A Study of the Evaluation of Products by Industrial Design Students
Hsin-Hung Lin 1  
More details
Hide details
Asia University, Department of Creative Product Design, Taichung, TAIWAN, ROC
National Cheng Kung University, Department of Industrial Design, Tainan, TAIWAN, ROC
Online publish date: 2017-11-02
Publish date: 2017-11-02
EURASIA J. Math., Sci Tech. Ed 2018;14(1):239–254
The objective of this study is to provide industrial design students with a comprehensive approach of product evaluation during the course of a research project. During the design evaluation stage, a student often encounters vague information since the attributes of product design demands are usually not quantifiable. Therefore, one of the important topics during the product development processes is to allow a student to carry out design evaluations effectively. The fuzzy Analytic Hierarchy Process (AHP) was utilized in this study to assess product designs and resolve the problems that might occur during product assessments. The purposive sampling technique was used in the questionnaire survey. By assigning a weighted value to each of the evaluation criteria, the case study verified the feasibility of the proposed design approach in fan designs. A total of 52 questionnaire copies were distributed. 36 copies of them were collected and the return rate is 77%. Among them, 28 copies were from the engineers and 8 copies were from people in the relevant industries. 22 copies were from males and this accounts for 61%. On the other hand, 14 copies were from females and this accounts for 39%. The results indicated that the weight of efficiency which is the secondary constituent element is the highest. Under the constituent element of efficiency, the weight of fan flow rate is 0.592 which is the highest. The defuzzification of efficiency is 0.744 which is the optimal value among the indices of various factors. The defuzzified value of Design No.4 after defuzzification is 0.682 which is the optimal one among four design candidates. The results of this study demonstrated the feasibility of the proposed design approach. New fan styles can be effectively created by implementing this design approach. This approach allows the entire evaluation process to be more precise. The combination of these approaches is not only practical and objective, but also is capable of assisting a student in making a decision under complicated and uncertain circumstances. This makes a design task definite and better clarified and also enhances a student’s learning competitiveness by supplying a good reference during the follow-up stage of product design assessments.
1. Asadzadeh, S., Azadeh, A., & Ziaeifar, A. (2011). A Neuro-Fuzzy-Regression Algorithm for Improved Prediction of Manufacturing Lead Time with Machine Breakdowns. Concurrent Engineering, 19, 269.
2. Azadeh, A., Raoofi, Z., & Haghnevis, M. (2012). A unique fuzzy simulation approach for concurrent improvement of customer satisfaction in integrated information and production processes with ambiguity. Concurrent Engineering, 20, 287.
3. Basaran, M. (2012). Calculating fuzzy inverse matrix using fuzzy linear equation system. Applied Soft Computing Journal, 12(6), 1810-1813.
4. Bellman, R., & Zadeh, L., (1970). Decision making in a fuzzy environment. Management Science, 17(4), 141-164.
5. Bortot, S., Alberto, R., & Pereira, M. (2013). Inconsistency and non-additive capacities: The analytic hierarchy process in the framework of Choquet integration. Fuzzy Sets and Systems, 213, 6-26.
6. Calvino, F., Gennusa, M. L., Morale, M. (2010). Comparing different control strategies for indoor thermal comfort aimed at the evaluation of the energy cost of quality of building. Applied Thermal Engineering, 30, (16), 2386-2395.
7. Caputo, A. C., & Pelagagge, P. M., (2000). Fuzzy control of heat recovery systems from solid bed cooling. Applied Thermal Engineering, 20(1), 49-67.
8. Carolus, T., Schneider, M., & Reese, H. (2007). Axial flow fan broad-band noise and prediction. Journal of Sound and Vibration, 300(1-2), 50-70.
9. Chang, C. C., Kuo, Y. F., & Wang, J. C. (2010). Air cooling for a large-scale motor. Applied Thermal Engineering, 30, 1360-1368.
10. Chang, T. C., & Wang, H. (2016). A Multi Criteria Group Decision-making Model for Teacher Evaluation in Higher Education Based on Cloud Model and Decision Tree. Eurasia Journal of Mathematics, Science & Technology Education, 12(5), 1243-1262.
11. Chen, Y. J., Chen, Y. M., Wang, C. B., & Chu, H. C. (2007). Design and Implementation of a Cooperative Enterprise Selection Mechanism for Allied Concurrent Engineering Environment. Concurrent Engineering September, 15, 257-274.
12. Cheng, Q., Zhang, G., Gu, P., & Shao, S. (2012). A product module identification approach based on axiomatic design and design structure matrix. Concurrent Engineering, 20, 185.
13. Chiou, C. B., Chiou, C. H., & Chuc, C. M. (2009). The application of fuzzy control on energy saving for multi-unit room air-conditioners. Applied Thermal Engineering, 29(2-3), 310-316.
14. Choi, J. W., Lee, G., & Kim, M. S. (2011). Capacity control of a heat pump system applying a fuzzy control method. Applied Thermal Engineering, 31(14-15), 2332-2339.
15. Farzaneh, Y., & Tootoonchi, A. A. (2008). Controlling automobile thermal comfort using optimized fuzzy controller. Applied Thermal Engineering, 28(14-15), 1906-1917.
16. Gorener, A., Toker, K., & Uluçay, K. (2012). Application of combined SWOT and AHP: A Case Study for a Manufacturing Firm. Procedia - Social and Behavioral Sciences, 58(12), 1525-1534.
17. Hambali, A., Sapuan, S. M., & Rahim, A. S. (2011). Concurrent Decisions on Design Concept and Material Using Analytical Hierarchy Process at the Conceptual Design Stage. Concurrent Engineering, 19(2), 111-121.
18. Hsiao, S. W. (1998). Fuzzy logic based decision model for product design. International Journal of Industrial Ergonomics, 21(14), 103-116.
19. Hsiao, S. W. (2002). Concurrent design method for developing a new product. International Journal of Industrial Ergonomics, 29(1), 41-55.
20. Hsiao, S. W., & Chou, J. R. (2006). A Gestalt-like perceptual measure for home page design using a fuzzy entropy approach. Int. J. Human-Computer Studies, 64(2), 137-156.
21. Hsiao, S. W., & Liu, E. (2004). A neurofuzzy-evolutionary approach for product design. Integrated Computer-Aided Engineering, 11, 323-338.
22. Hsiao, S. W., & Tsai, H. C. (2004). Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design. International Journal of Industrial Ergonomics, 35(5), 411-428.
23. Hsiao, S. W., Lin, H. H., & Ko, Y. C. (2017). Application of Grey Relational Analysis to Decision-Making during Product Development. EURASIA Journal of Mathematics Science and Technology Education, 13(6), 2581-2600.
24. Hsiao, S. W., Lin, H. H., & Lo, C. H. (2016). A study of thermal comfort enhancement by the optimization of airflow induced by a ceiling fan. Journal of Interdisciplinary Mathematics, 19(4), 859-891.
25. Hsiao, S. W., Lin, H. H., Lo. C. H., & Ko, Y. C. (2016). Automobile shape formation and simulation by a computer-aided systematic method. Concurrent Engineering, 24(3), 290-301.
26. Hu, X., Hu, J., & Sekhari, A. (2011). A Fuzzy Knowledge Fusion Framework for Terms Conflict Resolution in Concurrent Engineering. Concurrent Engineering, 19, 71.
27. Huang, H. Z., Gu, Y. K., & Li, Y. H. (2008). Neural-network-driven Fuzzy Reasoning of Dependency Relationships among Product Development. Concurrent Engineering, 16, 213.
28. Jiao, J., & Tseng, M. M. (1998). Fuzzy Ranking for Concept Evaluation in Configuration Design for Mass Customization. Concurrent Engineering, 6, 189.
29. Khayyam, H., Abawajy, J., & Jazarc, R. N. (2012). Intelligent energy management control of vehicle air conditioning system coupled with engine. Applied Thermal Engineering, 48(15), 211-224.
30. Khoo, L. P., Chen, C. H., & Jiao, L. (2003). A Dynamic Fuzzy Decision Support Scheme for Concurrent Design Planning. Concurrent Engineering, 11, 279.
31. Korposh, D., Lee, Y. C., Wei, C. C., & We, C. C. (2011). Modeling the Effects of Existing Knowledge on the Creation of New Knowledges. Concurrent Engineering, 19, 225.
32. Kuo, Y. F., & Chen, P. C. (2006). Selection of mobile value - Added services for system operators using fuzzy synthetic evaluation. Expert Systems with Applications, 30(4), 612-620.
33. Lata, S., & Kumar, A. (2012). A new method to solve time-dependent intuitionistic fuzzy differential equations and its application to analyze the intuitionistic fuzzy reliability of industrial systems. Concurrent Engineering, 20, 177.
34. Lin, M. C., Wang, C. C., & Chen, T. C. (2006). A Strategy for Managing Customer-oriented Product Design. Concurrent Engineering, 14, 231.
35. Lin, S. C., & Chou, C. A. (2004). Blockage effect of axial-flow fans applied on heat sink assembly. Applied Thermal Engineering, 24(23), 75-2389.
36. Liu, G., & Liu, M. (2012). Development of simplified in-situ fan curve measurement method using the manufacturers fan curve. Building and Environment, 48, 77-83.
37. Liu, S., Zhang, J., & Liu, W. (2012). Qian, A comprehensive decision-making method for wind power integration projects based on improved fuzzy AHP. Energy Procedia, 14, 937-942.
38. Lo, C. H. (2016). Building a Relationship between Elements of Product Form Features and Vocabulary Assessment Models. Eurasia Journal of Mathematics, Science & Technology Education, 12(5), 1399-1423.
39. Moon, J. M., Jung, S. K., & Han, S. H. (2011). Comparative study of artificial intelligence-based building thermal control methods e Application of fuzzy, adaptive neuro-fuzzy inference system, and artificial neural network. Applied Thermal Engineering, 31, 2422-2429.
40. Nepal, B., Yadav, O. P., & Murat, A. (2010). A fuzzy-AHP approach to prioritization of CS attributes in target planning for automotive product development. Expert Systems with Applications, 37, 6775-6786.
41. Peng, J. (2012). Selection of logistics outsourcing service suppliers based on AHP. Energy Procedia, 17, 595-601.
42. Subramanian, N., & Ramanathan, R. (2012). A review of applications of analytic hierarchy process in operations management. International Journal of Production Economics, 138(2), 215-241.
43. Tsai, H. C., & Hsiao, S. W. (2004). Evaluation of alternatives for product customization using fuzzy logic. Information Sciences, 158, 233-262.
44. Tsaur, S. H., Chang, T. Y., & Yena, C. H. (2002). The evaluation of airline service quality by fuzzy MCDM. Tourism Management, 23(2), 107-115.
45. Tummala, V. M. R., Chin, K. S., & Ho, S. H. (1998). Evaluating Success Factors to Implement CE Using AHP in Hong Kong Electronics and Plastic Products Industries. Concurrent Engineering, 6, 245.
46. Wang, M. J., & Chang, T. C. (1995). Tool steel materials selection under fuzzy environment. Fuzzy Sets and Systems, 72(3), 263-270.
47. Wu, Y. W., Weng, K. H., & Young, L. M. (2016). A Concept Transformation Learning Model for Architectural Design Learning Process. Eurasia Journal of Mathematics, Science & Technology Education, 12(5), 1189-1197.