Selection Of Machining Processes Research Articles

Automated manufacturability analysis and machining process selection using deep generative model and Siamese neural networks

Industry 4.0 calls for highly autonomous manufacturing process planning. Significant effort has been devoted over the years to generative Computer Aided Process Planning (CAPP), which aims to generate process plans for new designs without human intervention. This goal has not been realized to date due to several reasons, such as poor scalability and the difficulty in modeling manufacturing process capability, which encapsulates the part shape, quality, and material property transformation capabilities of the process. In our prior work, the shape transformation capabilities of lathe-based machining operations were modeled as latent probability distributions using a data-driven deep learning-based generative machine learning approach, from which visualizations of realistic machinable features could be sampled to assist manual process selection. In this paper, a Siamese Neural Network (SNN) is integrated with Autoencoder-based deep generative models of machining operations to enable automated comparison of the query part shapes with sampled outputs. This enables automated manufacturability analysis and machining process selection necessary for generative CAPP. The paper also demonstrates that the proposed Autoencoder and Siamese Neural Network (AE-SNN) achieves a class-average process selection accuracy of 89 %, and a manufacturability analysis accuracy of 100 %, which outperforms a discriminative model trained on the same dataset.

Generative Modeling of the Shape Transformation Capability of Machining Processes

The shape, material property, and part quality transformation capabilities of a manufacturing process are essential process capability knowledge that are traditionally acquired by process planners through experience. While efforts have been made over the years to develop automated systems that utilize known process capabilities for process selection and manufacturability assessment of part designs, such systems are hampered by the lack of a systematic approach to capture and model the shape, material property, and part quality transformation capabilities from design and manufacturing data. In this paper, the shape transformation capabilities of representative machining operations are modeled using 3D Variational Autoencoders and Generative Adversarial Networks (3D-VAE-GANs.) The proposed approach models the shape transformation capability as a latent probability distribution from which visualizations of realistic machinable features can be sampled for shape decomposition and reconstruction, thereby assisting machining process selection by a process planner and manufacturability assessment of part shapes generated by a designer.

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.

Selection of non-conventional machining process using CRITIC-CODAS method

High strength materials such as stainless steel, titanium, ceramics, and composites are widely used in the manufacturing of various components for aircraft, missile, turbine, automotive, tool and die-making, etc. These high-strength and difficult-to-machine materials are processed using a variety of non-conventional machining (NCM) processes to produce the desired forms with required dimensional accuracy and surface finish. The NCM processes are costly and consume a lot of electricity, thus, choosing the best NCM process is critical for cost-effective machining. The proper selection of the NCM processes is essential to the productivity and profitability of the company. In this research work, the NCM process selection problem is addressed using CRITIC (criteria importance through inter-criteria correlation) and CODAS (combinative distance-based assessment) multicriteria decision-making methods. The successive integration of CRITIC and CODAS makes the computational methodology simple yet effective for use in NCM process selection problems. A case study is conducted to assess the performance of proposed approach while selecting the best NCM process for machining titanium from the eight most often used NCM processes, namely, AJM (abrasive jet machining), USM (ultrasonic machining), CHM (chemical machining), EBM (electron beam machining), LBM (laser beam machining), ECM (electrochemical machining), EDM (electrodischarge machining), and PAM (plasma arc machining). Material removal rate, shape feature, work material type, tolerance and surface finish, power requirement, and cost are the criteria used to evaluate and pick the best NCM process. The criteria are weighted using the CRITIC method. Based on the criteria stated, PAM is proven to be the best possible NCM process for machining titanium, followed by EBM. The acquired results are compared to the results of previous studies and found to be in good accord.

Critical Analysis of the Selection of Non-Conventional Machining Processes

Non-Convectional Machining (NCM) processes are used widely to produce accurate and intricate material shape such as titanium, stainless steels and resistant alloy that are of high strength, fiber-based composite, refractories and ceramics. The production of more complex shapes of materials using convectional machining processes is considered to be challenging. This research paper focusses on the section of the most effective NCM process. The ‘choice’ of the most considerable NCM process for a particular application could be seen as a multi-technique for making proper choices for diverse or conflicting approaches. To aid the process of choosing, various NCM techniques have been proposed in this research. This contribution focuses on the usage of unexplored NCM and Multi-Feature Decision-Making (MFDM) ‘choice’ issues.

Implementation of Jaya algorithm for process parameter optimization during EDM processing of NiTi 60alloy

Electrical Discharge Machining (EDM) process is an advanced machining process used in wide range of manufacturing applications especially for hard materials. Functioning and quality of EDMed machined parts/components plays important role when put to the service. For this not only selection of optimum machining process but also selection of effective optimization technique need to be given special attention. With this intention the target of the current research is to survey the different optimization techniques (Response surface, Taguchi, Grey regression analysis, Artificial nueral network etc.), research gap indicates scope for implementation of Jaya algorithm which is a modern optimization technique. With use of Jaya algorithm technique research work was carried out to evaluate white layer thickness (WLT) and micro hardness (MH) as output parameters during EDM machining of NiTi60 alloy. These output parameters were evaluated with variations in input process parameters considering voltage (V), current (C), pulse on (Pon) and pulse off (Poff) time. The experiments were conducted using L9 Taguchi orthogonal array and mathematical predictions models were generated using regression analysis. Jaya algorithms were developed with the intention to minimize the WLT and minimize the MH as the objective functions. It is concluded that Jaya optimization technique can be effectively implemented to optimize the process parameters in EDM process which shows considerable reductions in WLT from 5.914 to 4.947 µm and MH from 208.71 to 200.3 respectively.

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.

Hybrid Non Conventional Machining of Glass - A Review

Glass, being considered as hard and brittle material is very difficult to machine into desired shapes. The readily available conventional machining process does not provide good surface finish thus requires additional machining process. This paper reviews the different existing non conventional machining process accessible till today for the machining of glass materials. This paper also discusses the advantages and disadvantages of the existing non conventional machining processes. The various hybrid non conventional machining processes are also studied with focus on machining output characteristics like MRR, surface finish, tool wear rate. This paper summarizes the selection of hybrid non conventional machining processes for the various type of glass.

A novel two-stage optimization approach to machining process selection using error equivalence method

The selection of a machining process involves the choice of machine tools, fixture elements, and fixture locator layout, as well as the allocation of tolerance in each operation. In practice, manufacturers frequently choose identical machine tools and fixture elements for each operation to reduce purchase cost. As such, fixture layout and tolerance allocation are critical in selecting or designing appropriate manufacturing processes. Conventional research deals with robust fixture layout design and simultaneous tolerance allocation for multiple types of error source separately. However, fixture layout design could also affect tolerance stackup caused by multiple error (not only the fixture error) sources. Therefore, considering the interaction between fixture layout and other types of error source is critical in the process selection to improve the process selection strategy. In this paper, a two-stage framework is proposed to optimize the process selection based on our previously developed error equivalence model, which transforms multiple errors into equivalent errors that occur on a fixture. In the first stage, a process is selected by determining the allowable tolerance for an aggregated base error given a fixture layout. In the second stage, a computer experiment model is established to search for the globally optimal fixture layout by exploring a large number of fixture layout alternatives. A real-world case study based on a two-operation machining process demonstrated the effectiveness of the proposed strategy in controlling manufacturing cost while ensuring product quality via proper fixture layout design.

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

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

Micro Machining Process Selection: An Integrated Theory

Selection of most apposite machining method is very crucial in precision machining of micro parts. Micro machining has wide applications in the fabrication of micro-electro-mechanical systems (MEMS), micro- fluidics and precision surgical instruments. The machining processes for these diverse applications have to be selected based on different accuracy attributes combined with least operating cost. So formulation of an appropriate selection methodology is very decisive. The aim of this paper is to develop a methodology for machining process selection by comparing the various attributes. An integrated approach combining technique for order preference by similarity to ideal solution (TOPSIS) & analytic hierarchy process (AHP) have been proposed. A case study has been presented to select from the major nontraditional machining processes for the purpose of micro drilling of fuel injector nozzle hole of specific diameter ranges by comparing their various attributes. The research shows that the methodology TOPSIS integrated with AHP is a very effective technique in selection of machining process based on applications by considering different attributes.

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.

An Expert System Based on a Hybrid Multi-Criteria Decision Making Method for Selection of Non-Conventional Machining Processes

In this study, a knowledge-based system has been developed for selection of non-conventional machining processes using a hybrid multi-criteria decision making Method. This approach is a combination ofDEMATEL(Decision Making Trial and Evaluation Laboratory),ANP(Analytic Network Process) andVIKOR(VlseKriterijumska Optimizacija I Kompromisno Resenje, in Serbian, meaning Multi-criteria Optimization and Compromise Solution) methods which evaluates different types of quantitative and qualitative measures of performance and economic factors, and ultimately provides a set of capable processes in order of priority. Twelve machining processes, eight group of workpiece material and eighteen shape features have been investigated in this study. What separates this approach from others is that, this hybrid method considers the influence of factors in the network relation map as well as their relative importance. Moreover, unlike other popular ranking methods such as TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution), it is not just based on two reference points, namely ideal and inferior points; instead, it proposes a compromise solution and not just a single ranking score. Observations have shown that the developed system works satisfactorily, yields acceptable results and makes accurate decisions as well. It also provides a comparative study among the alternative processes by utilizing graphical features for better analysis and judgment of acceptable alternatives.

Selection of non-conventional machining processes using the OCRA method

Selection of the most suitable nonconventional machining process (NCMP) for a given machining application can be viewed as multi-criteria decision making (MCDM) problem with many conflicting and diverse criteria. To aid these selection processes, different MCDM methods have been proposed. This paper introduces the use of an almost unexplored MCDM method, i.e. operational competitiveness ratings analysis (OCRA) method for solving the NCMP selection problems. Applicability, suitability and computational procedure of OCRA method have been demonstrated while solving three case studies dealing with selection of the most suitable NCMP. In each case study the obtained rankings were compared with those derived by the past researchers using different MCDM methods. The results obtained using the OCRA method have good correlation with those derived by the past researchers which validate the usefulness of this method while solving complex NCMP selection problems.

Selection of the most suitable non-conventional machining processes for ceramics machining by using MCDMs

Selection of the most suitable non-conventional machining process (NCMP) for a ceramics machining represents a multi-criteria decision making (MCDM) problem. This paper describes the application of relatively novel MCDM methods for selecting the most suitable NCMP for the ceramics machining. By applying WASPAS and COPRAS methods, ten NCMPs (alternatives) were ranked based on the ten criteria. Comparison of obtained ranking performances with other MCDM methods used by previous researchers was carried out in order to demonstrate WASPAS and COPRAS applicability and capability for non-conventional machining process selection.

A classification of multi-axis features based on manufacturing process

A machining feature can be regarded as the shape of the removal volume through a cutting process, which represents some manufacturing attributes of a part. Since the machining feature can play a crucial role in process planning, it is desirable to name machining features in such a way that a clear correlation between machining features and machining methods are reflected in the feature classification. However, most researches dealing with this topic do not clearly show the relationship between machining features and machining processes. On the other hand, no consensus on the feature classification method exists yet, which causes confusion when using the term “feature”. As a result, use of the features has not been very helpful in manufacturing preparation such as machining process selection. In this paper, classification methods of multi-axis machining features known in relevant literatures are studied firstly. Secondly, machining characteristics of various features are investigated to find out the relationship between machining features and machining methods. Thirdly, based on the machining methods of features, a logical classification scheme of multi-axis machining features is proposed. And finally, the proposed feature taxonomy is verified through applying to an example part with multi-axis machining features.

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 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.

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.