A Taguchi-Neural-Based In-Process Tool Breakage Monitoring System in End Milling Operations

Po Tsang B. Huang; James C. Chen; Chiuhsiang Joe Lin; Peng Hua Lyu; Bo Chen Lai

doi:10.4028/www.scientific.net/KEM.450.251

Paper Titles

Analysis of Heating Steady Temperature Field in an Al1100/Ti-6Al-4V/SiC 2D-FGM Plane Plate by FEM
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Study of Monitoring for Oil Film Thickness of Elastic Metallic Plastic Pad Thrust Bearing
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Improved Design of J-Box Steam Lines in Continuous Bleaching Range Based on TRIZ
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Productivity Improvement with Lean Production in Glove Manufacturing Industry
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A Taguchi-Neural-Based In-Process Tool Breakage Monitoring System in End Milling Operations
p.251

Redesign of a Substructure for a Milling Machine Using Topology Optimization
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A Real-Time Condition Monitoring System for a Machine Tool Spindle
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Assembly of Nanostructures by Using a Mechanical Nanomanipulator
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Sensor Fusion for Condition Monitoring System of End Milling Operations
p.267

HomeKey Engineering MaterialsKey Engineering Materials Vol. 450A Taguchi-Neural-Based In-Process Tool Breakage...

A Taguchi-Neural-Based In-Process Tool Breakage Monitoring System in End Milling Operations

Abstract:

The use of CNC machines is one of the successful factors in the computer integrated manufacturing (CIM). Even though the CNC machine can automatically perform the machining processes, some of the situations that may significantly influence the quality of product such as a cutting tool breakage. Therefore, to prevent the machine from damaging and ensure the quality of product, it is important to develop a system that can monitor the tool conditions. The purpose of this study is to develop a Taguchi-neural-based in-process tool breakage monitoring system in end milling operations that can monitor the tool conditions and immediately response a proper action. For an in-process tool breakage monitoring system, a neural network was applied to making decisions of monitoring. One of the disadvantages of neural network is the training processes. It is difficult to determine an optimal combination of training parameters of neural networks. Traditionally, the try-and-error method is time-consuming and without systematic base. Therefore, the optimization of training parameters for neural networks using Taguchi design was applied to training the neural network model and to enhance the accuracy of the tool breakage monitoring system.