Optimization of machining parameters of composites using multi-attribute decision-making techniques: A review

An in-depth literature survey of machining study on fiber-matrix composites is presented in this review paper. The review work mainly focuses on optimization of machining parameters in composite materials with different machining factors. Conventional machining processes such as turning, drilling and milling as well as composite materials which are reinforced with fibers are considered in this study. Machining aspects on various fiber matrix composites has been carried out over a long period of time. In this review work, conflicting conditions of multi-attribute decision-making techniques and machining conditions are focused. The optimization study on machining parameters is done, considering both priori and posteriori approach including advanced optimization techniques. Optimization of machining parameters in fiber reinforced particulate composites has not been explored earlier. The review work on machining study of composites was not attempted earlier and hence this work provides valuable information for subsequent researchers to enhance the scope of research work in particle-reinforced polymer composites.

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Parametric analysis of dry machining process using a novel integrated multi-attribute decision making approach

Decision Science Letters ◽

10.5267/j.dsl.2021.11.001 ◽

2022 ◽

Vol 11 (2) ◽

pp. 193-202

Author(s):

G. Venkata Ajay Kumar ◽

A. Ramaa ◽

M. Shilpa

Keyword(s):

Decision Making ◽

Process Parameters ◽

Machining Process ◽

Machining Parameters ◽

Machining Processes ◽

Order Preference ◽

Dry Conditions ◽

Criteria Importance ◽

The Right ◽

Selection Of

In most of the machining processes, the complexity arises in the selection of the right process parameters, which influence the machining process and output responses such as machinability and surface roughness. In such situations, it is important to estimate the inter-relationships among the output responses. One such method, Decision-Making Trial and Evaluation Laboratory (DEMATEL) is applied to study the inter-relationships of the output responses. Estimation of proper weights is also crucial where the output responses are conflicting in nature. In the current study, DEMATEL technique is used for estimating the inter-relationships for output responses in machining of EN 24 alloy under dry conditions. CRiteria Importance Through Inter-criteria Correlation (CRITIC) method is used to estimate the weights and finally the optimal selection of machining parameters is carried out using Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The model developed guides the decision maker in selection of precise weights, estimation of the inter relationships among the responses and selection of optimal process parameters.

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Analysis of Tool Wear while Milling Hybrid Metal Matrix Composites

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.813-814.279 ◽

2015 ◽

Vol 813-814 ◽

pp. 279-284 ◽

Cited By ~ 1

Author(s):

A. Arun Premnath ◽

P. Suryatheja ◽

A. Srinath ◽

S. Karthikeyan

Keyword(s):

Tool Wear ◽

Cutting Speed ◽

Weight Fraction ◽

Stir Casting ◽

Particulate Composites ◽

Depth Of Cut ◽

Machining Parameters ◽

Matrix Composites ◽

Materials Used ◽

Average Size

In the present work, an attempt has been made to analyze the factors influencing tool wear while milling Al/Al2O3/Gr particulate composites. Materials used for the present investigation are Al 6061-aluminium alloy reinforced with alumina (Al2O3) of size 45 microns and graphite (Gr) of an average size 60 microns, which are produced by stir casting route. Central composite design (CCD) was employed in developing the tool wear model in relation to machining parameters such as feed rate, cutting speed, depth of cut and weight fraction of Alumina. From the Analysis of variance (ANOVA), it is found that feed is the dominant parameter for tool wear whereas weight fraction of alumina shows minimal effect on tool wear compared to other parameters. From the Scanning Electron Microscope (SEM), the Al2O3 and Gr particles get adhered to the tool surface owing to the high pressure generated at the tool-workpiece interface.

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Soft Computing Applications in Drilling of GFRP Composites: A Review

Materials Science Forum ◽

10.4028/www.scientific.net/msf.766.99 ◽

2013 ◽

Vol 766 ◽

pp. 99-107

Author(s):

V.S. Senthil Kumar ◽

C. Ezilarasan

Keyword(s):

Surface Roughness ◽

Composite Structures ◽

Thrust Force ◽

Optimization Techniques ◽

Machining Parameters ◽

Automotive Electronics ◽

Gfrp Composites ◽

Machining Processes ◽

Nsga Ii ◽

Reinforced Plastics

Glass fiber reinforced plastics (GFRP) are finding increased applications in various engineering fields such as aerospace, automotive, electronics and other industries. Among the various machining processes, drilling is the important process, mainly used in joining of composite structures. As a consequence, the number of authors have discussed on the aspects concerning the machiniability of GFRP composites. In this study, a review has been done on the machinability of drilling of GFRP composites through the various aspects such as tool materials and geometry, machining parameters and their influence on thrust force, torque, surface roughness, delamination factor and hole damage. Additionally, the modeling of the machining parameters on drilling of GFRP composites using response surface methodology (RSM), artificial neural network (ANN), fuzzy logic, NSGA-II etc., have been discussed. The results indicated that the thrust force, torque and surface roughness need to be controlled simultaneously for delamination free drilling. Further, there is a need to create a multi-response optimization in drilling of GFRP composites using different optimization techniques for obtaining optimum results of thrust force, torque, surface roughness and delamination free drilling.

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Plithogenic n-Super Hypergraph in Novel Multi-Attribute Decision Making

10.54216/ijns.070101 ◽

2020 ◽

pp. 08-30

Author(s):

Florentin .. ◽

◽

Nivetha Martin

Keyword(s):

Decision Making ◽

Research Work ◽

Decision Makers ◽

Optimal Decision ◽

The Novel ◽

Multi Attribute Decision Making ◽

Vertex Classification ◽

Optimal Decision Making ◽

Novel Concept

An optimal decision-making environment demands feasible Multi-Attribute Decision-Making methods. Plithogenic n – Super Hypergraph introduced by Smarandache is a novel concept and it involves many attributes. This article aims to bridge the concept of Plithogenic n-Super Hypergraph in the vicinity of optimal decision making. This research work introduces the novel concepts of enveloping vertex, super enveloping vertex, dominant enveloping vertex, classification of the dominant enveloping vertex (input, intervene, output dominant enveloping vertices), plithogenic connectors. An application of Plithogenic n-super hypergraph in making optimum decisions is discussed under various decision-making scenarios. Several insights are drawn from this research work and will certainly benefit the decision-makers to overcome the challenges in building decisions.

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Review of Composite Machining and Related Optimization Techniques

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.813-814.398 ◽

2015 ◽

Vol 813-814 ◽

pp. 398-403

Author(s):

M.M. Thamizharasan ◽

Y.J. Nithiya Sandhiya ◽

K.S. Vijay Sekar

Keyword(s):

Composite Materials ◽

Taguchi Method ◽

Weight Ratio ◽

Optimization Techniques ◽

Machining Process ◽

Machining Processes ◽

Surface Method ◽

Simulated Algorithm ◽

Modeling Techniques ◽

Experimental Trials

This paper provides an inclusive review of literature, mostly from the past decade, on optimization techniques of composite materials machining, both conventional and non-conventional process. Composite materials are continually replacing conventional materials due to their excellent corrosion resistance, higher strength to weight ratio, but the machining of composites is a challenging process. Experimental trials notwithstanding, researchers have also used various optimization techniques such as Taguchi method, Genetic Algorithm, Simulated Algorithm, Response Surface Method, and Fuzzy Logic with ANOVA etc., to identify the optimal parameters for the machining processes. Also predictive modeling techniques such as Artificial Neural Networks and Finite Element Methods have also been employed as an optimization tools for studying the composite machining process. It was found that Taguchi method is the most preferred technique in the optimization studies.

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Process Evaluation and Numerical Optimization in Friction Stir Welding of Dissimilar AMCs

10.4018/978-1-7998-7864-3.ch014 ◽

2022 ◽

pp. 311-338

Author(s):

Rajesh P. V. ◽

Saravanan A.

Keyword(s):

Friction Stir Welding ◽

Low Cost ◽

Research Work ◽

Weight Ratio ◽

High Strength ◽

Matrix Composites ◽

Friction Stir ◽

Multi Attribute Decision Making ◽

Solid State Joining ◽

Al 2024

In recent times, any engineering material is deemed worthwhile only if it satisfies functional characteristics such as weldability, formability, machinability, etc. Aluminum-based metal matrix composites have extensive usage in modern automobile parts, aircraft components, and ship structures, mainly due to their attractive properties such as low cost, high strength-to-weight ratio, excellent corrosion and wear resistance. Friction stir welding is one of the most versatile solid-state joining processes to ensure weldability between two AMC plates. In this research work, an analysis of FSW process through parameters (e.g., composition of alumina, spindle speed, feed, etc.) in joining Alumina reinforced aluminum alloy composites Al 6061 and Al 2024 together at various proportions by analyzing properties like impact strength, hardness, flatness, and ultimate tensile strength has been done. Finally, optimization is carried out to select the best possible combination using a multi-attribute decision-making technique called the complex proportional assessment of alternatives.

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Optimization of machining parameters under MQL turning of Ti-6Al-4V alloy with textured tool using multi-attribute decision-making methods

World Journal of Engineering ◽

10.1108/wje-06-2019-0170 ◽

2019 ◽

Vol 16 (5) ◽

pp. 648-659 ◽

Cited By ~ 4

Author(s):

Rupinder Singh ◽

Jasminder Singh Dureja ◽

Manu Dogra ◽

Jugraj Singh Randhawa

Keyword(s):

Decision Making ◽

Desirability Function ◽

Cutting Tools ◽

Cutting Temperature ◽

Cutting Speed ◽

Minimum Quantity Lubrication ◽

Depth Of Cut ◽

Machining Parameters ◽

Content Type ◽

Multi Attribute Decision Making

Purpose This paper aims to focus on the application of multi-attribute decision-making methods (MADMs) to ascertain the optimal machining parameters while turning Ti-6Al-4V alloy under minimum quantity lubrication (MQL) conditions using Jatropha-curcas oil (JCO) bio-based lubricant. Design/methodology/approach The experiments were designed and performed using Taguchi L27 design of experiments methodology. A total of 27 experiments were performed under MQL conditions using textured carbide cutting tools on which different MADMs like Analytic hierarchy process (AHP), Technique for order preference by similarity to ideal solution (TOPSIS) and Simple additive weighting (SAW) were implemented in an empirical manner to extract optimize machining parameters for turning of Ti-6Al-4V alloy under set of constrained conditions. Findings The results evaluated through MADMs exhibit the optimized set of machining parameters (cutting speed Vc = 80 m/min, feed rate f = 0.05 mm/rev. and depth of cut ap = 0.10 mm) for minimizing the average surface roughness (Ra), maximum flank wear (Vbmax), tangential cutting force (Fc) and cutting temperature (T). Further, analysis of variance (ANOVA) and traditional desirability function approach was applied and results of TOPSIS and SAW methods having optimal setting of parameters were compared as well as confirmation experiments were conducted to verify the results. A SEM analysis at lowest and highest cutting speeds was performed to investigate the tool wear patterns. At the highest speed, large cutting temperature generated, thereby resulted in chipping as well as notching and fracturing of the textured insert. Originality/value The research paper attempted in exploring the optimized machining parameters during turning of difficult-to-cut titanium alloy (Ti-6AL-4V) with textured carbide cutting tool under MQL environment through combined approach of MADMs techniques. Ti-6Al-4V alloy has been extensively used in important aerospace components like fuselage, hydraulic tubing, bulk head, wing spar, landing gear, as well as bio-medical applications.

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Process Parameters Optimization by using the Regression Method in Micro Abrasive Air Jet Machining on Alumina Reinforced Zerconia Composite Materials

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9778.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 4582-4589

Keyword(s):

Surface Roughness ◽

Composite Materials ◽

Process Parameters ◽

Ceramic Composite ◽

Research Work ◽

Parameters Optimization ◽

Interaction Parameters ◽

Machining Parameters ◽

Air Jet ◽

Conventional Machining

It is very difficult to make a hole in brittle materials like glass and ceramic materials by using conventional machining methods like turning and milling therefore non conventional machining such as micro abrasive air jet machine is used to overcome the above problem. In this research work to prepare alumina reinforced zerconia ceramic composite materials using powder metallurgy sintering method experiments have been conducted on micro abrasive air jet erosion tester. In this work to varied abrasive air jet machining parameters i.e. Pressure, Abrasive flow rate, Standoff distance and different Weight percentage of zirconium added into alumina i.e. 5wt%, 10wt% and 15wt% and responses are Material Removing Rate and Surface Roughness. 30µm size of Silicon carbide (sic) sand particles are impinged Ceramic composite plates with given input process parameters. L27 Orthogonal array of Taguchi and Regression analysis is used to determine the Signal to Noise ratios of all experiments and process parameters impact, Percentage contribution of each process parameters, square parameters and interaction parameters on MRR and Surface Roughness and check weather parameters, square and interaction parameters are significant are not, to eliminate insignificant parameters by using backward elimination method. To improve R2 value by eliminated insignificant parameters.

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Feature based building orientation optimization for additive manufacturing

Rapid Prototyping Journal ◽

10.1108/rpj-03-2014-0037 ◽

2016 ◽

Vol 22 (2) ◽

pp. 358-376 ◽

Cited By ~ 29

Author(s):

Yicha Zhang ◽

Alain Bernard ◽

Ravi Kumar Gupta ◽

Ramy Harik

Keyword(s):

Decision Making ◽

Additive Manufacturing ◽

High Performance ◽

Research Work ◽

Geometric Features ◽

Content Type ◽

Build Orientation ◽

Multi Attribute Decision Making ◽

Production Knowledge ◽

Short Time

Purpose The purpose of this paper is to present research work based on the authors’ conceptual framework reported in the VRAP Conference 2013. It is related with an efficient method to obtain an optimal part build orientation for additive manufacturing (AM) by using AM features with associated AM production knowledge and multi-attribute decision-making (MADM). The paper also emphasizes the importance of AM feature and the implied AM knowledge in AM process planning. Design/methodology/approach To solve the orientation problem in AM, two sub-tasks, the generation of a set of alternative orientations and the identification of an optimal one within the generated list, should be accomplished. In this paper, AM feature is defined and associated with AM production knowledge to be used for generating a set of alternative orientations. Key attributes for the decision-making of the orientation problem are then identified and used to represent those generated orientations. Finally, an integrated MADM model is adopted to find out the optimal orientation among the generated alternative orientations. Findings The proposed method to find out an optimal part build orientation for those parts with simple or medium complex geometric shapes is reasonable and efficient. It also has the potential to deal with more complex parts with cellular or porous structures in a short time by using high-performance computers. Research limitations/implications The proposed method is a proof-of-concept. There is a need to investigate AM feature types and the association with related AM production knowledge further so as to suite the context of orientating parts with more complex geometric features. There are also research opportunities for developing more advanced algorithms to recognize AM features and generate alternative orientations and refine alternative orientations. Originality/value AM feature is defined and introduced to the orientation problem in AM for generating the alternative orientations. It is also used as one of the key attributes for decision-making so as to help express production requirements on specific geometric features of a desired part.

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