The book “Procedia Manufacturing” (Vol. 10), published by Elsevier, brings together selected papers from the recently-held 45th SME North American Manufacturing Research Conference, NAMRC 45, Los Angeles. The volume features the paper presented by Plethora IIoT Team Leader Javier Díaz, entitled “Machine learning-based CPS for clustering high throughput machining cycle conditions”.
Javier Díaz’s paper is found in Track 4: “Cyber-Physical Systems in Manufacturing”. The article focuses on testing various algorithms for knowledge discovery applications using real machine data, making it possible to gain new knowledge oriented to analysing and operationally improving the machines.
As the article’s abstract reads:
“Cyber-physical systems (CPS) have opened up a wide range of opportunities in terms of performance analysis that can be applied directly to the machine tool industry and are useful for maintenance systems and machine designers. High-speed communication capabilities enable the data to be gathered, pre-processed and processed for the purpose of machine diagnosis. This paper describes a complete real-world CPS implementation cycle, ranging from machine data acquisition to processing and interpretation. In fact, the aim of this paper is to propose a CPS for machine component knowledge discovery based on clustering algorithms using real data from a machining process. Therefore, it compares three clustering algorithms – k-means, hierarchical agglomerative and Gaussian mixture models – in terms of their contribution to spindle performance knowledge during high throughput machining operation.”
Follow this link to access and download the full paper: