Decision support system for tool condition monitoring in milling process using artificial neural network
T. Mohanraj, A. Tamilvanan
Abstract
This work discusses the design of tool condition monitoring system (TCMs) during milling of AISI stainless steel 304 using sound pressure and vibration signals. Response Surface Methodology (RSM) was used to design the experiments. The various milling parameters and vegetable-based cutting fluids (VBCFs) were optimized to reduce the surface roughness and flank wear. The experimental results reveal the direct relationship between the flank wear and sound & vibration signals. The various statistical parameters were extracted from the measured signals and given as input data to train the neural network. From the developed ANN model, the flank wear was predicted with the mean squared error (MSE) of 0.0656 mm.
Topics & Concepts
FlankArtificial neural networkVibrationTool wearSurface roughnessMean squared errorResponse surface methodologyProcess (computing)Surface finishComputer scienceAcousticsMechanical engineeringArtificial intelligenceEngineeringMaterials scienceMachiningMachine learningMathematicsStatisticsComposite materialAnthropologySociologyPhysicsOperating systemAdvanced machining processes and optimizationAdvanced Machining and Optimization TechniquesAdvanced Surface Polishing Techniques