Four Ways AI Will Change Manufacturing in 2022
While the rest of the world embraced 21st-century technology, muchof the manufacturing industry remained mired in 20th-century industrial revolution practices. But after nearly two years of struggling against pandemic shutdowns, supply chain issues, and worker shortages, this sector is on the cusp of an AI revolution.
AI and machine learning offer manufacturing a chance for new and stable growth. When paired with traditional automation, these technologies can improve efficiency and decrease costs. Manufacturers have many reasons to embrace smart technologies. Specifically, here are four ways AI and ML will alter the manufacturing sector in the coming year.
Traditional customization requires unprecedented time and labor, beginning with the design process. Such optimization remains unattainable for smaller manufacturers due to the resulting costs.
But AI and ML tools bring optimization back into reach. AI tools like generative design feed the software with parameters for materials, costs, and manufacturing methods. The software then explores all possible solutions and design iterations within those parameters.
In this way, manufacturers can try out virtual alternatives before producing a single product. A thousand iterations can take less time and cost than one traditionally created mocked-up product.
Additionally, digital twins--virtual AI product/service models--allow these generative designs to leap into a virtual reality that mirrors the real world. Here, products or services are tested and their data reviewed for design flaws. This offers yet another tool to ensure a design works before it moves to real-time manufacturing assembly.
Traditional manufacturing systems produce a consistent product at a reliable rate. But systems cannot identify changing parameters to evaluate or adjust output except in the most basic way. This can mean disaster when something goes wrong.
Added sensors connected to AI and ML algorithms work differently. Smart systems can identify changes in temperature, material, or process and automatically adjust or stop manufacturing before ruining materials or producing a substandard product. This quick response can decrease costs while increasing reliability. In turn, quality improves while manufacturing time diminishes.
2021 has been full of supply chain disruptions. No one expects this to improve any time soon.
The cumbersome overseas shipping network stranded thousands of large containers far away from goods waiting for shipment. Meanwhile, local railroads, ports, and trucking lines are dealing with labor shortages, causing bottlenecks and delays when goods finally arrive. To make matters worse, costs are skyrocketing everywhere.
It’s true that AI algorithms cannot correct all these problems. But AI can search and track shipping data patterns and raw materials expenses. This gives manufacturers a clearer picture of how and when raw materials and finished goods should move to and from their facility. It can be invaluable for tracking trends and forecasting the best time for ordering and shipping to ensure best outcomes and lowest prices. This risk management optimizes the flow of goods, making best use of manufacturing facilities, limiting delays and curtailing expensive waste.
Cybersecurity threats persist as a top concern for the manufacturing industry. Cybercrime is rising, and manufacturing targets are valuable. In fact, last year the National Association of Manufacturers (NAM) launched NAM Cyber Cover, a special cybersecurity and risk mitigation program to help manufacturers understand security threats and blunt their effects on the industry.
AI algorithms, with their ability to process and sort through massive amounts of data, can significantly reduce unknown threats from hackers. It can prevent expensive infiltrations before they happen by detecting vulnerabilities and possible threats before something goes wrong. Security fixes can then be made before issues arise.
Additionally, AI can help secure the factory floor through facial recognition software and AI cameras, biometric tags, and integrated sensors. These integrations ensure only authorized workers access data, machines, or specific areas. They also monitor for other irregularities like smoke, fire, or injury.
Other Ways AI Will Figure Into Manufacturing
Several other aspects of AI integration must be considered. As robotics integrate more thoroughly into manufacturing automation, AI will improve both machine vision and robotic use patterns. AI also helps move the manufacturing robot out of the cage and next to the living worker as a cobotic companion. Such a move is impossible without AI and ML.
Additionally, embedded sensors within machines will continue to improve machine performance. They will also enable predictive maintenance capabilities that drive down labor costs and optimize manufacturing run times by resolving machine issues early.
Finally, AI will drive sustainability goals through optimized use of energy resources and raw materials. As consumers become more and more focused on supporting sustainable products, this will become an important part of AI’s use in manufacturing.
At a time when the manufacturing industry is changing rapidly, it’s important to remain up-to-date on the latest technological developments. Even those who are doing well today risk being left behind as competitors change around them. Without adaptation, decline is almost inevitable. The sector is early on in this change, but change will come, ready or not.
Technology writer Marla Keene works for AX Control, Inc, a North Carolina industrial automation parts supplier. She writes about drones, green tech, artificial intelligence, and other technologies changing our world. Her articles have been featured in Power Magazine, Food Industry Executive, and other industry sites. Before working for AX Control, Marla spent twelve years running her own small business.
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