Electrochemical micromachining (EMM) appears to be a very promising micromachining process for having higher machining rate, better precision and control, reliability, flexibility, environmental acceptability, and capability of machining a wide range of materials. It permits machining of chemically resistant materials, like titanium, copper alloys, super alloys and stainless steel to be used in biomedical, electronic, micro-electromechanical system and nano-electromechanical system applications. Therefore, the optimal use of an EMM process for achieving enhanced machining rate and improved profile accuracy demands selection of its various machining parameters. Various optimization tools, primarily Derringer’s desirability function approach have been employed by the past researchers for deriving the best parametric settings of EMM processes, which inherently lead to sub-optimal or near optimal solutions. In this paper, an attempt is made to apply an almost new optimization tool, i.e. differential search algorithm (DSA) for parametric optimization of three EMM processes. A comparative study of optimization performance between DSA, genetic algorithm and desirability function approach proves the wide acceptability of DSA as a global optimization tool.

Suitable selection of various machining parameters for wire electrical discharge machining (WEDM) process heavily relies on the operator’s experience and manufacturer’s technologies because of their numerous and diverse operating ranges. Artificial neural networks have been introduced as an effective tool to predict values of responses and input parameters of different machining processes through forward and reverse modeling approaches respectively. This paper mainly focuses on predicting values of some machining responses, like machining rate, surface roughness, dimensional deviation and wire wear ratio using feed forward back propagation artificial neural network based on six WEDM process parameters, such as pulse on time, pulse off time, peak current, spark gap voltage, wire feed and wire tension. The corresponding reverse model is also developed to recommend the optimal settings of WEDM process parameters for achieving the desired responses according to the requirements of the end users. These modeling approaches are quite efficient to predict the values of machining responses as well as process parameter settings with reduced time and effort which otherwise have to be determined experimentally based on trial and error method. The predicted results are found to be in well congruence with the previously obtained experimental observations.

Selection of industrial robots for the present day’s manufacturing organizations is one of the most difficult assignments due to the presence of a wide range of feasible alternatives. Robot manufacturers are providing advanced features in their products to sustain in the globally competitive environment. For this reason, selection the most suitable robot for a given industrial application now becomes a more complicated task. In this paper, four models of data envelopment analysis (DEA), i.e. Charnes, Cooper and Rhodes (CCR), Banker, Charnes and Cooper (BCC), additive, and cone-ratio models are applied to identify the feasible robots having the optimal performance measures, simultaneously satisfying the organizational objectives with respect to cost and process optimization. Furthermore, the weighted overall efficiency ranking method of multi-attribute decision-making theory is also employed for arriving at the best robot selection decision from the short-listed competent alternatives. In order to demonstrate the relevancy and distinctiveness of the adopted DEA-based approach, two real time industrial robot selection problems are solved.

Nowadays, industrial robots are being pervasively used in almost every manufacturing organization for improving operational quality, safety and productivity. Depending on the nature of task to be performed, many varieties of robots are now commercially available from different manufacturers. For efficiently carrying out the designed task, a number of functional attributes of an industrial robot are also simultaneously responsible. Therefore, selection of an appropriate and competitive robot alternative becomes a complicated and equally challenging task for the decision makers. A quite strong model of multi-criteria decision-making is needed to deal with this problem of industrial robot evaluation and selection. In this paper, the applicability of fuzzy axiomatic design (FAD) principles is explored for solving a real time robot selection problem. Seven candidate robots which are commercially available for light assembly operations are evaluated with respect to a mix of nine criteria. All these criteria are either qualitative in nature or expressed as a range of numerical values. Suitability rankings of all the feasible alternatives are derived using FAD methodology, thus establishing it as a systematic and dependable tool for solving industrial robot selection problems in fuzzy environment.

With ever-increasing demands for high surface finish and complex shape geometries on various difficult-to-machine materials, conventional metal removal methods are now being replaced by non-traditional machining (NTM) processes. These NTM processes use energy in its direct form to remove material from the workpiece surface. They are also cost effective for a wide range of micro- and nano-level applications. For effective utilization of different NTM processes, it is quite important to study their characteristics and material removal mechanisms in order to identify the most significant control parameters affecting the process responses. In this paper, a data mining approach using classification and regression tree algorithm is employed to identify the most important input parameters of three NTM processes, i.e. micro electro discharge milling process, wire electrical discharge machining process and laser beam machining process. The derived observations are also validated using the analysis of variance results.

Supplier selection is always found to be a complex decision-making problem in manufacturing environment. The presence of several independent and conflicting evaluation criteria, either qualitative or quantitative, makes the supplier selection problem a candidate to be solved by multi-criteria decision-making (MCDM) methods. Even several MCDM methods have already been proposed for solving the supplier selection problems, the need for an efficient method that can deal with qualitative judgments related to supplier selection still persists. In this paper, the applicability and usefulness of measuring attractiveness by a categorical-based evaluation technique (MACBETH) is demonstrated to act as a decision support tool while solving two real time supplier selection problems having qualitative performance measures. The ability of MACBETH method to quantify the qualitative performance measures helps to provide a numerical judgment scale for ranking the alternative suppliers and selecting the best one. The results obtained from MACBETH method exactly corroborate with those derived by the past researchers employing different mathematical approaches.

Ultrasonic machining (USM) process has multiple performance measures, e.g. material removal rate (MRR), tool wear rate (TWR), surface roughness (SR) etc., which are affected by several process parameters. The researchers commonly attempted to optimize USM process with respect to individual responses, separately. In the recent past, several systematic procedures for dealing with the multi-response optimization problems have been proposed in the literature. Although most of these methods use complex mathematics or statistics, there are some simple methods, which can be comprehended and implemented by the engineers to optimize the multiple responses of USM processes. However, the relative optimization performance of these approaches is unknown because the effectiveness of different methods has been demonstrated using different sets of process data. In this paper, the computational requirements for four simple methods are presented, and two sets of past experimental data on USM processes are analysed using these methods. The relative performances of these methods are then compared. The results show that weighted signal-to-noise (WSN) ratio method and utility theory (UT) method usually give better overall optimisation performance for the USM process than the other approaches.

The designers often face the problem of choosing the best fitted material for a specific application from a huge array of available alternatives while simultaneously fulfilling all the given design requirements. For dealing with the material selection problems, a systematic and efficient approach is thus necessary. Selection of an optimal material from among various alternatives based on different conflicting subjective as well as objective attributes can be defined as a typical multi-criteria decision-making (MCDM) problem. The material selection problems for diverse engineering applications have already been solved by the past researchers employing different MCDM approaches. In this paper, the feasibility of Q-analysis technique as an MCDM tool is explored for arriving at the most appropriate decision regarding materials selection. Its applicability is demonstrated with the help of four illustrative examples and its capability to provide almost accurate material selection decisions is also validated.

The paper focuses on the application of TODIM (TOmada de Decisao Interativa Multicriterio), which means in Portuguese ‘interactive and multi-criteria decision making’) method in identify-ing the most attractive and affordable under-construction housing project in the city of Kolkata in India. In this decision making problem, 14 under-construction housing projects spanning in and around Kolkata are evaluated with respect to ten important criteria. The deployment of TODIM method can well be validated with respect to its ability to deal with both qualitative and quantitative criteria in the presence of risk factors. This method is also acknowledged to be a robust tool being almost unaffected by the varying values of the attenuation factor of losses. Using TODIM method, an under-construction housing project at Rajarhat in the eastern fringes of Kolkata city is selected as the optimal choice while meeting the requirements of investors, inhabitants and other stakeholders.

Non-traditional machining (NTM) refers to a variety of thermal, chemical, electrical and mechanical material removal processes, developed to generate complex and intricate shapes in advanced engineering materials with high strength-to-weight ratio. Selection of the optimal NTM process for generating a desired feature on a given material requires the consideration of several factors among which the type of the work material and shape to be machined are the most significant ones. Presence of a large number of NTM processes along with their complex characteristics and capabilities, and lack of experts in NTM process selection domain compel for development of a structured approach for NTM process selection for a given machining application. Thus, the objective of this paper is set to develop a decision-making model in Visual BASIC 6.0 to automate the NTM process selection procedure with the help of graphical user interface and visual decision aids. It is also integrated with quality function deployment technique to correlate the customers’ requirements (product characteristics) with technical requirements (process characteristics). Four illustrative examples are also provided to demonstrate the potentiality of the developed model in solving NTM process selection problems.