As an effective lightweight technique, reliability-based multi-objective optimization (RBMO) for welding process parameters of aluminum alloy sheets demonstrates the unprecedented potential and stability in the automo...
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As an effective lightweight technique, reliability-based multi-objective optimization (RBMO) for welding process parameters of aluminum alloy sheets demonstrates the unprecedented potential and stability in the automobile manufacturing. In order to ensure load-bearing capacity and assembly feasibility of welded joints in the body structure, the process-property-performance (3P) relationship should be fully considered in optimizing double-pulse MIG (DP-MIG) welding process parameters. This study proposes an RBMO design of welding process parameters that employed a hybrid optimization strategy (HOS), which includes screening significant parameters, building process-property meta-models, and searching optimal solution under performance requirements. Combining entropy weight and technique for order preference by similarity to an ideal solution for Plackett-Burman design is used to screen significant parameters. Then, the response surface methodology based on central composite design is used to construct the regression models between significant factors and responses. Also, the reliability of each response is analysed through the Monte Carlo simulation and Design for Six Sigma design. The non-dominated sorting genetic algorithm and multi-objective decision criteria based on performance requirements are employed to find the optimal solution of RBMO. The effectiveness and applicability of the proposed HOS method are demonstrated by optimization of DP-MIG welding process parameters, which could yield improvements of load-bearing, geometry performance of welded joints and their robustness. The combination of 3P relationships and optimization design reveals the internal connection between design and manufacturing, and provides a guideline for DP-MIG welding parameters design. RBMO can be generally applied to types of jointing technology in automotive manufacturing and the proposed HOS method can be used in the optimization design of multiple influencing factors and multiple r
This paper proposes an effective couple method for solving reliability-based multi-objective optimization problems of truss structures with static and dynamic constraints. The proposed coupling method integrates a sin...
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This paper proposes an effective couple method for solving reliability-based multi-objective optimization problems of truss structures with static and dynamic constraints. The proposed coupling method integrates a single-loop deterministic method (SLDM) into the nondominated sorting genetic algorithm II (NSGA-II) algorithm to give the so-called SLDM-NSGA-II. Thanks to the advantage of SLDM, the probabilistic constraints are treated as approximating deterministic constraints. And therefore the reliability-based multi-objective optimization problems can be transformed into the deterministic multi-objectiveoptimization problems of which the computational cost is reduced significantly. In these reliability-based multi-objective optimization problems, the conflicting objective functions are to minimize the weight and the displacements of the truss. The design variables are cross-section areas of the bars and contraints include static and dynamic constraints. For reliability analysis, the effect of uncertainty of parameters such as force, added mass in the nodes, material properties and cross-section areas of the bars are taken into account. The effectiveness and reliability of the proposed method are demonstrated through three benchmark-type truss structures including a 10-bar planar truss, a 72-bar spatial truss and a 200-bar planar truss. Moreover, the influence of parameters on the reliability-based Pareto optimal fronts is also carried out.
In the literature, several works aim to improve the performance of hydrocyclones using geometry optimization. However, these approaches do not consider the uncertainties in the models, design variables, and parameters...
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In the literature, several works aim to improve the performance of hydrocyclones using geometry optimization. However, these approaches do not consider the uncertainties in the models, design variables, and parameters, which can lead to solutions that do not correspond to the expected results when applied to real systems. Considering that, the present work proposed a reliability-basedoptimization of the hydrocyclone dimensions using the Advanced Mean Value (AMV) technique associated with the multi-objective Differential Evolution (MODE) algorithm. For that purpose, the maximization of total efficiency and minimization of underflow-tothroughput ratio and Euler number were taken as objectives and constraints were set to ensure an acceptable performance of the hydrocyclone in the main aspects of the process. Some of the obtained devices were selected to be fabricated through an additive manufacturing technique (3D printing) and, after that, be experimentally tested. The optimization results showed that the reliability-basedoptimization affected the Pareto curves in two different ways: a shortening or a displacement of the curve in relation to the nominal one, depending on which constraints are being activated. The experimental results demonstrated that the hydrocyclones obtained by the reliable approach had a greater tendency to meet the set constraints in practice. Finally, hydrocyclones with great experimental performance were obtained considering separation efficiency, concentration capacity, and energy consumption.
This paper presents a methodology for reliability-basedmultiobjective design optimization (RBMODO) of automotive body components under impact scenario. Conflicting design requirements arise as one tries, for example,...
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ISBN:
(纸本)9780791842492
This paper presents a methodology for reliability-basedmultiobjective design optimization (RBMODO) of automotive body components under impact scenario. Conflicting design requirements arise as one tries, for example, to minimize structural mass while maximizing energy absorption of an automotive rail section under structural and occupant safety related performance measure constraints. Because deterministic optimum designs obtained without taking uncertainty into account could lead to unreliable designs, a reliability-based approach to design optimization is preferable using a reliability-based design optimization method. Uncertainty quantification is performed using two methods: reliabilitybased approach and robustness based approach. The technique employed here treats multiple objective functions separately without combining them in any form. A decision-making criterion is subsequently invoked to select the "best" subset of solutions from the obtained non-dominated Pareto optimal solutions. The pareto optimal set obtained in case are compared and contrasted and observations made comparing reliabilitybased approach vis-a-vis robustness based approach. Deterministic, reliability-based and robustness basedmultiobjectiveoptimization solutions are compared.
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