IoT Industry 4.0 is a growing trend in manufacturing. We’ve gone over the basics of Industry 4.0 training on the blog, as well as the challenges and how to incorporate technologies and concepts into training programs. The integration of Internet of Things (IoT) devices and sensors is a major component of Industry 4.0 and can be applied to many manufacturing and industrial processes to improve efficiency and productivity.
Factory Automation
IoT is a key component of smart factories in the manufacturing industry. Machines on the factory floor are equipped with sensors that feature an IP address that allows the machines to connect with other web-enabled devices. This mechanization and connectivity make it possible for large amounts of valuable data to be collected, analyzed, and exchanged.
This can lead to benefits like:
- Increased efficiency– Automation can boost productivity and streamline the functionality of factories. IoT sensors can be used to track the performance of manufacturing assets (more on that later) and ensure they are meeting or exceeding standards.
- Predictive maintenance – IoT sensors collect real-time information about assets and can predict the time for maintenance, reducing the amount of time that machines are down.
- Real-time data monitoring– As data is monitored in real-time, relevant changes can be made as needed to boost productivity, streamlining the decision-making process.
- Reduced cost– These predictive maintenance and real-time data monitoring features of IoT reduce the amount of human intervention needed, which leads to a reduction in costly human errors.
Amatrol Smart Factory Enterprise is an example of a training system that teaches Industry 4.0 skills with a multi-station automated manufacturing system that integrates mechatronics, robotics, conveyors, autonomous robots, and a variety of other Smart Factory technologies.
Smart sensors with I/O links in the system provide flexible manufacturing, predictive maintenance, and data analytics capabilities. Featured smart sensors include pneumatics/vacuum, ultrasonic, photoeye, stacklight, electrical current, and analog position and pressure sensors.
Process Optimization
IoT provides a distinct advantage for developing and implementing improvement strategies to optimize production. By monitoring machinery in real time and identifying potential production bottlenecks, waste, or quality issues, it is easier to maximize production and minimize costs.
This can be implemented through the following technologies:
- Real-time data connectivity and capture– IoT sensors can securely connect to assets and machinery to capture data in real time, assuring product quality and production efficiency.
- Process-based machine learning– Process-based artificial intelligence can be used to get a detailed view of the full manufacturing process and to discover issues that need attention. By analyzing real-time data, not only can process inefficiencies be identified, predicted, and even avoided.
- Digital Twin visualization– A digital twin is a virtual representation that matches the attributes and operational metrics of a “physical” product or system. A digital twin allows for virtual testing and simulations to be run ahead of time to diagnose potential pitfalls on the production line, ensuring viability without extensive costs from physical testing.
The energy industry is one that is striving to optimize and utilize more energy for industrial purposes, as over half of energy that goes to actual energy services is wasted. However, successful development of sensors, monitoring and data analytics is projected to improve yield to 95 percent, reducing energy consumption overall by 5 percent.
Smart Manufacturing
Smart manufacturing, which includes the use of technologies like IoT, was valued at 262.45 billion in 2022 and is projected to reach 759.23 billion by 2030, and is expected to grow 14.2 percent from 2023 to 2030. One of the main drivers of this growth is the rising adoption of Industry 4.0, which enables greater efficiency and productivity using smart systems and machines.
Three key digital technologies enable the smart factory:
- Connectivity– for example, leveraging the Industrial IoT to collect data from existing equipment and new sensors.
- Intelligent automation– for example, advanced robotics, machine vision, distributed control, and drones.
- Cloud-scale data management and analytics– for example, implementing predictive analytics/AI.
IoT supports smart manufacturing by allowing factory managers to automatically collect and analyze data to make better-informed decisions and optimize production. The flow of data allows for changes to be made in real time, and helps improve manufacturing outcomes by reducing waste, speeding production, and improving the quality of goods produced.
The automotive industry has been an early adopter of smart manufacturing technologies such as advanced robotics, artificial intelligence, and IoT to improve their manufacturing processes. The industry had expansive plans for expansion in the last five years with investment into smart factories, as a percentage of revenue, was projected to increase by more than 60 percent.
Mercedes Benz has a smart factory in Sindelfingen, Germany that produces luxury, hybrid, pure electric and self-driving cars using elements like IoT in assembly facilities and materials handling technology. An Audi smart factory in Mexico utilizes technologies like virtual assembly technology, remote maintenance, collaborative robots to create custom premium SUV’s. (See page 16 on this report for more on the Mercedes factory and page 15 for more on the Audi factory.)
Beyond the manufacturing floor, sensors and sensing technologies in cars can monitor the vehicle’s health and send that information back to the manufacturer or individual in charge of its maintenance, or this same system can transmit weather and traffic information and other data back to the driver to help them make informed driving decisions.
Performance Monitoring
Most physical things in Industry 4.0 – devices, robots, machinery, equipment, products – use sensors and RFID tags to provide real-time data about their condition, performance, or location. At one time, GE’s smart factory in Schenectady, New York was armed with 10,000 sensors, all of which are linked to the company’s 400 factories across the globe. Each sensor captured 10,000 variables from the manufacturing process all within a matter of milliseconds. The types of data collected from IoT sensors in the manufacturing process can include temperature, pressure, vibration, energy consumption, and more. This data is then transmitted to cloud-based platforms where it is stored, processed, and monitored.
IoT performance monitoring includes:
- Collecting and analyzing– diverse IoT data is analyzed across devices, customers, and applications.
- Bridging performance gaps– performance is optimized across applications, networks, and protocols.
- Gaining actionable insights– customer experience is improved, problems remediated, and IoT opportunities are maximized.
Using techniques such as machine learning and statistical modeling, the analytics platform uncovers patterns, trends, anomalies, and correlations within the data. With real-time and historical analytics, businesses can gain a comprehensive understanding of their operations.
Ultimately IoT technology can help companies run smoother supply chains, rapidly design, and modify products, prevent equipment downtime, stay on top of consumer preferences, track products and inventory, and much more.
