Coupling Taguchi Experimental Designs with Deep Adaptive Learning Enhanced Artificial Intelligence Process Models: A Novel Case in Promising Experimental Cost Savings Possibilities in Manufacturing Process Development

Abstract

Abstract The Aluminum alloy AA7075 workpiece material is observed under dry finishing turning operation. This work is an investigation reporting promising potential of deep adaptive learning enhanced artificial intelligence process models for L18 (6133) Taguchi orthogonal array experiments and major cost saving potential in machining process optimization. Six different tool inserts are used as categorical parameter along with three continuous operational parameters i.e., depth of cut, feed rate and cutting speed to study their effect on output. Workpiece surface roughness and tool life are considered as output parameters. The data obtained from special L18 (6133) orthogonal array experimental design in dry finishing turning process is used to train AI models. Multi-layer perceptron based artificial neural networks (MLP-ANNs), support vector machines (SVMs) and decision trees are compared for better understanding ability of low resolution experimental design. Seven model evaluation criteria and external validation is used for final model selection. The AI models can be used with low resolution experimental design to obtain causal relationships between input and output variables. The best performing operational input ranges for surface roughness and tool life are identified keeping workpiece surface roughness as primary criteria of range selection in aerospace industry. TiN and TiCN are top two tool insets for obtaining low surface finish with maximum tool life under specified conditions. AI-response surfaces indicate different tool life behavior for alloy based coated tool inserts and non-alloy based coated tool inserts. The AI-Taguchi hybrid modelling and optimization technique helped in achieving 26% of experimental savings (obtaining causal relation with 26% less number of experiments) compared to conventional Taguchi design combined with two screened factors three levels full factorial experimentation.