Comparative Evaluation of Rapid Prototyping Processes for Product Development Using Optimization Techniques

International Journal of Mechanical Engineering |
© 2025 by SSRG - IJME Journal |
Volume 12 Issue 8 |
Year of Publication : 2025 |
Authors : V E Kothawade, V P Wani, H A Chavan, S R Suryawanshi |
How to Cite?
V E Kothawade, V P Wani, H A Chavan, S R Suryawanshi, "Comparative Evaluation of Rapid Prototyping Processes for Product Development Using Optimization Techniques," SSRG International Journal of Mechanical Engineering, vol. 12, no. 8, pp. 61-69, 2025. Crossref, https://doi.org/10.14445/23488360/IJME-V12I8P107
Abstract:
Creating innovative products within stringent timeframes presents a significant hurdle for small-scale manufacturers, highlighting the necessity for innovative solutions. To develop products effectively, it is essential to have a deep understanding of the complex interplay between product features, manufacturing processes, and market requirements. In this context, a technique known as Rapid Prototyping (RP) has emerged as a promising solution, but its successful implementation relies on specialized expertise and the ability to navigate intricate variables. For example, the design and optimization of water sprinklers offer opportunities for innovation in both commercial and agricultural contexts, with key factors such as water pressure and coverage playing a crucial role. Regular testing of prototypes ensures the efficacy of these systems, making RP the preferred method for prototyping. However, the implementation of RP is often hindered by a lack of skilled expertise and the complexity of process parameters. This research conducts a comparative evaluation of RP methods, using a decision-making approach that considers multiple criteria to assess product attributes such as surface finish, production duration, accuracy, strength, and cost. This MCDM approach enables ranking of available methods. The fundamental objective of the mentioned study is to calculate the most suitable RP method for manufacturing prototypes of sprinkler system components, enabling small scale manufacturers to develop innovative products efficiently and effectively.
Keywords:
MCDM techniques, Product attributes, Prototype, Ranking of RP method.
References:
[1] Ian Gibson, David Rosen, and Brent Stucker, Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing, 2nd ed., Springer, 2015.
[Google Scholar]
[2] R. Venkata Rao, and K.K. Padmanabhan, “Rapid Prototyping Process Selection Using Graph Theory And Matrix Approach,” Journal of Materials Processing Technology, vol. 194, no. 1-3, pp. 81-88, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Syed H. Masood, and Mazen Al-Alawi, “The IRIS Rapid Prototyping System Selector for Educational and Manufacturing Users,” International Journal of Engineering Education, vol. 18, no. 1, pp. 66-77, 2002.
[Google Scholar] [Publisher Link]
[4] Ahmed M. Romouzy-Ali et al., “Adopting Rapid Prototyping Technology within Small and Medium-Sized Enterprises: The Differences between Reality and Expectation,” International Journal of Innovation, Management and Technology, vol. 3, no. 4, pp. 427-432, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Insaf Bahnini et al., “Additive Manufacturing Technology: The Status, Applications, and Prospects,” The International Journal of Advanced Manufacturing Technology, vol. 97, no. 1-4, pp. 147-161, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Cauê G. Mançanares et al., “Additive Manufacturing Process Selection Based on Parts' Selection Criteria,” The International Journal of Advanced Manufacturing Technology, vol. 80, no. 5-8, pp. 1007-1014, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Hongbo Lan, “Web-Based Rapid Prototyping and Manufacturing Systems: A Review,” Computers in Industry, vol. 60, no. 9, pp. 643-656, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Peng Wang et al., “A Hybrid Method Using Experiment Design and Grey Relational Analysis for Multiple Criteria Decision-Making Problems,” Knowledge-Based Systems, vol. 53, pp. 100-107, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Prasad Karande, and Shankar Chakraborty, “Application of Multi-Objective Optimization based on Ratio Analysis (MOORA) Method for Materials Selection,” Materials & Design, vol. 37, pp. 317-324, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Prasenjit Chatterjee, and Shankar Chakraborty, “Advanced Manufacturing Systems Selection Using ORESTE Method,” International Journal of Advanced Operations Management, vol. 5, no. 4, pp. 337-361, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Thomas L. Saaty, “Decision Making with the Analytic Hierarchy Process,” International Journal of Services Sciences, vol. 1, no. 1, pp. 83-98, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Fikri Dweiri et al., “Designing an Integrated AHP-Based Decision Support System for Supplier Selection in the Automotive Industry,” Expert Systems with Applications, vol. 62, pp. 273-283, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Mohammad Kazem Sayadi, Majeed Heydari, and Kamran Shahanaghi, “Extension of VIKOR Method for Decision Making Problem with Interval Numbers,” Advances in Environmental Biology, vol. 33, no. 5, pp. 2257-2262, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Sigfrid-Laurin Sindinger et al., “Thickness Dependent Anisotropy of Mechanical Properties and Inhomogeneous Porosity Characteristics in Laser-Sintered Polyamide 12 Specimens,” Additive Manufacturing, vol. 33, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] K. Chockalingam et al., “Optimization of Stereolithography Process Parameters for Part Strength Using Design of Experiments,” The International Journal of Advanced Manufacturing Technology, vol. 29, pp. 79-88, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Leonhard Hitzler et al., “Thermal Expansion and Temperature-Dependent Young’s Modulus of Invar Fabricated via Laser Powder-Bed Fusion,” Progress in Additive Manufacturing, vol. 7, pp. 463-470, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Omar Ahmed Mohamed et al., “Effect of Process Parameters on Dynamic Mechanical Performance of FDM PC/ABS Printed Parts Through Design of Experiment,” Journal of Materials Engineering and Performance, vol. 25, pp. 2922-2935, 2016. [CrossRef] [Google Scholar] [Publisher Link]
[18] Ashish Dwivedi et al., “Selection of Sustainable Materials for Additive Manufacturing Processes: A Hybrid AHP-DEMATEL Approach,” International Journal of Industrial and Systems Engineering, vol. 48, no. 4, pp. 531-555, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Mukesh Chandra et al., “Selection for Additive Manufacturing using Hybrid MCDM Technique Considering Sustainable Concepts,” Rapid Prototyping Journal, vol. 28, no. 7, pp. 1297-1311, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] R. Venkata Rao, Decision Making in Manufacturing Environment Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods, 1st ed., Springer London, pp. 1-294, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Gülçin Büyüközkan, and Da Ruan, “Evaluation of Software Development Projects Using A Fuzzy Multi-Criteria Decision Approach,” Mathematics and Computers in Simulation, vol. 77, no. 5-6, pp. 464-475, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Sandile Thamie Mhlanga, and Manoj Lall, “Influence of Normalization Techniques on Multi-criteria Decision-making Methods,” Journal of Physics: Conference Series, vol. 2224, pp. 1-13, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Lee-Ing Tong, Chi-Chan Chen, and Chung-Ho Wang, “Optimization of Multi-Response Processes using the VIKOR Method,” The International Journal of Advanced Manufacturing Technology, vol. 31, pp. 1049-1057, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[24] S.R. Gangurde, and M.M. Akarte, “Ranking of Product Alternatives Based on Customer-Designer Preferences,” IEEE International Conference on Industrial Engineering and Engineering Management, Macao, China, pp. 1334-1338, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[25] S.M. Vadivel, and A.H. Sequeira, “A Hybrid Method for the Selection of Facility Layout Using Experimental Design and Grey Relational Analysis: A Case Study,” International Journal of Hybrid Intelligent Systems, vol. 15, no. 2, pp. 101-110, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Vesile Sinem Arıkan Kargı, and Fatma Cesur, “Renewable Energy Technology Selection for Hotel Buildings: A Systematic Approach Based on AHP and VIKOR Methods,” Buildings, vol. 14, no. 9, pp. 1-32, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Yann Collette, and Patrick Siarry, Multiobjective Optimization: Principles and Case Studies, 1st ed., Springer, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Martin Peterson, An Introduction to Decision Theory, Cambridge Introductions to Philosophy, 2nd ed., 2017.
[Google Scholar] [Publisher Link]
[29] Mukesh Chandra et al., “Selection for Additive Manufacturing using Hybrid MCDM Technique Considering Sustainable Concepts,” Rapid Prototyping Journal, vol. 28, no. 7, pp. 1297-1311, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Vimal KEK et al., “Rapid Prototyping Process Selection using Multi Criteria Decision Making Considering Environmental Criteria and its Decision Support System,” Rapid Prototyping Journal, vol. 22, no. 2, pp. 225-250, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Khalil Mustafa Abdulkarem Algunaid, and Jichang Liu, “Decision Support System to Select a 3D Printing Process/Machine and Material from a Large-Scale Options Pool,” The International Journal of Advanced Manufacturing Technology, vol. 121, pp. 7643-7659, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Diqian Ren, Jun-Ki Choi, and Kellie Schneider, “A Multicriteria Decision-Making Method for Additive Manufacturing Process Selection,” Rapid Prototyping Journal, vol. 28, no. 11, pp. 77-91, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Akin Menekse et al., “Additive Manufacturing Process Selection for Automotive Industry using Pythagorean Fuzzy CRITIC EDAS,” PLoS ONE, vol. 18, no. 3, pp. 1-23, 2023.
[CrossRef] [Google Scholar] [Publisher Link]