Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Tool Life Prediction of Ti [C,N] Mixed Alumina Ceramic Cutting Tool Using Gradient Descent Algorithm on Machining Martensitic Stainless Steel

Version 1 : Received: 5 November 2020 / Approved: 6 November 2020 / Online: 6 November 2020 (15:49:06 CET)

How to cite: A, S.K.; S, J.D. Tool Life Prediction of Ti [C,N] Mixed Alumina Ceramic Cutting Tool Using Gradient Descent Algorithm on Machining Martensitic Stainless Steel. Preprints 2020, 2020110248 (doi: 10.20944/preprints202011.0248.v1). A, S.K.; S, J.D. Tool Life Prediction of Ti [C,N] Mixed Alumina Ceramic Cutting Tool Using Gradient Descent Algorithm on Machining Martensitic Stainless Steel. Preprints 2020, 2020110248 (doi: 10.20944/preprints202011.0248.v1).

Abstract

In automated manufacturing systems, most of the manufacturing processes including machining are automated. Automatic tool change is one of the important parameters for reducing manufacturing lead time. Ceramic cutting tools are used to machine hard materials. Ti[C,N] mixed alumina ceramic cutting tools are widely used to machine hardened steel and Stainless Steel due to its superior mechanical properties. Martensitic stainless steel has wide applications in screws, bolts, nuts and other engineering applications. Machining studies on Martensitic Stainless Steel was conducted using Ti[C,N] mixed alumina ceramic cutting tool. Tool life was evaluated using flank wear criterion. The tool life obtained from experimental machining process was taken as training dataset and test dataset for machine learning. Using the dataset obtained from experimental machining tool life model has been developed using Gradient Descent algorithm. The model was validated using co-efficient of determination. The accuracy of the machine learning model was tested using the test data and 99.83% accuracy was obtained. Tool life model based on Gradient Descent Algorithm was successfully implemented for the tool life of Ti[C,N] mixed alumina ceramic cutting tool.Keywords: keyword 1; keyword 2; keyword 3 (List three to ten pertinent keywords specific to the article; yet reasonably common within the subject discipline.)

Subject Areas

Tool life; Machine Learning; Gradient Descent Algorithm; Prediction; Machining

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