Yixiong Feng

An integrated method for flexible platform modular architecture design

Product platform modular architecture identification is the most critical process to support the incremental design of derivative instances. In order to solve the inflexibility in a single modular or scalable platform of complex mechatronics products, an integrated product modularisation scheme based on flow analysis, design structure matrix (DSM) and fuzzy clustering is proposed to compose a flexible platform. The definition of flexible platform is explained and internal mappings among functions, components and modules are set up. DSM for a kernel product is constructed by the flow analysis between the leaf components in design bill of material. An improved scaling by minimising a convex function algorithm is developed to transform the DSM to vectors in two-dimensional spaces, and the vectors are clustered using the traditional fuzzy c-means algorithm. Cluster centre components are selected to identify modular types with respective functional features. Thus, the internal relationships between components can be modelled with flow analysis-based DSM in a clear format, and the proposed DSM transformation and clustering algorithm identify the modular architecture for a flexible platform in a lower computational complexity. A numerical example and computational comparisons are also given to illustrate the proposed concept and the effectiveness and efficiency of the proposed approach.

Product platform two-stage quality optimization design based on multiobjective genetic algorithm

Product platform design (PFD) has been recognized as an effective means to satisfy diverse market niches while maintaining the economies of scale and scope. Numerous optimization-based approaches have been proposed to help resolve the tradeoff between platform commonality and the ability to achieve distinct performance targets for each variant. In this study, we propose a two-stage multiobjective optimization-based platform design methodology (TMOPDM) for solving the product family problem using a multiobjective genetic algorithm. In the first stage, the common product platform is identified using a nondominated sorting genetic algorithm II (NSGA-II); In the second stage, each individual product is designed around the common platform such that the functional requirements of the product are best satisfied. The design of a family of traction machine is used as an example to benchmark the effectiveness of the proposed approach against previous approachs.

A methodology to support product platform optimization using multi-objective evolutionary algorithms

A critical step when designing a successful product family is to determine a cost-saving platform configuration along with an optimally distinct set of product variants that target different market segments. A multi-objective optimization-based platform design methodology (MOPDM) was presented to optimize the individual product performances with a feasible platform commonality level. The process and optimization model for scale-based product platform was constructed firstly, and then the MOPDM was carried out in two stages using the non-dominated sorting genetic algorithm II (NSGA-II). A mechanism based on fuzzy set theory was developed to extract one of the Pareto-optimal solutions as the best compromise one. During the first stage of MOPDM, each product in the family was optimized independently with NSGA-II. Those design variables that show small deviations were held constant to form the product platform. The scaling variables of each instance product were optimized in the second stage. The efficiency a...A critical step when designing a successful product family is to determine a cost-saving platform configuration along with an optimally distinct set of product variants that target different market segments. A multi-objective optimization-based platform design methodology (MOPDM) was presented to optimize the individual product performances with a feasible platform commonality level. The process and optimization model for scale-based product platform was constructed firstly, and then the MOPDM was carried out in two stages using the non-dominated sorting genetic algorithm II (NSGA-II). A mechanism based on fuzzy set theory was developed to extract one of the Pareto-optimal solutions as the best compromise one. During the first stage of MOPDM, each product in the family was optimized independently with NSGA-II. Those design variables that show small deviations were held constant to form the product platform. The scaling variables of each instance product were optimized in the second stage. The efficiency and effectiveness of proposed method is illustrated by optimizing a family of six capacitor-run single-phase induction motors, and the results are compared against previous work.

A Hybrid Energy-Aware Resource Allocation Approach in Cloud Manufacturing Environment

Cloud manufacturing (CMfg) is a new service-oriented production paradigm from the wide application of cloud computing for the manufacturing industry. The aim of this manufacturing mode is to provide resource-sharing and on-demand manufacturing mode to improve operation efficiency. Resource allocation is considered as a crucial technology to implement CMfg. Traditional resource allocation approaches in CMfg mainly focus on the optimal resource selection process, but the energy consumption for manufacturing resources is rarely considered. In response, this paper proposes a hybrid energy-aware resource allocation approach to help requestors acquire energy-efficient and satisfied manufacturing services. The problem description on energy-aware resource allocation in CMfg is first summarized. Then a local selection strategy based on fuzzy similarity degree is put forth to obtain appropriate candidate services. A multi-objective mathematical model for energy-aware service composition is established and the nondominated sorting genetic algorithm (NSGA-II) is adopted to conduct the combinatorial optimization process. Furthermore, a technique for order preference by similarity to an ideal solution is used to determine the optimal composite services. Finally, a case study is illustrated to validate the effectiveness of the proposed approach.

Equilibrium Design Based on Design Thinking Solving: An Integrated Multicriteria Decision-Making Methodology

A multicriteria decision-making model was proposed in order to acquire the optimum one among different product design schemes. VIKOR method was introduced to compute the ranking value of each scheme. A multiobjective optimization model for criteria weight was established. In this model, projection pursuit method was employed to identify a criteria weight set which could keep classification information of original schemes to the greatest extent, while PROMETHEE II was adopted to keep sorting information. Dominance based multiobjective simulated annealing algorithm (D-MOSA) was introduced to solve the optimization model. Finally, an example was taken to demonstrate the feasibility and efficiency of this model.

An optimal dynamic interval preventive maintenance scheduling for series systems

This paper studies preventive maintenance (PM) with dynamic interval for a multi-component system. Instead of equal interval, the time of PM period in the proposed dynamic interval model is not a fixed constant, which varies from interval-down to interval-up. It is helpful to reduce the outage loss on frequent repair parts and avoid lack of maintenance of the equipment by controlling the equipment maintenance frequency, when compared to a periodic PM scheme. According to the definition of dynamic interval, the reliability of system is analyzed from the failure mechanisms of its components and the different effects of non-periodic PM actions on the reliability of the components. Following the proposed model of reliability, a novel framework for solving the non-periodical PM schedule with dynamic interval based on the multi-objective genetic algorithm is proposed. The framework denotes the strategies include updating strategy, deleting strategy, inserting strategy and moving strategy, which is set to correct the invalid population individuals of the algorithm. The values of the dynamic interval and the selections of PM action for the components on every PM stage are determined by achieving a certain level of system availability with the minimum total PM-related cost. Finally, a typical rotary table system of NC machine tool is used as an example to describe the proposed method.

Research on quality performance conceptual design based on SPEA2

In order to solve the multi-objective performance optimal problems, SPEA2+ is used to realize the performance design of injection molding machine. The optimization objectives are constructed to maximize mould control power, maximize injection quantity and minimize injection power. The mathematical model is found to optimize the problem. A solution is extracted to eliminate the imprecise nature of preference through the Pareto optimal set based on fuzzy set theory. Compared with NSGA-II and SPEA2, SPEA2+ could acquire the Pareto front with better distribution and smaller distance with the optimum solutions. Finally, the case illustration of HTG1000X3Y injection molding machine is taken as an example to demonstrate that such method is effective and practical. Effective references could be provided to decision makers for objectives tradeoff at the performance conceptual design stage of injection molding machine.

Environmentally friendly MCDM of reliability-based product optimisation combining DEMATEL-based ANP, interval uncertainty and Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR)

Environmentally friendly reliability-based product optimisation combining DANP, interval uncertainty and VIKOR is proposed.The proposed method conducts sufficient technical consideration of product environmental features.The organic combination of DANP with interval algorithms can handle the uncertainties and correlations among components.The organic integration of VIKOR can select the optimal reliability design scheme reasonably in the MCDM process.The superiority of the proposed method is verified by the case study of a waste tire shredder. With the far-reaching and overwhelming consequences resulting from energy crisis and carbon emissions, industrial products are required to be environmentally friendly, as well as of high quality and functionality. However, conventional reliability-based product optimisation methods cannot sufficiently ensure the environmental friendliness of modern industrial products. Firstly, the environmental features of a product are not considered. Secondly, the uncertainty of reliability-based product optimisation is not processed efficiently. Thirdly, no sufficient attention is being paid to capturing relationships among product components, despite such dependencies possibly exhibit a major impact on product functions. In order to address these issues, an environmentally friendly multi-criteria decision making (MCDM) model for reliability-based product optimisation is proposed by combining a decision-making trial and evaluation laboratory (DEMATEL)-based analytical network process (ANP) (DANP), interval uncertainty and the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR). The validity of this method is demonstrated by a numerical example.

Data-driven accurate design of variable blank holder force in sheet forming under interval uncertainty using sequential approximate multi-objective optimization

Abstract In this paper, a sequential approximate multi-objective optimization method is employed to obtain the data-driven optimal variable blank holder forces in sheet forming under interval uncertainty. Uncertain parameters driven by data are modeled as intervals, bounds of which can be easily obtained from a small number of uncertainty information. Through a nonlinear interval number programming method, the multi-objective optimization problem under interval uncertainty is converted into a deterministic one. Considering the problem of low efficiency and local optima caused by traditional optimization methods, sequential approximate optimization approach is introduced to improve the efficiency and the ability to find global optimal solutions during variable blank holder forces (VBHF) uncertain multi-objective optimization process. The presented method is applied to accurate design the variable blank holder force for a certain space large size thin-walled part forming under uncertainty. The forming quality is compared with deterministic multi-objective optimization of VBHF, and its effectiveness is proved.

Multi-objective disassembly line balancing via modified ant colony optimization algorithm

The present article focuses on the application of a procedure based on ant colonies to solve disassembly line balancing problem. Firstly, the problem under study is proposed with the objective functions of line idle rate, workload smoothness and disassembly cost. Subsequently, the multi-objective optimization mathematical model of disassembly line balancing is formulated based on the proposed functions of the three objectives. Then, combining characteristics of disassembly line balancing problem, an improved multi-objective ant colony algorithm based on Pareto set is developed to optimize the optimization model. Finally, a practical case is provided to illustrate the proposed model and algorithm.