Jianrong Tan

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.

Intelligent assembly modeling based on semantics knowledge in virtual environment

The paper proposes a semantics knowledge modeling approach for product assembly, which is suitable for virtual reality interaction. Assembly semantics knowledge is used to express design conception, constrain the relationship between parts, and encapsulate design knowledge in semantics knowledge modeling. Using assembly semantics, the designer is able to express design intention in an engineers language, which is natural and convenient for designer. In addition, the intelligence of the system can be promoted through the inference of implicit knowledge of semantics. These methods are implemented in the development of VIRDAS (Virtual Reality Design and Assembly System) and several assembly examples are also given.

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 virtual environment simulator for mechanical system dynamics with online interactive control

Abstract Virtual prototyping is an effective tool in the development of mechanical product. Physically accurate simulation of multi-body mechanical system enables designers to investigate, explore, and experience the performance and behavior of an evolving product and thus reduce the number of physical prototypes needed. For the sake of better support for designers to manipulate the simulation, we have developed a dynamic simulation package of mechanical system, which provides an interactive control during the simulation process. The package incorporates physical behaviors and dynamic interactions by dynamics based modeling approach in multi-body mechanical system. In the package, user’s actions (e.g., loading a model, picking and dragging objects, steering objects during simulation process, etc.) are based on an ATN task management, and a multi-modal interface is provided which supports 2D desktop devices and 3D VR devices. The main contribution of the simulator is providing supports that allow users’ interactive manipulation in the simulation loop. During the simulation process, users can modify the constraints between components, apply force/torque to interested components and change the parameters of forces/torques. The simulator automatically updates the dynamic model of the mechanical system in real time, and then continuously simulates the behavior of the model under the current condition in the loop. An example of dynamic simulation of a vehicle is implemented and the simulation result is compared to that of ADAMS to verify the correctness and accuracy of the simulator. With the real-time interaction, solution and visualization of simulation model, the package affords better support for designers to participate in the simulation interactively and effectively.

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.

Industrial robot layout based on operation sequence optimisation

The robot layout is one of the primary problems in the robot work cell design. An optimal layout not only makes the robotic end-effector reach the desired position with the desired orientation, but also minimises the robot cycle time for completing a given task with collision avoidance. There may be many feasible robot operation sequences for a given task. The optimisation of operation sequence for a robot placed in a fixed location is NP-complete. There may be different optimal operation sequences as the robot is placed in the different locations. Hence the problem of robot layout is quite complex. This paper presents a method of industrial robot layout based on operation sequence optimisation. The robot motion control model with velocity, acceleration and jerk is established. The feasible space of robot base is calculated and then divided into discrete grids before the optimisations. Then the ant colony algorithm is applied to optimise the operation sequence for each grid. The adjacent grids are merged or divided enough times to form the bigger grids. The pattern search algorithm is adopted to solve the local optimal position and orientation of the robot base in each big grid, and the global optimal layout is found through the comparison of the local solutions. The industrial robot layout method has been applied to the work cell design for car door welding.

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.

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.

Positioning and driving virtual prototyping with metaphors in dynamic analysis

Abstract In the process of dynamic analysis for virtual prototyping based on multibody system equations, from which the external force and moment are isolated as a kind of drive to simulation. Two models are proposed for the external force/moment to define the behavior property of the generalized external force vector, and they are banded together with metaphors which associate users with the artifacts in virtual environment so as to make some parameters of the prototype adjusted on-line and simulation process more flexible. It turns out that this method should be effective and intuitive very much by analyzing a slider crank mechanism of an automotive engine as a virtual prototyping instance.