Industrial revolutions have changed the way we work, think and do most everything. Today, with the help of electronics, smart computing and the Industrial Internet of Things (IIoT), Industry 4.0 is enabling new efficiencies and higher levels of performance on the factory floor. TTI supplies the components that drive everything for computer-controlled manufacturing and material movement. 3D printing rapid prototyping are leading to CNC machine driven robotic assembly. The integration of sensors and process controllers into the most elemental industrial applications is pushing production and performance to new levels. Tighter tolerances, better monitoring and quicker production are creating a revolution in industry that hasn’t been seen in 100 years. Talk to a TTI industrial technology Specialist at 1.800.CALL.TTI and see how easy it can be to propel your industrial design ideas into the next century.
Robots aren’t new to industrial operations, but remote control capabilities, Internet connection and other recent advancements in technology are bringing fully-automated robotics to factory floors. Robot controlled technology powered by data and signal interconnects enables the control of a robot’s position, trajectory, operation sequence and action timing. Installing sensors on robots allows them to gather and act on data in real time, resulting in more reliable, intuitive and safe automation. For example, if a robot detects an unexpected human or object within a certain radius, it can cease operations until the coast is clear.
Advances in robot operation technology also can move factories toward full automation. Traditionally, robots rely on motors to generate movement, but this mechanical approach limits the range of motion and leads to wear and tear, resulting in frequent breakage. Using contactless couplers instead of motors and cables relies on magnetic fields to achieve connectivity, which increases throughput, enables flexibility and allows for operations in harsh environments.
While data collected from the factory floor has long impacted operations, real-time automated feedback and response can revolutionize operations while dramatically reducing downtime. Traditionally, a sensor that detects an overheating machine will alert a worker, who turns off the machine and addresses the problem. Process control enables the active changing of the process based on data collection by either involving a worker or fixing the problem automatically. Machine learning will take this approach from a simple alert to a predictive and self-healing process, while collecting data that, over time, can be used to predict problems in processes before they even occur.
A key step to moving toward fully automated factories and commercial building developments is through efficient and automated power management, which reduces cost and environmental impact. This approach can be enhanced even further by using data analysis to automate a facility’s HVACR and power settings. Sensors placed throughout the building can help these smart systems determine when to adjust the temperature in certain zones, eradicating the need to manually program and adjust thermostats. Such connected sensors can also monitor maintenance power and HVACR needs in real-time, increasing efficiency and reducing downtime.
Similarly, rotary position sensors can monitor valve position applications to ensure equipment is operating at full capacity, especially in harsh environments where it is difficult to manually supervise valves. Valves are critical for regulating basic operations, and the ability for them to self-diagnose and even self-tune helps keep a factory running. Retrofitting power supply equipment with AC/DC drives, surge and protection devices, I/O ports and programmable logic controllers is essential for valve automation.
The food and beverage industry has a lot to gain from smart manufacturing processes: fewer humans involved in the supply chain means less chance of contamination, and fine-tuned operations allow for smaller or customized batches of a product, leading to less waste as well as more business opportunities and the ability to adapt to market needs in real time. Data analysis and machine learning can predict new trends in the industry while decreasing research and development times.
Additionally, the ability to source and trace ingredients is critical in food processing, especially in the wake of a recall. Factories can respond to such issues more quickly by identifying the affected batches and pulling them before they ship.
The automation of material handling applications has a huge impact on both the factory floor and business development—it will lead to a reduction in labor required to transport goods while making logistics management easier and more cost effective. As in other applications, the communication between machines and warehouse management systems enables efficient operations while gathering valuable data that can drive distribution operations. Automated material handling can be updated in real time if there is a change in a task sheet while tracking goods throughout the supply chain, reducing loss and providing logistical insights.
As more factories adopt IIoT functionality, which allows them to monitor, track and control operations remotely via Internet connection, they also are gathering massive amounts of critical data that could revolutionize the way they do business―if utilized correctly. IIoT is a key component of the Industry 4.0 approach, which analyzes that collected data and uses it to power artificial intelligence, machine learning and factory automation. This combination of operational data and smart computing truly can bring a facility into the future, where processes are optimized by data analysis, maintenance is carried out automatically before problems occur, and operations are fully automated.
To move toward a fully connected and automated factory, machines need to be equipped with connectors, wiring systems, sensors that are robust, scalable, and have the ability to send and receive data without fail. These smart components are best paired with reliable network interfaces, simulation software and programmable logic controllers that can manage large amounts of data, enabling machine learning capabilities.