Tool Wear Prediction using Neural Networks.
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This chapter contains information on the fundamentals of the machining process, the tools used in the process, tool wear, surface roughness, and cutting force. It also sheds light on the necessity of a force measurement system to assess flank wear. 1.1 INTRODUCTION
The process of machining involves removing material in order to create the desired dimensions with the needed accuracy and surface polish.This is done to boost performance while also meeting the product's functional needs.To reduce cutting force, surface roughness, and tool flank wear during the machining process, the best process parameters must be chosen.For turning operations, a single point cutting tool is typically utilized.The new surface created when the material is removed from the work piece is referred to as the machined surface.As a result of the adhesive wear mechanism, the work piece and tool surface come into contact during the turning process and cause tool wear. The shape or location of the cutting edge, the size of the particle, and the dimensions of the work piece can all be used as direct indicators of tool wear.Using signals such as cutting force, acoustic emission, sound, vibration, motor current, cutting temperature, and surface roughness of the component where the parameter linked with tool wear and was measured, tool wear can be indirectly quantified.The ideal process variable is Optimisation techniques such as Response Surface Methodology (RSM), Taguchi's Design of Experiments (DoE), and optimization algorithms such as Particle Swarm Optimisation (PSO), Cuckoo Search (CS),
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