Training and Tuning of Neuro - Fuzzy Control Laws for the Machining of Prosthetics

Abstract

Adaptive machining is a process of performing real-time parameter updates based on changes in external operating conditions. With the advancements being made in the area of customised implants, this study is conducted to determine the utilisation of Artificial Neural Network (ANN) within a Computer Numerical Control (CNN) manufacturing cell to perform real-time tool offset adjustment in the manufacture of prosthetic knees. This study integrates smart sensor technology in the CNC machining cell, enabling the acquisition of force data for a critical tool used in the machining of a Tibial component. The collected time series data is used to compare performance between a random forest classification algorithm and Bi-directional Long Term Short Memory (LSTM) neural networks. Pre-processing improvements are presented for the random forest algorithm using a time series data conversion step. In the case of the Bi-directional LSTM model, 2D time series data is converted to a 3D array using a novel projection technique. The result of both techniques is then converted to real world tool offset value by implementing a fuzzy logic controller. Results are presented with recommendations to the move from synthetic to real training data for future deployments.