This work presents a new methodology for machine tools anomaly detection via operational data processing. The previous methodology has been field tested on a milling-boring machine in a real production environment. This paper also describes the data acquisition process, as well as the technical architecture needed for data processing. Subsequently, a technique for operational machine data segmentation based on dynamic time warping and hierarchical clustering is introduced. The formerly mentioned data segmentation and analysis technique allows for machine tools anomaly detection thanks to comparison between near real-time machine operational information, coming from strategically positioned sensors and outcomes collected from previous production cycles. Anomaly detection techniques shown in this article could achieve significant production improvements: “zero-defect manufacturing”, boosting factory efficiency, production plans scrap minimization, improvement of product quality, and the enhancement of overall equipment productivity.

POSITIVE ASPECTS: The study's subject, addressing a country's capacity to meet its food demands, is undoubtedly significant. Predicting arable land productivity is crucial for informed decision-making, particularly for governments considering food i...

Dear authors, congratulations on your article. Although the use of UAVs in agriculture is not something new, the article could be more interesting if the design were explained in more detail, providing additional technical details and leading to a m...

Dear authors, Please accept the following recommendations as a way to improve the quality of your articles. 1. The abstract should be concise but comprehensive. Shorten the abstract and ensure it clearly highlights the methodology, key findings, and...

The subject covered in the article is interesting as it presents an artificial intelligence implementation in agriculture. However, we would like to suggest some improvements. 1. Correction of grammatical mistakes. 2. Improve the bibliography with...