The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

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Machine learning in manufacturing: advantages, challenges, and applications

Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method

This paper presents an overview of the current technological and economical capabilities of electrochemical (ECM-based), electro-physical (EDM-based) and photonic (Laser-/EBM-based) additive and removal processes for turbomachinery component manufacture. Starting with the industrial demands and challenges of today, the technologies are reviewed in detail regarding achievable geometrical precision and surface integrity as well as material removal and deposition rates for conventionally difficult-to-cut Ti- and Ni-based alloys and dedicated steels. Past, existing and future areas of technology application of these advanced non-mechanical manufacturing processes are discussed. The paper focusses on the description of shaping processes therefore excludes pure welding or coating applications.

Turbomachinery component manufacture by application of electrochemical, electro-physical and photonic processes

Modeling And Optimizing of Different Quality Characteristics In Electrical Discharge Drilling Of Titanium Alloy (Grade-5) Sheet

Multi-response optimization of minimum quantity lubrication parameters using Taguchi-based grey relational analysis in turning of difficult-to-cut alloy Haynes 25

Electrical discharge machining of titanium alloy (Ti–6Al–4V)

A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method

A wire electrical discharge machined (WEDM) surface is characterized by its roughness and metallographic properties. Surface roughness and white layer thickness (WLT) are the main indicators of quality of a component for WEDM. In this paper an adaptive neuro-fuzzy inference system (ANFIS) model has been developed for the prediction of the white layer thickness (WLT) and the average surface roughness achieved as a function of the process parameters. Pulse duration, open circuit voltage, dielectric flushing pressure and wire feed rate were taken as model's input features. The model combined modeling function of fuzzy inference with the learning ability of artificial neural network; and a set of rules has been generated directly from the experimental data. The model's predictions were compared with experimental results for verifying the approach.

An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM

This paper presents an effective approach for the optimization of laser cutting process of St-37 steel with multiple performance characteristics based on the grey relational analysis. Sixteen experimental runs based on the Taguchi method of orthogonal arrays were performed to determine the best factor level condition. The response table and response graph for each level of the machining parameters were obtained from the grey relational grade. In this study, the laser cutting parameters such as laser power and cutting speed are optimized with consideration of multiple-performance characteristics, such as workpiece surface roughness, top kerf width and width of heat affected zone (HAZ). By analyzing the grey relational grade, it is observed that the laser power has more effect on responses rather than cutting speed. It is clearly shown that the above performance characteristics in laser cutting process can be improved effectively through this approach.

Use of the grey relational analysis to determine optimum laser cutting parameters with multi-performance characteristics

In this study, the effect and optimization of machining parameters on surface roughness and tool life in a turning operation was investigated by using the Taguchi method. The experimental studies were conducted under varying cutting speeds, feed rates, and depths of cut. An orthogonal array, the signal-to-noise (S/N) ratio, and the analysis of variance (ANOVA) were employed to the study the performance characteristics in the turning of commercial Ti-6Al-4V alloy using CNMG 120408-883 insert cutting tools. The conclusions revealed that the feed rate and cutting speed were the most influential factors on the surface roughness and tool life, respectively. The surface roughness was chiefly related to the cutting speed, whereas the axial depth of cut had the greatest effect on tool life.

Ulaş Çaydaş

Papers

Electrical discharge machining of titanium alloy (Ti–6Al–4V)

A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method

An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM

Use of the grey relational analysis to determine optimum laser cutting parameters with multi-performance characteristics

Optimization of turning parameters for surface roughness and tool life based on the Taguchi method