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J. Comput. Inf. Sci. Eng. 2019;19(4):041001-041001-12. doi:10.1115/1.4042837.

In the early stages of the product design, multiple principle solutions are obtained through function solving, and a large number of conceptual schemes are generated by combination. Therefore, scheme decisions are important factors in the concept design. The existing decision methods primarily focus on the satisfaction of economic needs, and the impact of technical indicators on the technical performance of the scheme, while ignoring the conflict of needs between the two subject objectives in the decision process. Actual decisions need to be weighed against each other’s expectations. In addition, the qualitative interactive objectives will affect the decision direction of the conceptual scheme. Herein, we propose a relative equilibrium decision approach for concept design based on the fuzzy decision-making trial and evaluation laboratory-cooperative game model. This model is primarily divided into two parts. One is to solve the impact relationship between the objectives, and the objectives’ weights are obtained through fuzzy decision-making trial and evaluation laboratory (FDEMATEL). The second is to incorporate the objectives’ weights and impact utility into the cooperative game model, to reasonably weigh the relative interests of the two subjects to meet the corresponding interactions, and to obtain the scheme with the largest overall design desirability. Finally, the case study proves that this decision model can identify the optimal scheme. This model is proven to be robust by comparison with other methods.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2019;19(4):041002-041002-11. doi:10.1115/1.4042838.

Math models aid designers in assessing relationships between tolerances that contribute to variations of a dependent dimension that must be controlled to achieve some design function at a target (functional) feature. The Tolerance-Maps© (T-Maps©) model for representing limits to allowable manufacturing variations is applied to identify the sensitivity of a dependent dimension to each contributing tolerance of the relationship. For each contributing feature and tolerances specified on it, the appropriate T-Map is chosen from a library of T-Maps, each represented in its own respective local reference frame. Each chosen T-Map is then transformed to the coordinate frame at the target feature, and the accumulation T-Map of these is formed with the Minkowski sum. The shape of a functional T-Map/deviation space is circumscribed (fitted) to this accumulation map. Since fitting is accomplished numerically by intersecting geometric shapes, T-Maps/deviation spaces are constructed with linear half-spaces. The sensitivity for each tolerance-and-feature combination is determined by perturbing the tolerance, refitting the functional shape to the modified accumulation map, and forming a ratio of the increment of functional tolerance to the perturbation. Taking tolerance-feature combinations one by one, sensitivities for an entire stack can be built. For certain loop equations, the same sensitivities result by fitting the functional shape to the T-Map/deviation space for each feature, without a Minkowski sum, and forming the overall result as a scalar sum. Sensitivities are used to optimize tolerance assignments by identifying the tolerances that most strongly influence the dependent dimension at the target feature. Form variations are not included in the analysis.

Commentary by Dr. Valentin Fuster

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