The miniaturization of machine components is perceived by many as a requirement for the future technological development of a broad spectrum of products. Miniature components can provide smaller footprints, lower power consumption and higher heat transfer, since their surface-to-volume ratio is very high. To create these components, micro-meso-scale fabrication using miniaturized mechanical material removal processes has a unique advantage in creating 3D components using a variety of engineering materials. The motivation for micro-mechanical cutting stems from the translation of the knowledge obtained from the macro-machining domain to the micro-domain. However, there are challenges and limitations to micro-machining, and simple scaling cannot be used to model the phenomena of micro-machining operations. This paper surveys the current efforts in mechanical micro-machining research and applications, especially for micro-milling operations, and suggests areas from macro-machining that should be examined and researched for application to the improvement of micro-machining processes.

Investigation of micro-cutting operations

The state of a cutting tool is an important factor in any metal cutting process as additional costs in terms of scrapped components, machine tool breakage and unscheduled downtime result from worn tool usage. Several methods to develop monitoring devices for observing the wear levels on the cutting tool on-line while engaged in cutting have been attempted. This paper presents a review of some of the methods that have been employed in tool condition monitoring. Particular attention is paid to the manner in which sensor signals from the cutting process have been harnessed and used in the development of tool condition monitoring systems (TCMSs).

Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods

This paper presents a review of the state-of-the-art in sensors and signal processing methodologies used for tool condition monitoring (TCM) systems in industrial machining applications. The paper focuses on the technologies used in monitoring conventional cutting operations, including drilling, turning, end milling and face milling, and presents important findings related to each of these fields. Unlike existing reviews, which categorize results according to the methodology used, this paper presents results organized according to the type of machining operation carried out. By extensively reviewing and categorizing over one hundred important papers and articles, this paper is able to identify and comment on trends in TCM research, and to identify potential weaknesses in certain areas. The paper concludes with a list of recommendations for future research based on the trends and successful results observed, thus facilitating the cross-fertilization of ideas and techniques within the field of TCM research.

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State-of-the-art methods and results in tool condition monitoring: a review

Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network

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

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

A new approach using a neural network to process the features of the cutting force signal for the recognition of tool breakage in face milling is proposed. The cutting force signal is first compressed by averaging the cutting force signal per tooth. Then, the average cutting force signal is passed through a median filter to extract the features of the cutting force signal due to tool breakage. With the back propagation training process, the neural network memorizes the feature difference of the cutting force signal between with and without tool breakage. As a result, the neural network can be used to classify the cutting force signal with or without tool breakage. Experiments show this new approach can sense tool breakage in a wide range of face milling operations.

Sensing tool breakage in face milling with a neural network

A new intelligent sensor system using neural networks to separate tool breakage clearly from the cutter run-out or cutting transients in milling is proposed. The features of the spindle displacement signal are fed into the input layer of the neural network. With the back propagation training algorithm, the output of the neural network can be used to identify the milling cutter with or without tool breakage. Experiments show that this new approach can monitor tool breakage in milling operations successfully.

An intelligent sensor for monitoring milling cutter breakage

Tool failure diagnosis in milling using a neural network

➢ A Hill-Sachs lesion (HSL) is a compression fracture on the posterolateral humeral head that can increase the risk of recurrent shoulder instability after isolated arthroscopic Bankart repair.➢ Remplissage involves capsulotenodesis of the infraspinatus tendon and posterior capsule into the HSL to prevent its engagement with the glenoid rim through extra-articular conversion and restraint against humeral head anterior translation.➢ The glenoid track concept can be applied preoperatively and intraoperatively to evaluate risk of recurrence and help direct clinical management options for recurrent shoulder instability.➢ Recent literature supports expanding indications for remplissage to include patients with on-track HSLs who are at increased risk of recurrence including collision athletes, military personal, and patients with joint hyperlaxity.➢ New techniques and suture constructs have demonstrated improved biomechanical strength while avoiding the need to access the subacromial space.

S.T. Hwang

Papers

Sensing tool breakage in face milling with a neural network

An intelligent sensor for monitoring milling cutter breakage

Tool failure diagnosis in milling using a neural network

Arthroscopic Remplissage for the Treatment of Anterior Shoulder Instability