Wong Yoke San

An empirical study on the characterization of machined surface integrity by chip morphology in dry end-milling of titanium alloy

Titanium alloys manifest low thermal conductivity and high work hardenability in machining. These alloys are thus considered as difficult-to-machine at higher cutting speeds and pose serious problems in machining such as degraded machined surface and rapid tool-wear. Mostly, the integrity of the machined surface is assessed by post-process microscopic examination or by metallurgical testing techniques where machined workpiece needs to be further processed to perform subsequent testing on sophisticated equipment in usually meticulous ways. This study presents a qualitative but simple approach for the rapid characterization of the machined surface integrity in high-speed milling of titanium alloy. It has been established empirically that the chip morphology carries significant information about the machined surface integrity, and hence, can be considered as a reliable representative of the machined surface integrity.

A Model to Determine the Effect of Tool Diameter on the Critical Feed Rate for Ductile-Brittle Transition in Milling Process of Brittle Material

This paper presents analytical and experimental results of ductile-mode machining of brittle material by milling process. In milling process of brittle material, feed per edge is the predominant parameter to achieve ductile-mode machining and hence it limits the permissible material removal rate. An analytical model has been proposed to evaluate the effect of tool diameter on the critical feed per edge for ductile-brittle transition in milling process of brittle material. The proposed model has been validated experimentally by performing microcutting tests on tungsten carbide workpiece by milling process. It has been established by the model and the experimental results that an end-mill of larger diameter improves the critical feed per edge for ductile-brittle transition in milling process of brittle material.

Identification of feature set for effective tool condition monitoring — a case study in titanium machining

Due to the rapid wear of the cutting tools when machining titanium alloy, tool condition monitoring (TCM) is most useful to avoid workpiece damage and maximize machining productivity. This paper uses sensor signals and feature analysis to identify a feature set for effective TCM. Firstly, basic requirements of sensor signals in tool condition identification are discussed, and the suitability of two candidate signals (acoustic emission and cutting force) commonly employed for machining monitoring are critically analyzed. Their effectiveness in TCM is investigated based on extracted features of these signals, singly or in combination. Experimental results based on titanium machining, which is an expensive process with high tool wear, indicate that this proposed method is capable to determine a suitable sensing method and an effective feature set to identify tool condition.