Artificial intelligence to detect metalworking machine anomalies27 July 2020

Finnish-Russian industrial digitalisation specialist Zyfra has developed a predictive analytics solution powered by machine learning to enhance the capabilities of metalworking machines by detecting anomalies in the technological process and identifying the possible cause.

Zyfra PdA solution is said to analyse the real-time data from CNC machines, alert users whenever an anomaly emerges, and indicate the possible cause while giving recommendations for further actions. The system classifies a range of anomalies, such as tool quality, operator error in case of incorrect operating modes selection and machine failure.

“Manufacturers of large-dimensioned products made of expensive materials face a detect defection problem,” says Alexander Smolensky, business development director at Zyfra. “Defects are caused by many factors, such as the quality of the cutting tools, technological faults [and] equipment wear, for example. There is a need for an intelligent system which will take into account all the necessary information from equipment monitoring systems, evaluate the impact of this information on defects and help operators and technologists to make decisions.”

Metalworking machine failures might lead to defects in manufactured parts or cause equipment to breakdown, according to Zyfra, which adds that implementation of the system results in cost reduction of manufactured parts, improvement in product quality and a decline in spoiled products amount, as well as reduction of maintenance and repair costs and equipment downtime.

Implementation of Zyfra PdA is envisioned for CNC machines embedded with MDCplus real-time machine monitoring and manufacturing data collection system. The operating principle of the equipment monitoring system is that each machine automatically transfers data about its own performance into a single digital system. Zyfra has connected 10,000 CNC Machines to its MDCplus real-time machine monitoring and manufacturing data collection system.

Adam Offord

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