J.Y Kao

Bio: J.Y Kao is an academic researcher. The author has contributed to research in topics: Electrical discharge machining & Artificial neural network. The author has an hindex of 1, co-authored 1 publications receiving 103 citations.

State of the art electrical discharge machining (EDM)

Abstract: Electrical discharge machining (EDM) is a well-established machining option for manufacturing geometrically complex or hard material parts that are extremely difficult-to-machine by conventional machining processes. The non-contact machining technique has been continuously evolving from a mere tool and die making process to a micro-scale application machining alternative attracting a significant amount of research interests. In recent years, EDM researchers have explored a number of ways to improve the sparking efficiency including some unique experimental concepts that depart from the EDM traditional sparking phenomenon. Despite a range of different approaches, this new research shares the same objectives of achieving more efficient metal removal coupled with a reduction in tool wear and improved surface quality. This paper reviews the research work carried out from the inception to the development of die-sinking EDM within the past decade. It reports on the EDM research relating to improving performance measures, optimising the process variables, monitoring and control the sparking process, simplifying the electrode design and manufacture. A range of EDM applications are highlighted together with the development of hybrid machining processes. The final part of the paper discusses these developments and outlines the trends for future EDM research.

Predictions on surface finish in electrical discharge machining based upon neural network models

Abstract: Predictions on the surface finish of work-pieces in electrical discharge machining (EDM) based upon physical or empirical models have been reported in the past years. However, when the change of electrode polarity has been considered, very few models have given reliable predictions. In this study, the comparisons on predictions of surface finish for various work materials with the change of electrode polarity based upon six different neural-networks models and a neuro-fuzzy network model have been illustrated. The neural-network models are the Logistic Sigmoid Multi-layered Perceptron (LOGMLP), the Hyperbolic Tangent Sigmoid Multi-layered Perceptron (TANMLP), the Fast Error Back-propagation Hyperbolic Tangent Multi-layered Perceptron (Error TANMLP), the Radial Basis Function Networks (RBFN), the Adaptive Hyperbolic Tangent Sigmoid Multi-layered Perceptron, and the Adaptive Radial Basis Function Networks. The neuro-fuzzy network is the Adaptive Neuro-Fuzzy Inference System (ANFIS). Being trained by experimental data initially screened by the Design of Experiment (DOE) method, the parameters of the above models have been optimally determined for predictions. Based upon the conclusive results from the comparisons on checking errors among these prediction models, the TANMLP, RBFN, Adaptive RBFN, and ANFIS model have shown consistent results. Also, it is concluded that the further experimental results have agreed to the predictions based upon the above four models.  2001 Elsevier Science Ltd. All rights reserved. 1. Intrduction Electrical discharge machining (EDM), is a nontraditional machining process for metals removing based upon the fundamental fact that negligible tool force is generated during the machining

Artificial neural network models for the prediction of surface roughness in electrical discharge machining

Abstract: In the present paper Artificial Neural Networks (ANNs) models are proposed for the prediction of surface roughness in Electrical Discharge Machining (EDM). For this purpose two well-known programs, namely Matlab® with associated toolboxes, as well as Netlab®, were emplo- yed. Training of the models was performed with data from an extensive series of EDM experiments on steel grades; the proposed models use the pulse current, the pulse duration, and the processed material as input parameters. The reported results indicate that the proposed ANNs models can satisfactorily predict the surface roughness in EDM. Moreover, they can be considered as valuable tools for the process planning for EDMachining.