A fuzzy QFD program modelling approach using the method of imprecision

The use of quality function deployment (QFD) to aid decision making in product planning has gained extensive international attention, but current QFD approaches are unable to cope with complex product planning (CPP) characterized by involving multiple engineering characteristics (ECs) associated with significant uncertainty. To tackle this difficulty, in this paper, fuzzy set theory is embedded into a QFD framework and a novel fuzzy QFD program modelling approach to CPP is proposed to optimize the values of ECs by taking the design uncertainty and financial considerations into account. In the proposed methodology, fuzzy set theory is used to account for design uncertainty, and the method of imprecision (MoI) is employed to perform multiple-attribute synthesis to generate a family of synthesis strategies by varying the value of s, which indicates the different compensation levels among ECs. The proposed methodology will allow QFD practitioners to control the distribution of their development budget by presetting the value of s to determine the compensation levels among ECs. An illustrative example of the quality improvement of the design of a motor car is provided to demonstrate the application and performance of the modelling approach.     

Optimisation of cutting parameters for burr minimization in face-milling operations

Burrs formed during face milling operations can be very difficult to characterize since there exist several parameters which have complex combined effects that affect the cutting process. Many researchers have attempted to predict burr characteristics including burr size and shape, using various experimental parameters such as cutting speed, feed rate, in-plane exit angle, and number of inserts. However, the results of these studies tend to be limited to a specific process parameter range and to certain materials. In this paper, the Taguchi method--which is a systematic optimisation method for design and analysis of experiments--is introduced to acquire optimum cutting conditions for burr minimization. In addition, analysis of variance (ANOVA) is employed to study more detailed performance characteristics. Experimental verifications are provided to show the effectiveness of this approach.     

A novel fuzzy group decision-making approach to prioritising engineering characteristics in QFD under uncertainties

In new product development, design teams commonly need to define engineering characteristics (ECs) in a quality function deployment (QFD) planning process. Prioritising the engineering characteristics in QFD is essential to properly plan resource allocation. However, the inherent vagueness or impreciseness in QFD presents a special challenge to the effective calculation of the importance of ECs. Generally, there are two types of uncertain input in the QFD process: human perception and customer heterogeneity. Many contributions have been made on methods to prioritise ECs. However, most previous studies only address one of the two types of uncertainties that could affect the robustness of prioritising ECs. To address the two types of uncertainties simultaneously, a novel fuzzy group decision-making method that integrates a fuzzy weighted average method with a consensus ordinal ranking technique is proposed. An example is presented to illustrate the effectiveness of the proposed approach. Results of the implementation indicate that the robustness of prioritising ECs based on the proposed approach is better than that based on the method of Chen et al. (Chen, Y., Fung, R.Y.K., Tang, J.F., 2006. Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator. European Journal of Operational Research, 174 (3), 1553–1556).     

Quantification for the importance degree of engineering characteristics with a multi-level hierarchical structure in QFD

Quantification for the importance degree of engineering characteristics (ECs) is an essential problem in quality function deployment. In real-world scenario, it is sometimes difficult to directly evaluate the correlation degree between ECs and customer requirements (CRs) as ECs are too abstract. Thus, the target ECs have to be further decomposed into several more detailed basic ECs and organised by a multi-level hierarchical structure. The paper investigates the quantification problem for the importance degree of such target ECs and tackles two critical issues. The first issue is how to deal with the uncertainties including fuzziness and incompleteness involved during the evaluation process. A fuzzy evidential reasoning algorithm-based approach is proposed to tackle this issue and derive the correlation degree between each of the basic ECs and the whole CRs. The second issue is how to deal with the interactions among the basic ECs decomposed from the same target EC during the aggregation process. A λ-fuzzy measure and fuzzy discrete Choquet integral-based approach is proposed to tackle this issue and aggregate these basic ECs. Final importance degree of the target ECs can then be obtained. At the end of this paper, a case study is presented to verify the feasibility and effectiveness of the method we propose.