An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Fringe projection techniques: Whither we are?

Wavelets for fault diagnosis of rotary machines: A review with applications

https://escholarship.org/content/qt4h56k1ch/qt4h56k1ch.pdf

Advanced monitoring of machining operations

This paper reviews the state-of-the-art of wavelet analysis for tool condition monitoring (TCM). Wavelet analysis has been the most important non-stationary signal processing tool today, and popular in machining sensor signal analysis. Based on the nature of monitored signals, wavelet approaches are introduced and the superiorities of wavelet analysis to Fourier methods are discussed for TCM. According to the multiresolution, sparsity and localization properties of wavelet transform, literatures are reviewed in five categories in TCM: time–frequency analysis of machining signal, signal denoising, feature extraction, singularity analysis for tool state estimation, and density estimation for tool wear classification. This review provides a comprehensive survey of the current work on wavelet approaches to TCM and also proposes two new prospects for future studies in this area.

Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results

A study on the machining of high-aspect ratio micro-structures using micro-EDM

Titanium alloys have been widely used in the aerospace, biomedical and automotive industries because of their good strength-to-weight ratio and superior corrosion resistance. However, it is very difficult to machine them due to their poor machinability. When machining titanium alloys with conventional tools, the tool wear rate progresses rapidly, and it is generally difficult to achieve a cutting speed of over 60m/min. Other types of tool materials, including ceramic, diamond, and cubic boron nitride (CBN), are highly reactive with titanium alloys at higher temperature. However, binder-less CBN (BCBN) tools, which do not have any binder, sintering agent or catalyst, have a remarkably longer tool life than conventional CBN inserts even at high cutting speeds. In order to get deeper understanding of high speed machining (HSM) of titanium alloys, the generation of mathematical models is essential. The models are also needed to predict the machining parameters for HSM. This paper aims to give an overview of recent developments in machining and HSM of titanium alloys, geometrical modeling of HSM, and cutting force models for HSM of titanium alloys.

/pdf/a-review-on-high-speed-machining-of-titanium-alloys-48m4fcphhh.pdf

A Review on High-Speed Machining of Titanium Alloys ∗

A study on the quality micro-hole machining of tungsten carbide by micro-EDM process using transistor and RC-type pulse generator

The capability of machining intricate features with high dimensional accuracy in hard and difficult-to-cut material has made electrodischarge machining (EDM) process as an inevitable and one of the most popular non-conventional machining processes. In recent years, both EDM and micro-EDM processes are being used extensively in the field of mould making, production of dies, cavities and complex 3D structures using difficult-to-cut tungsten carbide and its composites. The objective of this paper is to provide a state of the art in the field of EDM and micro-EDM of tungsten carbide and its composites. The review begins with a brief introduction on the EDM and micro-EDM processes. The research and developments in electrodischarge machining of tungsten carbide are grouped broadly into conventional EDM of tungsten carbide, micro-EDM of tungsten carbide and current research trends in EDM and micro-EDM of tungsten carbide. The problems and challenges in the area of conventional and micro-EDM of tungsten carbide and the importance of compound and hybrid machining processes are discussed. A summary of the future research directions based on the review is presented at the final section.

Yoke San Wong

Papers

Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results

A study on the machining of high-aspect ratio micro-structures using micro-EDM

A Review on High-Speed Machining of Titanium Alloys ∗

A study on the quality micro-hole machining of tungsten carbide by micro-EDM process using transistor and RC-type pulse generator

A review on the conventional and micro-electrodischarge machining of tungsten carbide