Implementing multi criteria decision making methods for computing complexity involved in industrial investment castings

Nikunj  Maheta; Amit  Sata

doi:10.59400/mea1962

Implementing multi criteria decision making methods for computing complexity involved in industrial investment castings

Nikunj Maheta Department of Mechanical Engineering, Marwadi University, Rajkot 360003, India
Amit Sata Department of Mechanical Engineering, Marwadi University, Rajkot 360003, India

Article ID: 1962

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DOI: https://doi.org/10.59400/mea1962

Keywords: investment casting; complexity index; multi-criteria decision-making; weighted criteria approach; analytical hierarchy process

Abstract

Investment casting is admired for its ability to produce industrial castings with remarkable precision, exceptional exterior finish and complex designs among diverse industrial application. Traditionally, the complexity of these castings is generally assessed qualitatively while quantitative measurement of this complexity remains largely unexplored. To identify the various parameters that affects the complexity of industrial investment casting, an in-person industrial survey was carried out in one of the major investment casting clusters that accounts for nearly 25% of India’s investment casting foundries. Through this survey it was found that complexity of investment casting is determined by three factors related to geometry, features and manufacturability. These three factors are further driven by 19 elements and 52 attributes. These 52 attributes are further characterised by 212 meta-attributes. This research focuses on applying multi-criteria decision-making methods to quantify the complexity affects in manufacturing industrial investment castings. Numerous methodologies within the domain of Multi-Criteria Decision-Making (MCDM) have been explored to determine the appropriate weightage for the factors, elements, attributes and meta-attributes involved. It was observed that for the specific problem mentioned, the Weighted Criteria Approach (WCA) and Analytical Hierarchy Process (AHP) were identified as suitable choices, aligning well with the required level of accuracy. The result obtained through these methods were used to compute the complexity of industrial castings. The proposed complexity index was validated using various industrial castings and proved to be a valuable tool for designers in adopting investment casting process for producing complex castings.

References

[1]Kerbrat O, Mognol P, Hascoet JY. Manufacturing complexity evaluation at the design stage for both machining and layered manufacturing. CIRP Journal of Manufacturing Science and Technology. 2010; 2(3): 208-215. doi: 10.1016/j.cirpj.2010.03.007

[2]Merkt S, Hinke C, Schleifenbaum H, et al. Geometric complexity analysis in an integrative technology evaluation model (item) for selective laser melting (SLM)#. The South African Journal of Industrial Engineering. 2011; 23(2). doi: 10.7166/23-2-333

[3]Conner BP, Manogharan GP, Martof AN, et al. Making sense of 3-D printing: Creating a map of additive manufacturing products and services. Additive Manufacturing. 2014; 1–4: 64-76. doi: 10.1016/j.addma.2014.08.005

[4]Hosseini-Ara R, Yavari P. A new criterion for preform design of H-shaped hot die forging based on shape complexity factor. International Journal of Material Forming. 2017; 11(2): 233-238. doi: 10.1007/s12289-017-1345-8

[5]Pradel P, Bibb R, Zhu Z et al. Exploring the Impact of Shape Complexity on Build Time for Material Extrusion and Material Jetting. Springer International Publishing; 2018. pp. 24-33.

[6]Qamar SZ, Chekotu JC, Al-Maharbi M, et al. Shape Complexity in Metal Extrusion: Definitions, Classification, and Applications. Arabian Journal for Science and Engineering. 2019; 44(9): 7371-7384. doi: 10.1007/s13369-019-03886-8

[7]Qamar SZ, Chekotu JC, Qamar SB. Effect of Shape Complexity on Ram Pressure and Metal Flow in Aluminum Extrusion. JOM. 2019; 71(12): 4378-4392. doi: 10.1007/s11837-019-03748-6

[8]Joshi D, Ravi B. Quantifying the Shape Complexity of Cast Parts. Computer-Aided Design and Applications. 2010; 7(5): 685-700. doi: 10.3722/cadaps.2010.685-700

[9]Martof A, Gullapalli R, Kelly J, et al. Economies of complexity of 3D printed sand molds for casting. ResearchGate; 2018. pp. 117-134.

[10]Zavadskas EK, Turskis Z, Kildiene S. State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy. 2014; 20(1): 165-179. doi: 10.3846/20294913.2014.892037

[11]Toloie-eshlaghy A. MCDM Methodologies and Applications: A Literature Review from 1999 to 2009. Research Journal of Internatıonal Studıes. 2011; 21(21): 86-137.

[12]Saaty TL. How to make a decision: The analytic hierarchy process. Eur J Oper Res. 1990; 48(1): 9-26. doi: 10.1016/0377-2217(90)90057-I

[13]Pal DK, Ravi B, Bhargava LS. Rapid tooling route selection for metal casting using QFD–ANP methodology. International Journal of Computer Integrated Manufacturing. 2007; 20(4): 338-354. doi: 10.1080/09511920600883229

[14]Singh C, Singh D, Khamba JS. Analyzing barriers of Green Lean practices in manufacturing industries by DEMATEL approach. Journal of Manufacturing Technology Management. 2020; 32(1): 176-198. doi: 10.1108/jmtm-02-2020-0053

[15]Che Hassan MF, Mohd Rosli MU, Mohd Redzuan MA. Material selection in a sustainable manufacturing practice of a badminton racket frame using Elimination and Choice Expressing Reality (ELECTRE) Method. Journal of Physics: Conference Series. 2018; 1020(1): 012012. doi: 10.1088/1742-6596/1020/1/012012

[16]Athawale VM, Chatterjee P, Chakraborty S. Decision making for facility location selection using PROMETHEE II method. International Journal of Industrial and Systems Engineering. 2012; 11(1/2): 16. doi: 10.1504/ijise.2012.046652

[17]Chodha V, Dubey R, Kumar R, et al. Selection of industrial arc welding robot with TOPSIS and Entropy MCDM techniques. Materials Today: Proceedings. 2022; 50: 709-715. doi: 10.1016/j.matpr.2021.04.487

[18]Ibrahim A, Surya RA. The Implementation of Simple Additive Weighting (SAW) Method in Decision Support System for the Best School Selection in Jambi. Journal of Physics: Conference Series. 2019; 1338(1): 012054. doi: 10.1088/1742-6596/1338/1/012054

[19]Dev S, Aherwar A, Patnaik A. Material Selection for Automotive Piston Component Using Entropy-VIKOR Method. Silicon. 2019; 12(1): 155-169. doi: 10.1007/s12633-019-00110-y

[20]Durán O, Aguilo J. Computer-aided machine-tool selection based on a Fuzzy-AHP approach. Expert Systems with Applications. 2008; 34(3): 1787-1794. doi: 10.1016/j.eswa.2007.01.046

[21]Pelissari R, Khan SA, Ben-Amor S. Application of Multi-Criteria Decision-Making Methods in Sustainable Manufacturing Management: A Systematic Literature Review and Analysis of the Prospects. International Journal of Information Technology & Decision Making. 2021; 21(02): 493-515. doi: 10.1142/s0219622021300020

[22]Karthik S, Chung CW, Ramani K, et al. Methodology for Metalcasting Process Selection. SAE Technical Paper Series; 2003.

[23]Mustafa A, Abdollah MFB, Shuhimi FF, et al. Selection and verification of kenaf fibres as an alternative friction material using Weighted Decision Matrix method. Materials & Design. 2015; 67: 577-582. doi: 10.1016/j.matdes.2014.10.091

[24]Anjum Mohd, Haque MdA, Ahmad N. Analysis and Ranking of Software Reliability Models Based on Weighted Criteria Value. International Journal of Information Technology and Computer Science. 2013; 5(2): 1-14. doi: 10.5815/ijitcs.2013.02.01

[25]Akarte MM, RaviB. Casting Process Selection using AHP and Fuzzy Logic. International Seminar on Manufacturing Technology Beyond. 2000; 1: 1-10.

[26]Chougule RG, Ravi B. Variant process planning of castings using AHP-based nearest neighbour algorithm for case retrieval. International Journal of Production Research. 2005; 43(6): 1255-1273. doi: 10.1080/00207540412331320517

[27]Joshi D, Ravi B. Early castability evaluation using analytical hierarchy process. The International Journal of Advanced Manufacturing Technology. 2010; 50(1-4): 21-36. doi: 10.1007/s00170-010-2517-6

[28]Sata AV, Maheta NR. 5 Cs of Investment Casting Foundries in Rajkot Cluster – An Industrial Survey. Archives of Foundry Engineering. Published online September 8, 2021: 102-108. doi: 10.24425/afe.2021.138672

[29]Maheta N, Sata DA. Systematic Development of Cumulative Complexity Index for Investment Casting. Journal of Advanced Manufacturing Systems. 2022; 22(02): 323-338. doi: 10.1142/s0219686723500166

[30]Maheta N, Sata A. Development of a Novel Complexity Index for Investment Casting. International Journal of Metalcasting. 2023; 18(3): 2165-2180. doi: 10.1007/s40962-023-01151-1

Published

2024-12-18

How to Cite

Maheta, N., & Sata, A. (2024). Implementing multi criteria decision making methods for computing complexity involved in industrial investment castings. Mechanical Engineering Advances, 3(1), 1962. https://doi.org/10.59400/mea1962

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Vol. 3 No. 1 (2025)

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Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

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