Non-traditional Machining Process Selection Research Articles

The m-polar fuzzy set ELECTRE-I with revised Simos’ and AHP weight calculation methods for selection of non-traditional machining processes

Using improvements to the recently published m-polar fuzzy set (mFS) elimination and choice translating reality-I (ELECTRE-I) approach for calculating criteria weights, the selection of a Non-Traditional Machining (NTM) process problem from the industry is solved in this research. The criteria weights for the m-polar fuzzy ELECTRE-I method are evaluated using the Analytical Hierarchy Process (AHP) approach and the Revised Simos' method. For the ELECTRE family's criteria weight calculations, the Simos’ approach has been revised. Many researchers calculated the weight of the criteria in the selection of the NTM process using the AHP approach. Problems with both physical and intangible properties can be solved using the m-polar fuzzy ELECTRE-I approach. Additionally, it has the ability to solve MCDM issues with more variables. The improved Simos' technique is used in this work because it incorporates user choices for the criteria, or user voting for the criterion. Using expert assistance, the AHP technique prioritizes the criterion based on pair-by-pair comparisons of the criteria. The AHP approach makes compromises between the criteria. The ultimate selection of the process based on the needed aim is affected by both tangible and intangible features in the NTM selection dilemma. The impact of criteria weight techniques on the choice of the NTM process is examined using a single dimensional sensitivity analysis. AHP approach is proven to be less stable for criteria weight variation than the improved Simos' weight calculation method. The updated Simos' method, which takes into account user preferences, performs better for the m-polar fuzzy ELECTRE-I algorithm than the AHP weight calculation method.

The m-polar fuzzy ELECTRE-I integrated AHP approach for selection of non-traditional machining processes

In the literature, there are a number of decision-making strategies that can be used to choose the best NTM processes. Chen (Citation2014) introduced a novel method to handle fuzzy data that includes multipolar uncertainty, referred to as the m-polar fuzzy set (mFS) approach. The mFS method, along with other multi-criteria decision-making (MCDM) techniques, is a good way to choose between options. An illustration of such a combination is the mFS elimination and choice translating reality-I (ELECTRE-I) . A criteria weight approach is also needed to increase the accuracy of the mFS ELECTRE-I method. The mFS ELECTRE-I method and the analytical hierarchy process (AHP) criteria weight calculation method are combined in the current work. The unique thing about this method is that it can be used to solve both MCDM and MCGDM problems by combining the mFS ELECTRE-I with the AHP criteria weight method. A single-dimensional weight sensitivity analysis is performed to confirm the technique’s stability for different criterion weights for the AHP method on alternative rank performance. The results of the NTM process selection are validated by previous research findings. EDM turned out to be the best way to create machine precise holes on duralumin alloy, and ECM turned out to be the best alternative to generate the surface of revolution in stainless steel. A C++-based soft solution that uses the mFS ELECTRE-I technique to analyze various MCDM and MCGDM problems has been developed. With the soft solution, you can fix problems with selecting the FMS, the robot, and so on.

Applications of Modified Simple Additive Weighting Method in Manufacturing Environment

Multiple criteria decisions making (MCDM) techniques are employed widely by decision-makers for ranking the potential alternatives under conflicting environments to select the best one for different industrial problems. Present work employed a modified Simple Additive Weighting (SAW) method to solve different decision-making problems in the manufacturing industry such as industrial robot selection, flexible manufacturing systems selection and, non-traditional machining processes selection respectively. The proposed methodology is simple and involves lesser mathematical complexity. The ranking obtained by the proposed modified SAW method corroborates well with other popular MCDM methods like MOORA, MABAC, TOPSIS and AHP for solving similar problems. It indicates the robustness of the proposed method. However, the proposed method is better compared to those methods through its simplicity, lesser computational complexity, and lesser computational time. Further, sensitivity analysis indicates the stability of the method. Being generic the method can be applied for solving problems related to ranking and selection in any societal segment.

Non-traditional machining processes selection and evaluation: A rough multi-attributive border approximation area comparison approach

Identification of the most appropriate non-traditional machining (NTM) process to generate a shape feature with the desired dimensional accuracy and surface finish in a work material for a given end application is a complex decision making task. It involves consideration and analysis of the machining capabilities and characteristics of each of the NTM processes. In order to evaluate the performance of the NTM processes with respect to their various characteristics, the opinions of the concerned process engineers are often sought which are usually expressed in terms of ambiguous/subjective data. Rough numbers are utilized to aggregate the judgments of those process engineers and express their observations while dealing with the impreciseness in the data. In this paper, an endeavour is put forward to solve two real time NTM process selection problems, i.e. generation of standard through holes in glass and deep through cavities in titanium while integrating rough numbers with multi-attributive border approximation area comparison (MABAC) method. The relative importance of various NTM process characteristics is determined using rough entropy technique. Rough set theory acts as an information aggregation procedure, while MABAC helps in ranking of the candidate NTM processes for a specified machining application. Ultrasonic machining emerges out as the best suited NTM process for generation of standard through holes in glass, while deep through cavities in titanium can be most efficiently generated using plasma arc machining process.

Prioritisation and selection of non-traditional machining processes and their criteria using analytic hierarchy process approach

In this study, a model is developed using analytic hierarchy process (AHP) methodology to help the process engineers and decision makers in prioritising and ranking the various NTM processes and their criteria identified from literature review and selecting the most appropriate NTM process and its criteria for typical machining process. The methodology also assists in identifying the most significant NTM processes and their criteria that could help the process engineers and decision makers for higher productivity and industry performance. Analysis of the present study shows that NTM process 'based on the source of energy' has ranked at first place followed by 'based on the medium of the energy transfer', 'based on the type of energy used' and 'based on the mechanism of material removal'. Further, the study also prioritised the various NTM processes criteria consisting of 27 criteria. The priority criteria that ranked on top are: voltage, current, ions and ionised particles. The findings of the present study will help process engineers, decision makers and practitioners in selecting the best NTM process and its criteria and then implementing them in their industry for improved performance.

Prioritisation and selection of non-traditional machining processes and their criteria using analytic hierarchy process approach

Prioritisation and selection of non-traditional machining processes and their criteria using analytic hierarchy process approach

A case-based reasoning approach for non-traditional machining processes selection

A case-based reasoning approach for non-traditional machining processes selection

A decision guidance framework for non-traditional machining processes selection

Abstract In order to realize the manufacturing/machining demands thrived by newer, hard and difficult-to-machine materials being utilized in the present day industries, an assortment of non-traditional machining (NTM) processes has been developed over the past few decades. These processes are capable of generating intricate and complex shapes with high degree of accuracy, close dimensional tolerance and better surface finish. In this paper, a decision guidance framework is developed in Visual BASIC 6.0 to help the process engineers in selecting the most appropriate NTM process for a specific work material and shape feature combination. It also assists in identifying the ideal process parameter combinations for the most suitable NTM process. The derived results highly corroborate with the opinions of the experts in the related field, demonstrating the acceptability of the developed system.

Application of fuzzy axiomatic design principles for selection of non-traditional machining processes

Since the last few decades, there have been tremendous technological advancements in communication, aeronautics, automobiles, textile engineering, nuclear energy, medical sciences and die-making industries. These have necessitated the use of some totally new and hitherto unknown high-strength temperature-resistant, tough and difficult-to-machine materials and, consequently, some newer unconventional processes for their efficient machining. It has been well established that non-traditional machining processes (NTMPs) far surpass their traditional counterparts in machining such advanced materials with respect to tolerance, surface finish, accuracy, complexity and miniatureness of the machined product/part. These NTMPs are also found to be more effective and economical. Choosing the most appropriate NTMP for generation of a desired shape feature on a given work material involves consideration of numerous conflicting qualitative and quantitative criteria. This paper proposes the application of fuzzy axiomatic design principles for selection of the most suitable NTMPs for generating cavities on ceramics and micro-holes on hardened tool steel and titanium materials, based on their practical/industrial importance. For micro-drilling operation on hardened tool steel, electrical discharge machining is found to be the best process followed by abrasive jet machining and ultrasonic machining. On the other hand, for generation of micro-holes on titanium, electrochemical machining is the most suitable process. Abrasive jet machining emerges out as the most efficient process for generating blind cavities on ceramics. These results are well in accordance with the expected machining practices and perfectly match with the decisions of the machining professionals.

A decision-making model for non-traditional machining processes selection

Article history: Received March 14, 2014 Accepted July 25, 2014 Available online July 31 2014 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. © 2014 Growing Science Ltd. All rights reserved.

Non-traditional machining process selection using integrated fuzzy AHP and QFD techniques: a customer perspective

With the advancement in material science and engineering, hard as well as difficult to cut materials are coming to the fore on regular basis. Machining such advanced materials with higher geometrical accuracy by conventional means is almost impossible. Thus, demand for higher accuracy with desired surface finish leads us to a large pool of non-traditional machining (NTM) processes. However, zeroing-in for a particular NTM process, for a particular operation and for a case-specific machining, is a cumbersome task. In order to make this task simpler for a decision-maker, the present research uses, fuzzy analytic hierarchy process to calculate the relative importance of various NTM processes taking product and process characteristics as the comparison basis. Finally, an overall score of various NTM processes have been obtained using quality function deployment methodology based on various shape features and work material combination. Also, the variations in the process capability features have been taken into consideration. Analysis of the present research study shows that electrochemical machining process has an edge over the other NTM processes with respect to surface finish, corner radii, minimum surface damage depth, and production time.

A fuzzy based decision model for nontraditional machining process selection

Manufacturing systems are processes in which inputs obtained from interior and exterior sources are transformed into an output by gathering inputs in an optimal way to guide the enterprises. Machining process plays a critical role in industry, and thus, directly affects the efficiency of the manufacturing systems. Due to different importance of the conflicting criterions, the multi-criteria decision-making methods are extremely useful in the selection process of the proper machining type. This study provides distinct systematic approaches in fuzzy and crisp environments to deal with the selection problem of proper machining process and proposes a decision support model for the guidance of decision makers to assess potentials of seven distinct nontraditional machining processes, namely laser beam machining, plasma arc machining, water jet machining, abrasive water jet machining, electrochemical machining, electrical discharge machining (EDM), and wire–EDM in the cutting process of carbon structural steel with the width of plate of 10 mm. The required data for decision and weight matrices are obtained via a questionnaire to specialists, as well as by deep discussions with experts and making use of past studies. Finally, an application of the proposed model is also performed via the SETED 1.0 software to show the applicability of the model.

Nontraditional machining processes selection using evaluation of mixed data method

Traditional edged cutting tool-based machining processes are now being continuously replaced by nontraditional machining (NTM) processes so as to generate complex and intricate shapes on advanced and harder materials, like titanium, stainless steel, high-strength temperature-resistant alloys, fiber-reinforced composites, and engineering ceramics. These NTM processes, while using energy in its direct form for removing materials from the workpiece surfaces, have the capabilities of meeting some higher level requirements, such as low tolerance, high surface finish, higher production rate, automated data transmission, miniaturization, etc., and are also quite suitable in the areas of micro- and nano-machining. Selection of the most appropriate NTM process to generate a desired shape feature on a given work material is often a challenging task as it involves consideration of diverse machining characteristics and performance of the NTM processes. This paper explores in details the applicability, suitability, and potentiality of evaluation of mixed data method for solving the NTM process selection problems. Three illustrative examples are presented, which validate the usefulness of this method. The observed results exactly corroborate with those obtained by the past researchers.

Application of PROMETHEE-GAIA method for non-traditional machining processes selection

Article history: Received April 17, 2012 Accepted 11 June 2012 Available online June 12 2012 With ever increasing demand for manufactured products of hard alloys and metals with high surface finish and complex shape geometry, more interest is now being paid to non-traditional machining (NTM) processes, where energy in its direct form is used to remove material from workpiece surface. Compared to conventional machining processes, NTM processes possess almost unlimited capabilities and there is a strong believe that use of NTM processes would go on increasing in diverse range of applications. Presence of a large number of NTM processes along with 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. Past researchers have already attempted to solve NTM process selection problems using various complex mathematical approaches which often require a profound knowledge in mathematics/artificial intelligence from the part of process engineers. In this paper, four NTM process selection problems are solved using an integrated PROMETHEE (preference ranking organization method for enrichment evaluation) and GAIA (geometrical analysis for interactive aid) method which would act as a visual decision aid to the process engineers. The observed results are quite satisfactory and exactly match with the expected solutions. © 2012 Growing Science Ltd. All rights reserved.

Non-traditional machining processes selection using data envelopment analysis (DEA)

The generic term ‘non-traditional machining’ (NTM) refers to a variety of thermal, chemical, electrical and mechanical material removal processes which have been developed due to lack of efficiency of the traditional machining processes to generate complex and intricate shapes in materials with ‘high strength-to-weight’ ratio. For effective utilization of the capabilities of different NTM processes, utmost care is needed for the selection of the most suitable process for a given machining application. Due to the lack of experienced experts in the domain of NTM processes, there is a need for a simple scientific/mathematical tool for selecting the most suitable NTM process when a particular shape feature is to be generated on a given work material. This paper focuses on the development of a two-phase decision model in this aspect. In the first phase, the most efficient NTM processes are selected for a given shape feature and work material combination having the best combination of performance parameters with the help of input-minimized-based Charnes, Cooper and Rhodes (CCR) model of data envelopment analysis (DEA). In the second phase, those efficient NTM processes are ranked in descending order of priority using the weighted-overall efficiency ranking method of multi-attribute decision-making (MADM) theory. Two real time machining applications are cited which prove the applicability, versatility and adaptability of this two-phase NTM process selection decision-making model as the results are quite consistent with those as derived by the past researches.

Selection of non-traditional machining processes using analytic network process

Non-traditional machining (NTM) processes are now being widely used to generate intricate and accurate shapes in materials, like titanium, stainless steel, high strength temperature resistant (HSTR) alloys, fiber-reinforced composites, ceramics, refractories and other difficult-to-machine alloys having higher strength, hardness, toughness and other diverse material properties. Generation of complex shapes in such materials by the traditional machining processes is experienced to be difficult. For effective utilization of the capabilities of different NTM processes, careful selection of the most suitable process for a given machining application is often required. Selection of the best suited NTM process for a work material and shape feature combination requires the consideration of several criteria. In this paper, an analytic network process (ANP)-based approach is proposed to select the most appropriate NTM process for a given machining application taking into account the interdependency and feedback relationships among various criteria affecting the NTM process selection decision. To avoid the difficult and time consuming mathematical calculations of the ANP, a computer program is also developed in Visual Basic 6.0 with graphical user interface to automate the entire NTM selection decision process. It simply acts as an ANP solver. The observed results from the ANP solver are quite satisfactory and match well with those obtained by the past researchers.

A combined TOPSIS-AHP-method-based approach for non-traditional machining processes selection

With the introduction and increased use of newer and harder materials such as titanium, stainless steel, high-strength temperature-resistant (HSTR) alloys, fibre-reinforced composites, and ceramics in the aerospace, nuclear, missile, turbine, automobile, tool, and die-making industries, a different class of machining processes has emerged. Instead of employing the conventional cutting tools, these non-traditional machining (NTM) processes use energy in its direct form to remove materials from the workpiece. Selection of the most suitable NTM process for machining a shape feature on a given work material requires consideration of several factors. A combined method using the ‘technique for order preference by similarity to ideal solution’ (TOPSIS) and an analytical hierarchy process (AHP) is proposed to select the most appropriate NTM process for a specific work material and shape feature combination, while taking into account different attributes affecting the NTM process selection decision. This paper also includes the design and development of a TOPSIS-AHP-method-based expert system that can automate the decision-making process with the help of a graphical user interface and visual aids. The expert system not only segregates the acceptable NTM processes from the list of the available processes, but also ranks them in decreasing order of preference. It also helps the user as a responsible guide to select the best NTM process by incorporating all the possible error-trapping mechanisms.

A digraph-based expert system for non-traditional machining processes selection

The presence of a number of available non-traditional machining (NTM) processes has brought out the idea of selecting the most suitable NTM process for generating a desired shape feature on a given work material. This paper presents a digraph-based approach to ease out the appropriate NTM process selection problem. It includes the design and development of an expert system that can automate this decision-making process with the help of graphical user interface and visual aids. The proposed approach employs the use of pair-wise comparison matrices to calculate the relative importance of different attributes affecting the NTM process selection decision. Based on the characteristics and capabilities of the available NTM processes to machine the required shape feature on a given work material, the permanent values of the matrices related to those processes are computed. Finally, if some of the NTM processes satisfy a certain threshold value, those are shortlisted as the acceptable processes for the given shape feature and work material combination. The digraph-based expert system not only segregates the accepted NTM processes from the list of the available processes but also ranks them in decreasing order of preference. It also helps the user as a responsible guide to select the most suitable NTM process by incorporating all the possible error trapping mechanisms. This paper takes into account some new work materials, shape features and NTM processes that have not been considered by the earlier researchers. It is further observed that the developed expert system is quite flexible and versatile as the results of the cited examples totally corroborate with those obtained by the past researchers.

Web-based Knowledge Based System for Selection of Non-traditional Machining Processes

This paper presents a web-based knowledge base system developed for identifying the most appropriate nontraditional machining process to suit specific circumstances based on the input parameter requirements such as material type, shape applications, process economy and some of the process capabilities namely, surface finish, corner radii, width of cut, length to diameter ratio, tolerance etc. The selection procedure proposed in this study is based on the idea that certain characteristics of a part restrict the choice of certain non-traditional machining processes for it to have a relatively small number of alternatives. Designers and Engineers who are geographically separated but well connected by the Internet can use this web-based system to realize process selection. Web technology has immense potential to develop a collaborative design and manufacturing environment. It simplifies the sharing of process knowledge and provides intelligent decision-making in a collaborative way through the Internet. This system employs three-tier web architecture for implementing user module to do the selection and expert module to update the knowledge base. The web-based non-traditional machining process selection system can cut down the product cost, enhance the product quality, and decrease the product lead-time considerably. A wide range of industrial parts has been evaluated in order to demonstrate the performance of the proposed selection procedure.

A Knowledge Base for Non-Traditional Machining Process Selection

A Knowledge Base for Non-Traditional Machining Process Selection