SOLUTIONS MACHINE LEARNING By Ingo Mierswa
Five ways machine learning will transform manufacturing in 2021 Emerging developments that will become reality with machine learning part of everyday operations
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echnology advances, such as complex robotic systems and artificial intelligence (AI), transformed manufacturing over the past decade and driven what’s commonly called the Industry 4.0 Revolution. COVID19 has further accelerated this transformation for many manufacturers as more plant operations need to be run effectively, and in many cases, be monitored and managed remotely. Machine learning (ML) — a branch of artificial intelligence centered around creating computer programs that learn from experience and improve their decision-making ability over time — is increasingly important in many industries, and manufacturing is no exception. Cheaper sensors and data storage, as well as the maturation of big data technology, has allowed manufacturers to capture vast amounts of data, and ML allows enterprises to derive actionable intelligence from said data, enabling smarter equipment, improved quality and increased productivity. As enterprises continue to use machine learning (ML) as part of their everyday operations, here are five ways manufacturing is expected to develop in the year ahead.
1. Widespread predictive maintenance
Typically, factories have relied on regular maintenance schedules dictated by usage or time to determine when a machine needs to be serviced — or have even waited until equipment breaks down to conduct maintenance. Leveraging AI, manufacturers are developing predictive maintenance models trained on historical data about what led up to past equipment problems to predict when machines need maintenance. They can then fire an alert and the equipment can be repaired. Because equipment is only shut down for repair when it’s actually needed, instead of according to a schedule, these models can save both time and money. Furthermore, as fast innovation cycles are shortening product lifecycles across most product
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categories, and dramatic shifts in customer expectations are shortening delivery lead times, manufacturers must be faster and more flexible with machine repairs and retooling. While predictive maintenance isn’t new to 2021, expect to see a dramatic increase in its widespread adoption this year, with enterprises using data from connected devices such as sensors embedded into equipment to remove the guesswork from maintenance decisions.
2. Improving energy efficiency
Most factories today operate on a 24/7 schedule to maintain optimal efficiency, requiring large amounts of energy to keep things moving. By taking energy prices, equipment maintenance, labor costs and inventory into account, ML algorithms can schedule the perfect time to perform energy-intensive activities. As a result, enterprises can maximize cost savings by running the right processes at the right time. This company has already seen this practice in play with a number of manufacturers, including a major global petrochemical manufacturer who came to RapidMiner looking to reduce its power consumption. The company was consuming $20 million a year in energy but wanted to cut costs and be more environmentally friendly. By deploying AI models that were easily adjustable in real time and worked on sensor data, RapidMiner was able to reduce its power consumption by 5%.
3. Guaranteeing product quality
Regardless of how optimized a manufacturing process is, every factory experiences product defects. Although there are various options for trying to correct them, flaws are still commonplace and treated as a cost of doing business. With ML, manufacturers can significantly reduce the possibility of error while optimizing quality control efforts. Instead of relying on humans to visually inspect each product on an assembly line, image recognition and www.plantengineering.com
