Development of a cyber physical production system framework for smart tool health management

Rishi Kumar, Kuldip Singh Sangwan (), Christoph Herrmann and Rishi Ghosh
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Rishi Kumar: Birla Institute of Technology and Science Pilani
Kuldip Singh Sangwan: Birla Institute of Technology and Science Pilani
Christoph Herrmann: Technische Universität Braunschweig – Institute of Machine Tools and Production Technology (IWF)
Rishi Ghosh: Birla Institute of Technology and Science Pilani

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 7, No 3, 3037-3066

Abstract: Abstract More and more organisations are trying to install tool health analytics dashboards for CNC machines to avoid unexpected failures, maintain machining accuracy, and optimise tool change. This paper aims at developing a cyber physical production system framework for a smart tool health management system to prescribe the optimum cutting parameters to managers/operators for optimising the remaining useful life and/or material removal rate at a predefined surface finish (individually or simultaneously). This is achieved by developing (i) a machine learning algorithm to predict the remaining useful life of a cutting tool, (ii) regression models to prescribe optimum cutting parameters (iii) a machine learning algorithm for anomaly detection, and (iv) a knowledge-based system for chip conditions and tool life curves. Experiments are designed and conducted based on Taguchi L-27 orthogonal array with varying combinations of cutting parameters during the milling of a difficult to machine material (AISI H13 tool steel). The effect of cutting parameters is analysed statistically; using analysis of variance (ANOVA), response tables, and main effect plots; to prescribe optimum cutting parameters based on managerial requirements. A novel knowledge-based system is also presented that updates knowledge and information about the chip colour at different health conditions of a tool. The present work will be a significant step towards improving productivity, product quality, and reducing maintenance costs by providing practitioners with an active decision support tool that will assist them to confidently adopt optimum management and control strategies within an Industry 4.0 environment.

Keywords: Diagnostic and prescriptive analytics; Cyber physical production system; Machine learning models; Remaining useful life prediction; Anomaly detection; Metal cutting (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02192-3

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