| Affiliation: | 1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China;2. Departments of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Alberta, Canada;1. Department of electronics and information engineering, Korea University Sejong Campus, Sejong 30019, Korea\n;2. Department of control and robotics engineering, Kunsan National University, Kunsan 54150, Korea;1. School of Management, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul 130-701, Republic of Korea;2. Big Data Center at Kyung Hee University 26, Kyungheedae-ro, Dongdaemun-gu, Seoul 130-701, Republic of Korea;1. DISIT, Computer Science Institute, Università del Piemonte Orientale, Viale Michel 11, Alessandria, Italy;2. Department of Computer Science, Università di Torino, Corso Svizzera 105, Torino, Italy;3. Department of Electrical, Computer and Biomedical Engineering, Università di Pavia, Via Ferrata 1, Pavia, Italy;4. I.R.C.C.S. Fondazione “C. Mondino”, Via Mondino 2, Pavia, Italy - on behalf of the Stroke Unit Network (SUN) collaborating centers, Italy;1. Department of Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran;2. Department of Computer Engineering, University of Guilan, Rasht, Iran;3. Department of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran |
| Abstract: | Conceptual design plays an important role in development of new products and redesign of existing products. Morphological matrix is a popular tool for conceptual design. Although the morphological-matrix based conceptual design approaches are effective for generation of conceptual schemes, quantitative evaluation to each of the function solution principle is seldom considered, thus leading to the difficulty to identify the optimal conceptual design by combining these function solution principles. In addition, the uncertainties due to the subjective evaluations from engineers and customers in early design stage are not considered in these morphological-matrix based conceptual design approaches. To solve these problems, a systematic decision making approach is developed in this research for product conceptual design based on fuzzy morphological matrix to quantitatively evaluate function solution principles using knowledge and preferences of engineers and customers with subjective uncertainties. In this research, the morphological matrix is quantified by associating the properties of function solution principles with the information of customer preferences and product failures. Customer preferences for different function solution principles are obtained from multiple customers using fuzzy pairwise comparison (FPC). The fuzzy customer preference degree of each solution principle is then calculated by fuzzy logarithmic least square method (FLLSM). In addition, the product failure data are used to improve product reliability through fuzzy failure mode effects analysis (FMEA). Unlike the traditional FMEA, the causality relationships among failure modes of solution principles are analyzed to use failure information more effectively through constructing a directed failure causality relationship diagram (DFCRD). A fuzzy multi-objective optimization model is also developed to solve the conceptual design problem. The effectiveness of this new approach is demonstrated using a real-world application for conceptual design of a horizontal directional drilling machine (HDDM). |
