Hao Zheng

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.

An integrated modular design methodology based on maintenance performance consideration

Maintenance nowadays not only plays a crucial role in the usage phase, but is fast becoming the primary focus of the design stage—especially with general increased emphasis on product service. The modularization of maintenance has been explored rarely by previous researchers, despite its significant potential benefit. Existing modular design methods on life cycle do not sufficiently improve maintenance performance as a whole. In effort to remedy this, this article considers relevant maintenance issues at early stages of product development and presents a novel modular methodology based on the simultaneous consideration of maintenance and modularity characteristics. The proposed method first employs the design structure matrix to analyze the comprehensive correlation among components. Next, based on graph theory, initial modules with high cohesion and low coupling are generated. After that, a maintenance performance multi-objective model is established for further optimization to minimize maintenance costs, minimize differences in the maintenance cycle, and maximize system availability. To conclude, an improved strength Pareto evolutionary algorithm 2 is used for modular optimization. The complete methodology is demonstrated using a case study with a hydraulic press, where results reveal that the optimized modules can reduce maintenance cost under the premise of approximately constant modular performance.

The Evolutionary Mechanism of Unit Cell: Parameterizations of Polyhedron Sandwich Structure Based on Rigid Origami

As the advantages of foldable or deployable structures are being discovered, research into origami engineering has attracted more focus from engineers. With computer aided design and parameterized modeling techniques, some computational origami design methods have been developed. Most of these methods focus on the problem of origami crease pattern design—the problem of determining a crease pattern to realize a specified origami final shape, but do not provide computational solutions to actually developing a shape that meets some design performance criteria. This paper presents a parameterization method of unit cell for N-1 type base patterns. Then, a computational design method of polyhedron sandwich structure is proposed and the polyhedron sandwich structure will be designed to satisfy geometric, functional, and foldability requirements. All parameterizations are validated by comparison with physical prototypes and compiled into a MATLAB Toolbox for subsequent work.

Research on intelligent product conceptual design based on cognitive process

Early in the design process, designers attempt to explore in the wide design space to generate a number of feasible solutions and decide the best concept scheme with a high degree of uncertainty according to customer demands. A strategy that can assist designers in exploring and ascertaining design solutions within this vast space is therefore crucial. However, existing product design tools mainly focus on the detailed design phase and due to lack of effective design tools, it is often difficult for human designers to explore in wide multi-disciplinary solution spaces. Therefore, this paper proposes a cognitive process-based approach for assisting designers achieving intelligent conceptual design. By analyzing designers’ thinking characteristics, a design meta-thinking model is defined and a simulation model which is composed of divergent thinking and convergent thinking is put forward. Case-based reasoning and genetic algorithm are applied to stimulate divergent thinking to associate and produce feasible concept solutions. Furthermore, multiple-attribute decision-making method based on intuitionistic fuzzy number is employed to stimulate convergent thinking to obtain the best solution from generated conceptual candidate solutions. Finally, a case study is implemented on a turbo-expander. The result of this example demonstrates that the proposed approach can provide an intelligent manner to perform conceptual design. Meanwhile, a computer-aided design prototype system is developed based on this framework.