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AI and Machine Learning

Four major trends for AI and robotics

Artificial intelligence (AI) and robotics have the ability to move forward in manufacturing thanks to advances in machine learning, better decision-making and increased efficiency.

By Control Engineering Europe June 19, 2020
Courtesy: Chris Vavra, CFE Media and Technology

Artificial intelligence (AI) has been the subject of considerable hype for several years, but is the manufacturing industry ready to move to the next stage and focus on how it can be sustainably implemented on the factory floor in robotics and industrial automation applications? Omron highlighted four key artificial intelligence (AI) trends it is seeing in robotics and industrial automation, which could have a major effect on the future of manufacturing:

1. Valuable machine data generated at the edge

The latest industrial automation and robotic developments in factories depend on the generation and collection of deep knowledge and data insights at machine level – i.e. at the edge. The machine can learn from its human operators and subsequently improve the output. Technology controlled by AI can empower machine learning by predicting both product and equipment failure, using data generated by Industrial Internet of Things (IIoT) devices. The analysis and use of combined data enables users to rapidly predict potential machine errors, preventing disruptions and the deterioration of product quality.

2. Increased efficiency through self-learning algorithms

With the move from mass customization to a high-mix, low-volume approach, efficiency must be improved by reducing human errors and machine downtime. AI with learning algorithms can help machine operators to achieve the best result in every change-over. Innovative control technology can also help employees to work alongside robots and machines to achieve manufacturing excellence. This is accomplished by using a broad range of factory automation equipment that enables IIoT-capable production or implements optimal AI algorithms in the equipment. An AI-equipped controller can be designed to detect signs of any equipment irregularity. AI algorithms allow it to learn the repeated movements of equipment from precise data from sensors. This in turn provides feedback for status monitoring and the real-time control of machines.

3. Efficient decision-making with visualized data

Industry 4.0 and IIoT enable the accurate collection of historical data. However, many AI projects struggle with the visualization of new data. Predictive maintenance and control solutions, can align the control functions of manufacturing lines and equipment with AI processing in real time. They can support companies by generating new, rather than historic, data that is time-stamped and easy to visualize.
The process of collecting raw data from machines is completely automated, using an AI-enabled controller which operates on the ‘edge’ within the machine. This can lead to higher data accuracy and consistency.

4. Sustainable technology

AI-assisted collaborative robots will play an increasingly important role beyond 2020. The aim is to create healthy and safe living and working conditions that cause less harm to the environment. Assembly and disassembly robots will have an important role to play here. The new generation of robots can learn from machine operators (sensing), and collaborate with collaborative robots (control) on a circular production line. They collect smart and intuitive data from their actions, assess the data using algorithms, advise the operator about the next steps, and implement efficient processes for each changeover (think).
This article originally appeared on the Control Engineering Europe site. Edited by CFE Media.

Control Engineering Europe