Skip to main content
SpringerLink
Log in

Comparative analysis of different digitization systems and selection of best alternative

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Manufacturing industry plays a very significant role in the economic functioning of any country. In recent times, reverse engineering (RE) has become an integral part of manufacturing set-up owing to its numerous applications. The quality of RE product primarily depends on the quality of digitization i.e., part measurement. There is a diverse range of digitization devices which can be employed in RE. These machines have variability in terms of cost, accuracy, ease of use, accessibility, scanning time, etc. Therefore, the decision regarding the selection of a suitable device becomes important in a particular RE application. The decisions taken in the planning stage for RE can have a long lasting impact on the functionality, quality and the economics of components to be used by manufacturing industries. To accomplish the selection procedure, a comparative study of three digitization techniques has been carried out. The determination of an appropriate digitization system is basically a multi-criteria decision making (MCDM) problem. MCDM techniques are yet to be applied in the selection of digitization systems for RE. MCDM is one of the most widely used decision methodologies in business and engineering spheres. The aim of this work is to describe various MCDM methods in the selection of digitization systems for RE. This paper intends to employ combinations between different MCDM methods such as group eigenvalue method (GEM), analytic hierarchy process (AHP), entropy method, elimination and choice expressing reality (ELECTRE), technique for order of preference by similarity to ideal solution (TOPSIS) and simple additive weighing (SAW) method. In this work, GEM, AHP, Entropy methods has been used to elicit weights of various selection criteria, while TOPSIS, ELECTRE and SAW have been applied to rank the alternatives. A comparative analysis has also been performed to determine the efficacies of different approaches. The conclusion of the paper reveals the best digitization system as well as the characteristics of different MCDM methods and their suitability in RE application.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Abedi, T., & Ghamgosar, M. (2013). Formulating forest management strategies using ELECTRE method. World Applied Programming, 3, 522–528.

    Google Scholar 

  • Adriyendi, A. (2015). Multi-attribute decision making using simple additive weighting and weighted product in food choice. International Journal of Information Engineering and Electronic Business, 6, 8–14.

    Google Scholar 

  • Afshari, A., Mojahed, M., & Yusuff, R. M. (2010). Simple additive weighting approach to personnel selection problem. International Journal of Innovation, Management and Technology, 1, 511–515.

    Google Scholar 

  • Ali, F., Chowdary, B. V., & Imbert, C. A. C. (2008). Part design and evaluation through reverse engineering approach. In Proceedings of the IAJC-IJME international conference, Nashville, TN, USA.

  • Al-Najjar, B., & Alsyouf, I. (2003). Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making. International Journal of Production Economics, 84, 85–100.

    Article  Google Scholar 

  • Angiz, L. M. Z., Mustafa, A., Ghani, N. A., & Kamil, A. A. (2012). Group decision via usage of analytic hierarchy process and preference aggregation method. Sains Malaysiana, 41, 361–366.

    Google Scholar 

  • Aruldoss, M., Lakshmi, T. M., & Venkatesan, V. P. (2013). A survey on multi criteria decision making methods and its applications. American Journal of Information Systems, 1, 31–43.

    Google Scholar 

  • Baker, D., Bridges, D., Hunter, R., Johnson, G., Krupa, J., Murphy, J., et al. (2001). Guidebook to decision-making methods. New York: McGraw Hill Inc.

    Google Scholar 

  • Balaji, C. M., Gurumurthy, A., & Kodali, R. (2009). Selection of a machine tool for FMS using ELECTRE III—A case study. In Proceedings of conference on automation science and engineering, Bangalore, India.

  • Banwet, D. K., & Majumdar, A. (2014). Comparative analysis of AHP–TOPSIS and GA–TOPSIS methods for selection of raw materials in textile industries. In Proceedings of the 2014 international conference on industrial engineering and operations management, Bali, Indonesia.

  • Barbero, B. R., & Ureta, E. S. (2011). Comparative study of different digitization techniques and their accuracy. Computer-Aided Design, 43(2), 188–206.

    Article  Google Scholar 

  • Barisic, B., Rucki, M., & Car, Z. (2008). Analysis of digitizing and traditional measuring system at surface measurement of lids. Key Engineering Materials, 381–382, 217–220.

    Article  Google Scholar 

  • Benayoun, R., Roy, B., & Sussman, N. (1966). Manual de reference du programme electre, Note de Synthese et Formation. Direction Scientifique SEMA, Paris, Franch.

  • Bentes, A., Carneiro, J., Silva, J., & Kimura, H. (2012). Multidimensional assessment of organizational performance: Integrating BSC and AHP. Journal of Business Research, 65, 1790–1799.

    Article  Google Scholar 

  • Çimren, E., Çatay, B., & Budak, E. (2007). Development of a machine tool selection system using AHP. The International Journal of Advanced Manufacturing Technology, 35, 363–376.

    Article  Google Scholar 

  • Cook, M., & Kress, A. (1990). A data envelopment model for aggregating preference rankings. Management Science, 36, 1302–1310.

    Article  Google Scholar 

  • De Chiffre, L., Hansen, H. N., & Morace, R. E. (2005). Comparison of coordinate measuring machines using an optomechanical hole plate. CIRP Annals - Manufacturing Technology, 54(1), 479–482.

    Article  Google Scholar 

  • Fazlollahtabar, H., & Yousefpoor, N. (2008). Selection of optimum maintenance strategies in a virtual learning environment based on analytic hierarchy process. In Proceedings of international conference on virtual learning, Constanţa, Romania.

  • Feng, H. Y., Liu, Y., & Xi, F. (2001). Analysis of digitizing errors of a laser scanning system. Journal of the International Societies for Precision Engineering and Nanotechnology, 25, 185–191.

    Google Scholar 

  • Forman, E., & Peniwati, K. (1998). Aggregating individual judgments and priorities with the analytic hierarchy process. European Journal Of Operational Research, 108, 165–169.

    Article  Google Scholar 

  • Fulop, J. (2005). Introduction to decision making methods. Budapest: Laboratory of Operations Research and Decision Systems, Computer and Automation Institute, Hungarian Academy of Sciences.

    Google Scholar 

  • Gapinski, B., Zachwiej, I., & Kołodziej, A. (2014). Comparison of different method of measurement geometry using CMM. Procedia Engineering, 69, 255–262.

    Article  Google Scholar 

  • Gestel, N. V., Cuypers, S., Bleys, P., & Kruth, J. P. (2009). A performance evaluation test for laser line scanners on CMMs. Optics and Lasers in Engineering, 47, 336–342.

    Article  Google Scholar 

  • Grandzol, J. R. (2005). Improving the faculty selection process in higher education: A case for the analytic hierarchy process. IR Applications - Using Advanced Tools, Techniques, and Methodologies, 6, 1–13.

    Google Scholar 

  • Habibi, A., Sarafrazi, A., & Izadyar, S. (2014). Delphi technique theoretical framework in qualitative research. The International Journal of Engineering and Science (IJES), 3, 8–13.

    Google Scholar 

  • Hansen, H. N., & De Chiffre, L. (1999). An industrial comparison of coordinate measuring machines in Scandinavia with focus on uncertainty statements. Precision Engineering, 23, 185–195.

    Article  Google Scholar 

  • Harvie, A. (1986). Factors affecting component measurement on coordinate measuring machines. Precision Engineering, 8, 13–18.

    Article  Google Scholar 

  • Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. New York: Springer.

    Book  Google Scholar 

  • Ilangkumaran, M., & Kumanan, S. (2009). Selection of maintenance policy for textile industry using hybrid multi-criteria decision making approach. Journal of Manufacturing Technology Management, 20, 1009–1022.

    Article  Google Scholar 

  • Ishizaka, A., & Labib, A. (2009). Analytic hierarchy process and expert choice: Benefits and limitations. OR Insight, 22, 201–220.

    Article  Google Scholar 

  • Jafari, A., Jafarian, M., Zareei, A., & Zaerpour, F. (2008). Using fuzzy Delphi method in maintenance strategy selection problem. Journal of Uncertain Systems, 2, 289–298.

    Google Scholar 

  • Krohlinga, R. A., & Pacheco, A. G. C. (2015). A-TOPSIS—An approach based on TOPSIS for ranking evolutionary algorithms. Procedia Computer Science, 15, 308–317.

    Article  Google Scholar 

  • Li, X., Wang, K., Liu, L., Xin, J., Yang, H., & Gao, C. (2011). Application of the entropy weight and TOPSIS method in safety evaluation of coal mines. Procedia Engineering, 26, 2085–2091.

    Article  Google Scholar 

  • Lotfi, F. H., & Fallahnejad, R. (2010). Imprecise Shannon’s entropy and multi attribute decision making. Entropy, 12, 53–62.

    Article  Google Scholar 

  • MacCrimmon, K. R. (1968). Decision-making among multiple-attribute alternatives: A survey and consolidated approach. Memorandum RM-4823-ARPA, The Rand Corporation, Santa Monica, California.

  • Martínez, S., Cuesta, E., Barreiro, J., & Álvarez, B. (2010). Methodology for comparison of laser digitizing versus contact systems in dimensional control. Optics and Lasers in Engineering, 48, 1238–1246.

    Article  Google Scholar 

  • Mavromihales, M., Mason, J., & Weston, W. (2003). A case of reverse engineering for the manufacture of wide chord fan blades (WCFB) used in Rolls Royce aero engines. Journal of Materials Processing Technology, 134, 279–286.

    Article  Google Scholar 

  • Michalos, G., Fysikopoulos, A., Makris, S., Mourtzis, D., & Chryssolouris, G. (2015). Multi criteria assembly line design and configuration—An automotive case study. CIRP Journal of Manufacturing Science and Technology, 9, 69–87.

    Article  Google Scholar 

  • Milani, A. S., Shanian, A., & EL-Lahham, C. (2006). Using different ELECTRE methods in strategic planning in the presence of human behavioral resistance. Journal of Applied Mathematics and Decision Sciences. Article ID 10936, 1–19.

  • Mojahed, M., Marjani, M. E., Afshari, A., & Marjani, S. (2013). Using ELECTRE-AHP as a mixed method for personnel selection. In Proceedings of the 12th international symposium on the analytic hierarchy process for multicriteria decision making, Kuala Lumpur, Malaysia.

  • Motavalli, S. (1998). Review of reverse engineering approaches. Computers & Industrial Engineering, 35, 25–28.

    Article  Google Scholar 

  • Mousavi, S. S., Nezami, F. G., Heydar, M., & Aryanejad, M. B. (2011). A hybrid fuzzy group decision making and factor analysis for selecting maintenance strategy. In Proceeding of international conference on computers & industrial engineering, Troyes, France.

  • Osman, M. S. A., Abd El_Hakim, G. E. A., & Khalifa, H. A. (2016). On a hybrid approach for treating multi-criteria decision making problems. International Journal of Computer Applications, 145, 49–57.

    Google Scholar 

  • Ossadnik, W., Schinke, S., & Kaspar, R. H. (2016). Group aggregation techniques for analytic hierarchy process and analytic network process: A comparative analysis. Group Decision and Negotiation, 25, 421–457.

    Article  Google Scholar 

  • Ozcan, T., & Celebi, N. (2011). Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem. Expert Systems with Applications, 38, 9773–9779.

    Article  Google Scholar 

  • Pang, J., Zhang, G., & Chen, G. (2011). ELECTRE I decision model of reliability design scheme for computer numerical control machine. Journal of Software, 6, 894–900.

    Google Scholar 

  • Papakostas, N., Mourtzis, D., Michalos, G., Makris, S., & Chryssolouris, G. (2012). An agent-based methodology for manufacturing decision making: A textile case study. International Journal of Computer Integrated Manufacturing, 25, 509–526.

    Article  Google Scholar 

  • Pourjavad, E., & Shirouyehzad, H. (2011). A MCDM approach for prioritizing production lines: A case study. International Journal of Business and Management, 6, 221–229.

    Google Scholar 

  • Qiu, W. H. (1997). Group eigenvalue method. Applied Mathematics and Mechanics, 18, 1027–1031.

    Google Scholar 

  • Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.

    Google Scholar 

  • Sadeghzadeh, K., & Salehi, M. B. (2011). Mathematical analysis of fuel cell strategic technologies development solutions in the automotive industry by the TOPSIS multi-criteria decision making method. International Journal of Hydrogen Energy, 36, 13272–13280.

    Article  Google Scholar 

  • Salomon, V. A. P., & Montevechi, J. A. B. (2001). Compilation of comparisons on the Analytic Hierarchy Process and others multiple criteria decision making methods: Some cases developed in Brazil. Berne: ISAHP.

    Google Scholar 

  • Sansoni, G., Trebeschi, M., & Docchio, F. (2009). State-of-the-art and applications of 3D imaging sensors in industry, cultural heritage, medicine, and criminal investigation. Sensors, 9, 568–601.

    Article  Google Scholar 

  • Savio, S. (2006). Uncertainty in testing the metrological performances of coordinate measuring machines. CIRP Annals - Manufacturing Technology, 55, 535–538.

    Article  Google Scholar 

  • Savio, E., De Chiffre, L., & Schmitt, R. (2007). Metrology of freeform shaped parts. Annals of the CIRP, 56, 810–835.

    Article  Google Scholar 

  • Schmidt, R., Lyytinen, K., Keil, M., & Cule, P. (2001). Identifying software project risks: An international Delphi study. Journal of Management Information Systems, 17, 5–36.

    Article  Google Scholar 

  • Sokovic, M., & Kopac, J. (2006). RE (reverse engineering) as necessary phase by rapid product development. Journal of Materials Processing Technology, 175, 398–403.

    Article  Google Scholar 

  • Son, S., Park, H., & Lee, K. H. (2002). Automated laser scanning system for reverse engineering and inspection. International Journal of Machine Tools & Manufacture, 42, 889–897.

    Article  Google Scholar 

  • Stefano, T., & Enrico, V. (2005). Feasibility study of a reverse engineering system benchmarking. In Proceedings of ADM-Inge-graf, Siviglia Spagna.

  • Thor, J., Ding, S.-H., & Kamaruddin, S. (2013). Comparison of multi criteria decision making methods from the maintenance alternative selection perspective. The International Journal of Engineering and Science (IJES), 2, 27–34.

    Google Scholar 

  • Tscheikner-Gratl, F., Patrick Egger, P., Wolfgang Rauch, W., & Kleidorfer, M. (2017). Comparison of multi-criteria decision support methods for integrated rehabilitation prioritization. Water, 9, 1–28.

    Article  Google Scholar 

  • Várady, T., Martin, R. R., & Cox, J. (1997). Reverse engineering of geometric models—An introduction. Computer-Aided Design, 29, 255–6.

    Article  Google Scholar 

  • Velasquez, M., & Hester, P. T. (2013). An analysis of multi-criteria decision making methods. International Journal of Operations Research, 10, 56–66.

    Google Scholar 

  • Vezzetti, E. (2007). Reverse engineering: A selective sampling acquisition approach. International Journal of Manufacturing Technology, 33, 521–529.

    Article  Google Scholar 

  • Vrhovec, M., & Munih, M. (2007). Improvement of coordinate measuring arm accuracy. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, San Diego.

  • Wu, M.-C., & Chen, T.-Y. (2009). The ELECTRE multicriteria analysis approach based on intuitionistic fuzzy sets. Fuzzy-IEEE, Korea.

  • Wu, Y., Liu, S., & Zhang, G. (2004). Improvement of coordinate measuring machine probing accessibility. Precision Engineering, 28, 89–94.

    Article  Google Scholar 

  • Yang, T., Xu, S., & Xiong, N. N. (2016). A novel machine selection method combining group eigenvalue method with TOPSIS method. International Journal of Future Generation Communication and Networking, 9, 201–210.

    Article  Google Scholar 

  • Ye, X., Liu, H., Chen, L., Chen, Z., Pan, X., & Zhang, S. (2008). Reverse innovative design—An integrated product design methodology. Computer-Aided Design, 40, 812–827.

    Article  Google Scholar 

  • Yuan, X., Zhenrong, X., & Haibin, W. (2001). Research on integrated reverse engineering technology for forming sheet metal with a freeform surface. Journal of Materials Processing Technology, 112, 153–156.

    Article  Google Scholar 

  • Yue, Z. (2011). A method for group decision-making based on determining weights of decision makers using TOPSIS. Applied Mathematical Modelling, 35, 1926–1936.

    Article  Google Scholar 

  • Zhang, Y. (2003). Research into the engineering application of reverse engineering technology. Journal of Materials Processing Technology, 139, 472–475.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the Deanship of Scientific Research, King Saud University for funding through Vice Deanship of Scientific Research Chairs.

Author information

Authors and Affiliations

  1. Princess Fatima Alnijiris’s Research Chair for Advanced Manufacturing Technology (FARCAMT Chair), Advanced Manufacturing Institute, King Saud University, Riyadh, Saudi Arabia

    Syed Hammad Mian & Abdulrahman Al-Ahmari

  2. Industrial Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi Arabia

    Abdulrahman Al-Ahmari

Authors
  1. Syed Hammad Mian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdulrahman Al-Ahmari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Syed Hammad Mian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mian, S.H., Al-Ahmari, A. Comparative analysis of different digitization systems and selection of best alternative. J Intell Manuf 30, 2039–2067 (2019). https://doi.org/10.1007/s10845-017-1371-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-017-1371-x

Keywords

Search

Navigation