Predrag Janković

Taguchi Approach for the Optimization of Cutting Parameters in Finish Turning of Medical Stainless Steel

Optimization of cutting parameters in finish turning of medical stainless steel 316LVM with coated carbide tools using Taguchi method is proposed in this paper. Four cutting parameters namely, insert radius, depth of cut, feed and cutting speed are optimized with considerations of surface roughness as performance characteristic. The effects of cutting parameters on the surface roughness were experimentally investigated. Experimentation was conducted as per Taguchis orthogonal array. Four cutting parameters with three levels are arranged in L27 orthogonal array. The orthogonal array, measured values of surface roughness, signal-to-noise ratios and analysis of variance are employed to study the surface roughness. Based on the analysis, the optimal cutting parameter settings were determined. Through the confirmation test with optimal cutting parameter settings the effectiveness of the optimization approach are validated. The obtained results have shown that Taguchi method is suitable for optimizing the cutting parameter levels with the minimum number of experiments.

Multi-Objective Optimization of Laser Cutting Using ROV-Based Taguchi Methodology

Multi-objective optimization of laser cutting for simultaneous improvement of performance characteristics is of great practical importance. In this study a range of value (ROV)-based Taguchi methodology is proposed for multi-objective optimization of laser cutting, i.e. surface roughness, kerf width and burr height in CO2 laser cutting of AISI 304 stainless steel. Laser cutting experiment was conducted based on Taguchi’s L27 experimental design by varying the laser power, cutting speed, assist gas pressure and focus position at three levels. In the proposed methodology based on the experimental data signal to noise ratios as per Taguchi’s method were calculated for each experimental trial upon which decision matrix was defined. Subsequently, multi-criteria decision making problem was solved by the ROV method. The proposed ROV-based Taguchi methodology has relatively simple computational procedure and can be easily applied by engineers for solving different multi-objective optimization problems that occur in real manufacturing environment.

Application of Recently Developed MCDM Methods for Materials Selection

It is well known fact that materials play an important role in engineering design. Nowadays over a hundred thousand available materials can be distinguished with constant tendency for increasing the novel designed materials. Therefore material selection process becomes a complex and time consuming task. Selection of the most suitable material for a given application can be regarded as a multi-criteria decision making (MCDM) problem with conflicting and diverse objectives. New MCDM methods have been developed, and existing methods improved, showing that research in the decision-making is important and still valuable. This paper describes the use of recently developed MCDM methods, i.e. Complex Proportional Assessment (COPRAS) and Weighted Aggregated Sum Product Assessment (WASPAS) for selecting the most suitable hard coating material.

Aspects of Machining Parameter Effect on Cut Quality in Abrasive Water Jet Cutting

Abrasive water jet machining is frequently used in industry. It is one of the most versatile processes in the world. The basic advantages of abrasive water jet machining is that no heat affected zones or mechanical stresses are left on an abrasive water jet cut surface, high flexibility and small cutting forces. Although this cutting technology includes many advantages, there are some drawbacks. For instance, abrasive water jet cutting can produce tapered edges on the kerf of workpiece being cut. This can limit the potential applications of abrasive water jet cutting, if further machining of the edges is needed to achieve the engineering tolerance required for the part. The machining parameters have a great influence on these phenomena. The aim of this paper is to investigate the cut quality of EN AW-6060 aluminium alloy sheets under abrasive water jets. The experimental results indicate that the feed rate (nozzle traverse speed) of the jet is a significant parameter on the surface morphology.

Artificial Intelligence Model for the Prediction of Cut Quality in Abrasive Water Jet Cutting

In abrasive water jet cutting, the cut quality is of great importance. In this paper, artificial intelligence model was developed for the prediction of cut quality in abrasive water jet cutting of aluminum alloy. To this aim, artificial neural network (ANN) model was developed in terms of workpiece material thickness, traverse rate and abrasive flow rate. Three-layered feedforward ANN model having four hidden neurons trained with backpropagation algorithm with momentum was used for modeling purposes. The mathematical model showed high prediction accuracy with average absolute percentage error of about 3 %. Using the developed ANN model, 3-D graphs, showing the interaction effects of the traverse rate and abrasive flow rate for three different thicknesses, were given. It was showed that ANNs may be used as a good alternative in analyzing the effects of abrasive water jet cutting parameters on the cut quality characteristics.