Abstract
Industry 4.0 has been advertised for a decade as the next disruptive evolution for production. It relies on automation growth and particularly on data exchange using numerous sensors in order to develop faster production with tight monitoring. The huge amount of data generated by clouds of sensors during production is often used to feed machine learning systems in order to detect faults, monitor and find possible ways for improvement. However, the artificial intelligence within machine learning requires finding and selecting key features, such as average and root mean square. While current machine learning has already proven its use in diverse applications, its efficiency could be further improved by generating better characteristics such as fractal parameters. In this paper, fractal analysis concept is presented and its current and future applications in machining are discussed. This sensitive and robust technique is already extracting high performance key features that could fill in monitoring and prediction systems. On top of improving features selection and, thus, improving the overall performance of monitoring and predictive systems in machining, this could lead to a more rapid artificial intelligence implementation into manufacturing.
Highlights
IntroductionThe industry has been evolving into a manufacturing 4.0 environment
For nearly a decade, the industry has been evolving into a manufacturing 4.0 environment
The progress in the machine shop could be leading to improvements in various areas such as process analysis and optimization, prescriptive maintenance, process monitoring and machining quality prediction
Summary
The industry has been evolving into a manufacturing 4.0 environment. Industrial internet-of-things allows bringing global connectivity to devices and machines by embedding sensors and actuators Control process systems such as SCADA (Supervisory Control And Data Acquisition) command actuators based on decisions made from multiple sensors signal combined information [1]. The implementation of fractal analysis either in a standalone model or embedded within a control system could drastically improve the diagnosis capabilities of a manufacturing process or machine. This manuscript presents an example of fractal analysis in order to fully comprehend the different pros and cons of such technique.
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