the message to all clients subscribed to that topic. The broker may also retain messages for clients that are not currently connected, depending on the configuration.

Message Retention: MQTT brokers can be configured to retain the last message published to a topic. This allows new subscribers to immediately receive the last known value when they subscribe to a topic.

Security: MQTT supports various security features, including authentication, access control lists (ACLs), and encryption (TLS/SSL), to ensure the confidentiality, integrity, and authenticity of message transmission.

Overall, MQTT’s lightweight design, efficient publish-subscribe model, and support for various quality of service levels make it well-suited for a wide range of IoT and M2M (machine-to-machine) communication scenarios.

To check out how IIoT sensor integration to the Cloud, click here.

In the session, we demonstrate the proprietory serial data interface to SCADA – The Adisra SCADA – an easier tool for proprietory data integration

Demonstration continued with the integration from SCADA to AIoT edge Connect Cloud platform.

Watch the demonstration session only at the youtube.

Deep Learning AI for Industrial Transformation

March 16, 2024March 17, 2024 by editor Comment Closed

Industrial Transformation with Deep Learning AI

How extensive does AI able to reduce the head count in the Manufacturing process?

The question was asked in the beginning of the “Sembang” AIoT session this round. Are you interested to know more? lets “sembang”!

What is Deep learning AI?

In simple terms, deep learning AI is a type of computer technology that learns to do tasks by itself, kind of like how you learn new things over time. Imagine you’re teaching a robot how to recognize cats in pictures. You’d show it lots of pictures of cats and tell it, “These are cats.” Then, the robot uses what it’s seen to figure out what a cat looks like. Deep learning AI works similarly but with a massive amount of data and complex math. It’s called “deep” because it learns from many layers of information, like peeling layers of an onion to get to the core. This technology has become really good at recognizing patterns in data, from identifying faces in photos to understanding spoken language. It’s used in things like voice assistants, self-driving cars, and even in healthcare for diagnosing diseases from medical images. Essentially, deep learning AI helps computers learn and make decisions on their own, making our lives easier and more efficient.

Deep learning AI has its roots in the field of artificial neural networks, which have been around for several decades. However, significant advancements in deep learning, particularly in the form of deep neural networks, started gaining attention around the early 2010s.

One of the pivotal moments in the history of deep learning AI was the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012. This competition showcased the effectiveness of deep convolutional neural networks (CNNs) in image classification tasks, significantly outperforming traditional machine learning methods. The winning entry, AlexNet, demonstrated the potential of deep learning to revolutionize computer vision tasks.

Since then, deep learning research has accelerated, leading to numerous breakthroughs in various domains, including natural language processing, speech recognition, robotics, healthcare, and more. Researchers and engineers continue to refine deep learning algorithms and architectures, contributing to its ongoing evolution and widespread adoption.

So, while the foundational concepts of deep learning have been around for some time, its explosive growth and impact on the field of artificial intelligence are more recent, particularly over the past decade.

Deep learning AI has revolutionized industrial transformation in several ways:

Predictive Maintenance: Deep learning algorithms can analyze sensor data from industrial equipment to predict potential failures before they occur. By detecting anomalies and patterns in the data, maintenance can be scheduled proactively, minimizing downtime and reducing maintenance costs.

Quality Control: Deep learning AI enables automated visual inspection systems to detect defects in manufactured products with high accuracy. By analyzing images or sensor data in real-time, these systems can identify imperfections and deviations from quality standards, ensuring only high-quality products reach the market.

Optimized Production Processes: Deep learning algorithms can optimize manufacturing processes by analyzing data from various sources, such as production line sensors, supply chain information, and historical data. This analysis helps identify inefficiencies, streamline operations, and improve overall productivity.

Supply Chain Management: Deep learning AI can enhance supply chain management by predicting demand, optimizing inventory levels, and identifying potential disruptions. By analyzing data from diverse sources, including historical sales data, market trends, and external factors, deep learning models can provide valuable insights for decision-making in logistics and supply chain operations.

Customization and Personalization: Deep learning algorithms can analyze customer preferences and behavior to enable personalized product recommendations and customization options. By leveraging data from customer interactions, purchase history, and demographic information, manufacturers can tailor their offerings to individual preferences, enhancing customer satisfaction and loyalty.

Energy Efficiency: Deep learning AI can optimize energy consumption in manufacturing facilities by analyzing data from energy meters, sensors, and other sources. By identifying opportunities for energy savings and implementing adaptive control strategies, manufacturers can reduce their environmental footprint and operating costs.

Overall, deep learning AI has transformed industrial processes by enabling predictive capabilities, automation, optimization, and personalized experiences. By harnessing the power of data and advanced algorithms, manufacturers can drive innovation, efficiency, and competitiveness in the global marketplace.

In the manufacturing environment, there are many vision inspection problem statement where it can be resolved with the latest Vision AI technology.

We demonstration the AIS objection detection onto a 3 Pin power plug label with normal USB camera running about 80FPS to detect the lable on a running conveyor belt.

In this demonstration, we show cased the SCADA and AI integration where all AI data is feed to the SCADA software for data presentation and manipulation.

To find out more about the AIS ( The AI suite development tool) check at Axiomtek AIS.

To find out more about our services and solutions Click here.

We have done demonstration on the simulated OEE tracking with AIoT, you may explore more from the past sharing as well.

To watch our live session subscribe to our youtube channel via this:-

https://youtube.com/live/09Pa83-OnVo