Safety & Maintenance
Robin Cave National Applications Engineer Mazak Corporation ABOUT THE AUTHOR Robin Cave is an application engineer at MAZAK Corporation. He has worked with the company for over 38 years. To learn more about MAZAK and their line of machine tools and controls, visit www.MazakUSA.com.
16 | IMD JANUARY 2022
Machine Tools Learn to Improve Thanks to AI
A
rtificial intelligence (AI) and machine learning can analyze performance data and deliver optimization results that exceed what human beings can accomplish. For data giant Google, AI reduced server-farm cooling costs by an average of 30% after only a few months of use, with even greater optimization potential as the system continued to acquire performance data. Similarly, AI can reduce manufacturing setup time, enhance efficiency and workpiece quality, and boost productivity for better profits, helping shops progress past old-fashioned trial and error. Until the advent of AI and machine learning in manufacturing technology, the old saying "If at first you don't succeed, try, try again" described how shops attempted to make good production results repeatable. Because the machines could not provide first-hand insights into their performance, engineers identified good parts and doubled back to the production floor to ask operators how they achieved that level of quality. But even with operator notes, many shops failed to replicate success from day to day, and the large, growing volume of data generated through
machine analytics provided too much information for timely processing. Now, with smart CNCs and AI systems, machines can provide essential data about how they operate when they make a good part, and process all that information automatically to improve future results through machine learning. In an AI-equipped CNC, the controller
