Machine learning models for online detection of wear and friction behaviour of biomedical graded stainless steel 316L under lubricating conditions

Abstract

Particularly in sectors where mechanisation is increasing, there has been persistent effort to maximise the use of existing assets. Since maintenance management is accountable for the accessibility of assets, it stands to acquire prominence in this setting. One of the most common methods for keeping equipment in good working order is predictive maintenance with machine learning methods. Failures can be spotted before they cause any downtime or extra expenses, and with this aim, the present work deals with the online detection of wear and friction characteristics of stainless steel 316L under lubricating conditions with machine learning models. Wear rate and friction forces were taken into account as reaction parameters, and biomedical-graded stainless steel 316L was chosen as the work material. With more testing, the J48 method’s accuracy improves to 100% in low wear conditions and 99.27% in heavy wear situations. In addition, the graphic showed the accuracy values for several models. The J48 model is the most precise amongst all others, with a value of 100% (minimum wear) and an average of 98.92% (higher wear). Amongst all the models tested under varying machining conditions, the J48’s 98.92% (low wear) and 98.92% (high wear) recall scores stand out as very impressive (higher wear). In terms of F1-score, J48 performs better than any competing model at 99.45% (low wear) and 98.92% (higher wear). As a result, the J48 improves the model’s overall performance.