Constructive optimisation criteria to increase bearing service life

For light mechanical structures, a bearing housing cannot be considered as rigid. Thus, rolling bearing rings are elastically deformed under loading and lose their initial cylindrical shape. The load distribution among the rolling bodies is then modified, which therefore affects the bearing life. In order to improve deficiencies in the existing models concerning the load distribution in the case of non-rigid housings, a new calculation model is proposed in the present work. This model takes into account all the constructive parameters that define the housing geometry. Using this model one can improve an existing housing shape or can optimise the design of a new one. The paper presents the results of a detailed, theoretical and experimental research project concerning the effect of the housing geometry on the bearing service life. This revised version was published online in July 2006 with corrections to the Cover Date.     

Input variable selection for model-based production control and optimisation

For model-based production control and optimisation, it is crucial to properly identify those input variables that have the strongest influence on production performance. This way, production operators can focus only on the relevant variables, and production control problems can be reduced. In order to identify previously unknown relationships among the production variables, hidden knowledge in historical production data needs to be explored. In the article, two decisive steps are considered. First, an input variable selection methodology, typically applied for selecting model regressors, is applied. Next, the appropriateness of the selected inputs and their manipulative strength is validated by an operating-space-based controllability analysis. To use the most appropriate input variable selection approach, different input selection methodologies are compared with synthetic data sets. Moreover, a case study of Tennessee Eastman process is applied to demonstrate a complete input variable selection procedure for model-based production control and optimisation.     

生产线缓冲区容量优化配置:案例研究

生产线缓冲区容量的配置是影响系统性能指标的重要因素之一。这里采用具有阻塞的开排队网络模型研究生产线缓冲区容量的优化配置问题,编制了该模型的一种近似解法——分解算法的应用程序以及仿真程序,并结合工厂的生产实例,探讨了该生产线缓冲区的分配方案,对提高生产效益有一定的指导意义。     

Towards the integration of process and production planning: an optimisation model for cutting parameters

This paper aims to contribute to process and production planning integration through the development of a new cutting parameters optimisation model. The developed model considers simultaneously the technology-related constraints and a shop floor constraint determined by the available time at each workstation. The latter, being a constraint related to the part machining time, is associated with the set of all elementary machining operations and implies the development of a new multi-operation optimisation model. In this approach, part machining time is a new variable for shop floor scheduling. Since the limiting factor of workstation available time at every scheduling date depends on the shop floor status, optimum part machining time can range from the time for minimum cost to the time for maximum production rate. The introduction of the available time in the optimisation process allows for the generation of improved schedules according to several performance measures. The proposed optimisation model is non-linear, uni-criterion and multi-variable. The search of the optimal solution is carried out using sequential quadratic programming.     

Tool selection optimisation for turning operations

This paper focuses on the combinatorial problems posed by tool selection for external turning operations. The problem of selecting the best set of tools to machine a component, according to a certain economic criterion, is here addressed as a binary programming problem and a specific branch-and-bound method for dealing with it is described. The results obtained show that this method solves the problem quickly and with little effort.     

Dynamic ant colony optimisation for TSP

Ants exhibit collective behaviour in performing tasks that cannot be carried out by an individual ant. When ants are working, they must communicate with each other through a kind of chemical substance—pheromones. Ants look for food and lay the way back to their nest with pheromones, and the other ants can follow the pheromone to find the food efficiently. Using the analogy of foraging behaviour and pheromones, Marco Dorigo proposed the ant algorithm and applied it to solving the travelling salesman problem (TSP) and solving job-shop scheduling. In this paper, we simulate real ants with more aspects. Updating of pheromones is more likely to be the real situation in the natural world. Our algorithm shows a better performance than the original algorithm.     

A tolerancing optimisation method for product design

Although extensive research has been carried out in the area of tolerancing techniques for product design, concurrent engineering is still very seldom used in this context. This paper introduces a unique tolerancing method which applies the concept of concurrent engineering. The proposed method essentially allocates the required functional assembly tolerances to the component tolerances by formulating the tolerancing problem into a mathematical model and solving the model using a linear programming approach. The component tolerances are first represented in terms of the process tolerances, assigned by process planners at an early stage of the product design. The objective function of the mathematical model, which is to maximise the residual tolerances of the processes, is then established and the constraints formulated based on the assembly requirements and process constraints. The model is subsequently solved using a linear programming approach. Finally, the proposed method is tested on a practical example.     

A cost-tolerance model for process sequence optimisation

This paper concerns the modelling of cost-tolerance data for various manufacturing processes and the optimisation of process sequences based on minimum production cost. A natural spline model representing the cost-tolerance relationship is introduced. A methodology to optimise the process sequences is developed using an expert system approach. An example to illustrate the methodology and the optimisation model is presented     

Process optimisation of transfer moulding for electronic packages using artificial neural networks and multiobjective optimisation techniques

Transfer moulding is the most common process for the encapsulation of electronic packages in semiconductor manufacturing. Quality of the moulding is affected by a large number of mould design parameters and process parameters. Currently, the parameters setting is performed by experienced engineers in a trial and error manner and often the optimal setting can not be obtained. In the face of global competition, the current practice is inadequate. In this research, a process optimisation system for transfer moulding of electronic packages is described which involves design of experiments (DOE) techniques, artificial neural networks (ANNs), multiple regression analysis and the minimax method. The system is aimed to determine the optimal mould design parameters and process parameter settings of transfer moulding of electronic packages for multiobjective problem. Implementation of the optimisation system has demonstrated that the time for the determination of optimal mould design parameters and process parameters setting can be greatly reduced and the parameters setting recommended by the system can contribute to the good quality of moulded packages without relying on experienced engineers.     

Genetic ordinal optimisation for stochastic flow shop scheduling

Stochastic flow shop scheduling is a typical and widely studied NP-hard stochastic optimisation problem with strong industrial roots. However, due to inaccurate estimation of objective values, NP-hardness and a limited computing budget, it is generally hard to solve such stochastic optimisation problems effectively and efficiently. Based on the idea of order comparison and goal softening, ordinal optimisation (OO) has been widely applied for stochastic optimisation. In this paper, OO and optimal computing budget allocation (OCBA) as well as a genetic algorithm (GA) are reasonably hybridised to propose an effective genetic ordinal optimisation (GOO) approach for flow shop scheduling with stochastic processing times. In GOO, limited computing effort can be intelligently allocated by OCBA to provide reliable and robust evaluation and identification of good solutions in a population, and the solution space can be well explored by an order-based evolutionary genetic search with the good solutions identified by OCBA. Simulation results based on benchmarks demonstrate the effectiveness of the GOO by comparison with traditional methods. Moreover, the effects of some parameters on the optimisation performance are discussed.     

The optimisation deep-draw clearance design for deep-draw dies

In this paper, the modelling of deep-drawing processing using neural networks is established. The relationships between process parameter (material thickness, punch diameter, die-cavity diameter and materials-clearance ratio) and deep-drawing performance (the dimensional error of diameter and cylinder) are created, based on a neural network. A simulated annealing (SA) optimisation algorithm with a performance index is then applied to the neural network to search for the optimal design parameters of the drawing-die. Experimental results have shown that deep-drawing performance can be enhanced by using this approach.     

适用于立方体卫星的激光测距合作目标

为了给立方体卫星星载GPS定轨数据提供检核标准,并满足高精度测定轨道的应用要求,通过在10cm×10cm×10cm的标准立方体卫星的每个表面分布3个通光口径为1.0cm的微小激光反射器,设计了质量约为108g、视场角满足360°、测距精度可达厘米级的激光测距合作目标。根据角锥棱镜的二面角误差、反射面面形误差及入射面面形误差所引起的光束附加相位,分析角锥棱镜在远场的衍射分布特性;接着,根据激光合作目标在卫星上的分布方式,计算激光合作目标的相对有效面积分布;然后,利用激光测距方程估算不同轨道高度的立方体卫星激光反射器回波光子数,并根据卫星质心改正模型估算激光测距精度;最后,以皮卫星激光合作目标测距试验为例,验证了装载在卫星上的1.0cm微小激光反射器能够反射回足够的激光回波信号。结果表明,此种分布方式对于运行在250~1 000km轨道上的立方体卫星能够提供足够的回波信号,激光测距内精度可达厘米级,满足立方体卫星对激光测距合作目标质量轻、分布灵活、测距精度高的要求。     

An optimisation of micro-EDM operation for fabrication of micro-hole

Micro electro-discharge machining (micro-EDM) operation is a versatile operation to machine any hard conducting material to fabricate micro-products of different shape and size. The micro-holes are a common feature in many micro-products. It is a challenging task to improve the surface integrity and aspect ratio of holes. A number of controlling parameters affect the micro-EDM operation. In the present investigation, the optimization of micro-EDM operation has been carried out for fabrication of micro-holes using design of experiment technique using the Taguchi method. The circularity error, the recast layer and machining time has been analysed. The significant factors influencing the process have been found out. The workpiece is taken as copper and the tool is tungsten electrode.     

Evolutionary optimisation of hedging points for unreliable manufacturing systems

The production rates of manufacturing systems are notoriously difficult to control, since such systems are dynamic, uncertain and non-linear. However, the introduction of hedging-point policies for such systems has led to much progress in optimal production control. But the theoretical results so far obtained for such hedging-point policies are still far from complete, since the optimal hedging points (i.e., the optimal inventory levels) are analytically available only for simple systems and under restrictive assumptions. In this paper, an evolutionary stochastic optimisation procedure is proposed to estimate the short-run optimal hedging points for failure-prone manufacturing systems under crisp-logic control. This methodology is illustrated by examples and is validated by comparing the evolutionary results with the available analytical long-run solutions. The proposed evolutionary methodology is also shown to be capable of generating optimal hedging points for unreliable systems producing multiple products with different priorities. In addition, the relative merits of genetic algorithms, evolution strategies and adaptive evolution strategies in hedging-point optimisation are compared.     

International strategies used for availability optimisation of assembly systems

The Institute of Production Systems investigated strategies and methods for availability optimisation of complex assembly systems in several countries all over the world. Researchers from the institute carried out the survey on site in the factories. The investigation consisted of interviewing, based on a uniform questionnaire, those responsible for maintenance and line operation, and capturing standstill data direct from the assembly lines to evaluate the information from the questionnaires. This procedure combined with similar constraints at the lines investigated allows a comparison of all participating assembly systems.     

机电一体化协同设计平台研究

为了提高机电一体化产品设计中多学科多专业设计人员协同设计的效率,利用CAX软件,基于Web的项目组级PDM,UML技术,虚拟现实样机技术搭建机电一体化协同设计平台,来满足机电一体化多学科的高效协同设计.重点讨论了组成该协同设计平台的基于Web的项目组级PDM服务器和完成各种设计任务的工作站群,以及该平台的运作过程.该平台的试验性运行结果表明,该机电一体化协同设计平台能够提高设计团队多学科多专业的设计人员协同设计效率,并且在设计过程中,设计人员进行创新性设计的比重有所增加.     

机电一体化协同设计平台研究

为了提高机电一体化产品设计中多学科多专业设计人员协同设计的效率,利用CAX软件,基于Web的项目组级PDM,UML技术,虚拟现实样机技术搭建机电一体化协同设计平台,来满足机电一体化多学科的高效协同设计。重点讨论了组成该协同设计平台的基于Web的项目组级PDM服务器和完成各种设计任务的工作站群,以及该平台的运作过程。该平台的试验性运行结果表明,该机电一体化协同设计平台能够提高设计团队多学科多专业的设计人员协同设计效率,并且在设计过程中,设计人员进行创新性设计的比重有所增加。     

Ordinal optimisation of genetic control parameters for flow shop scheduling

Genetic algorithm (GA) has been widely applied to many non-polynomial hard optimisation problems, such as flow shop and job shop scheduling. It is well known that the efficiency and effectiveness of GA highly depend on its control parameters, but even setting suitable parameters often suffers from tedious trial and error. Currently, setting optimal parameters is still an open problem and one of the most important and promising areas for GA. In this paper, the determination of optimal GA control parameters with limited computational effort and total simulation replication constraint, namely, population size, crossover and mutation probabilities, is firstly formulated as a stochastic optimisation problem. Ordinal optimisation and optimal computing budget allocation are then applied to select the optimal GA control parameters while providing reasonable performance evaluation for hard flow shop scheduling problems. Lastly the effectiveness of the methodology is demonstrated by simulation results based on benchmarks.