Interactive robust optimal design of plastic injection products with minimum weldlines

A new robust optimal design methodology has been developed and applied to the design of plastic injection molding products. Taguchi’s robust design method and an optimal design search algorithm are integrated with a commercial CAE simulation tool. A direct search-based optimization procedure is implemented with the considerations of process variations as well as uncontrollable noise variables. The Interactive Design Space Reduction Method (IDSRM) based on orthogonal arrays for design of experiments is developed as a general optimization tool. Using the system, designers can interactively adjust the design space during the searching process for the optimal solution based on the outcomes of the experiments. The developed methodology is applied to an industrial application: a molding process design of an automobile front bumper to minimize the weldline, a form defect of plastic parts. Compared with the initial design solution, the optimized design aided by the proposed methodology shows a more efficient and better result in terms of design robustness against process variations.     

Robust metaheuristics for group scheduling with sequence-dependent setup times in hybrid flexible flow shops

This paper considers group scheduling problem in hybrid flexible flow shop with sequence-dependent setup times to minimize makespan. Group scheduling problem consists of two levels, namely scheduling of groups and jobs within each group. In order to solve problems with this context, two new metaheuristics based on simulated annealing (SA) and genetic algorithm (GA) are developed. A design procedure is developed to specify and adjust significant parameters for SA- and GA-based metaheuristics. The proposed procedure is based on the response surface methodology and two types of objective function are considered to develop multiple-objective decision making model. For comparing metaheuristics, makespan and elapsed time to obtain it are considered as two response variables representing effectiveness and efficiency of algorithms. Based on obtained results in the aspect of makespan, GA-based metaheuristic is recommended for solving group scheduling problems in hybrid flexible flow shop in all sizes and for elapsed time SA-based metaheuristic has better results.     

Designing integrated cellular manufacturing systems with scheduling considering stochastic processing time

This paper addresses a new mathematical model for cellular manufacturing problem integrated with group scheduling in an uncertain space. This model optimizes cell formation and scheduling decisions, concurrently. It is assumed that processing time of parts on machines is stochastic and described by discrete scenarios enhances application of real assumptions in analytical process. This model aims to minimize total expected cost consisting maximum tardiness cost among all parts, cost of subcontracting for exceptional elements and the cost of resource underutilization. Scheduling problem in a cellular manufacturing environment is treated as group scheduling problem, which assumes that all parts in a part family are processed in the same cell and no inter-cellular transfer is needed. Finally, the nonlinear model will be transformed to a linear form in order to solve it for optimality. To solve such a stochastic model, an efficient hybrid method based on new combination of genetic algorithm (GA), simulated annealing (SA) algorithm, and an optimization rule will be proposed where SA and optimization rule are subordinate parts of GA under a self-learning rule criterion. Also, performance and robustness of the algorithm will be verified through some test problems against branch and bound and a heuristic procedure.     

Optimization of multi-pass turning economies through a hybrid particle swarm optimization technique

Enhancing the performance of manufacturing operations represents a significant goal, especially when cost savings are linked with economies of scale to be exploited. In the area of machining optimization, the selection of optimal cutting parameters subjected to a set of technological constraints plays a key role. This paper presents a novel hybrid particle swarm optimization (PSO) algorithm for minimizing the production cost associated with multi-pass turning problems. The proposed optimization technique consists of a PSO-based framework wherein a properly embedded simulated annealing (SA), namely an SA-based local search, aims both to enhance the PSO search mechanism and to move the PSO away from being closed within local optima. In order to handle the numerous constraints which characterize the adopted machining mathematical model, a constraint violation function integrated with a suitable objective function has been engaged. In addition, a twofold strategy has been implemented to manage the equality constraint between the provided total depth of cut and the number of passes to be performed. Firstly, an accurate problem encoding involving only five cutting parameters has been performed. Secondly, a proper repair procedure that should be run just before any solution evaluation has been engaged. Five different test cases based on the multi-pass turning of a bar stock have been used for comparing the performance of the proposed technique with other existing methods.     

MACHINING PARAMETERS OPTIMIZATION FOR ALUMINA BASED CERAMIC CUTTING TOOLS USING GENETIC ALGORITHM

Alumina-based ceramic cutting tools can be operated at higher cutting speeds than carbide and cermet tools. This results in increased metal removal rates and productivity. While the initial cost of alumina based ceramic inserts is generally higher than carbide or cermet inserts, the cost per part machined is often lower. Production cost is the main concern of the industry and it has to be optimised to fully utilize the advantages of ceramic cutting tools. In this study, optimization of machining parameters on machining S.G. iron (ASTM A536 60-40-18) using alumina based ceramic cutting tools is presented. Before doing the optimization work, experimental machining study is carried out using Ti [C,N] mixed alumina ceramic cutting tool (CC 650) and Zirconia toughened alumina ceramic cutting tool (Widialox G) to get actual input values to the optimization problem, so that the optimized results will be realistic. The optimum machining parameters are found out using Genetic algorithm and it is found that Widialox G tool is able to machine at lower unit production cost than CC 650 tool. The various costs affecting the unit production cost are also discussed.     

MACHINING PARAMETERS OPTIMIZATION FOR ALUMINA BASED CERAMIC CUTTING TOOLS USING GENETIC ALGORITHM

Alumina-based ceramic cutting tools can be operated at higher cutting speeds than carbide and cermet tools. This results in increased metal removal rates and productivity. While the initial cost of alumina based ceramic inserts is generally higher than carbide or cermet inserts, the cost per part machined is often lower. Production cost is the main concern of the industry and it has to be optimised to fully utilize the advantages of ceramic cutting tools. In this study, optimization of machining parameters on machining S.G. iron (ASTM A536 60-40-18) using alumina based ceramic cutting tools is presented. Before doing the optimization work, experimental machining study is carried out using Ti [C,N] mixed alumina ceramic cutting tool (CC 650) and Zirconia toughened alumina ceramic cutting tool (Widialox G) to get actual input values to the optimization problem, so that the optimized results will be realistic. The optimum machining parameters are found out using Genetic algorithm and it is found that Widialox G tool is able to machine at lower unit production cost than CC 650 tool. The various costs affecting the unit production cost are also discussed.     

Multi-objective optimization of multipass turning processes

In this paper, a methodology is proposed for the multi-objective optimization of a multipass turning process. A real-parameter genetic algorithm (RGA) is used for minimizing the production time, which provides a nearly optimum solution. This solution is taken as the initial guess for a sequential quadratic programming (SQP) code, which further improves the solution. Thereafter, the Pareto-optimal solutions are generated without using the cost data. For any Pareto-optimal solution, the cost of production can be calculated at a higher level for known cost data. An objective method based on the linear programming model is proposed for choosing the best among the Pareto-optimal solutions. The entire methodology is demonstrated with the help of an example. The optimization is carried out with equal depths of cut for roughing passes. A simple numerical method has been suggested for estimating the expected improvement in the optimum solution if an unequal depth of cut strategy would have been employed.     

Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization

In this paper, we introduce a procedure to formulate and solve optimization problems for multiple and conflicting objectives that may exist in turning processes. Advanced turning processes, such as hard turning, demand the use of advanced tools with specially prepared cutting edges. It is also evident from a large number of experimental works that the tool geometry and selected machining parameters have complex relations with the tool life and the roughness and integrity of the finished surfaces. The non-linear relations between the machining parameters including tool geometry and the performance measure of interest can be obtained by neural networks using experimental data. The neural network models can be used in defining objective functions. In this study, dynamic-neighborhood particle swarm optimization (DN-PSO) methodology is used to handle multi-objective optimization problems existing in turning process planning. The objective is to obtain a group of optimal process parameters for each of three different case studies presented in this paper. The case studies considered in this study are: minimizing surface roughness values and maximizing the productivity, maximizing tool life and material removal rate, and minimizing machining induced stresses on the surface and minimizing surface roughness. The optimum cutting conditions for each case study can be selected from calculated Pareto-optimal fronts by the user according to production planning requirements. The results indicate that the proposed methodology which makes use of dynamic-neighborhood particle swarm approach for solving the multi-objective optimization problems with conflicting objectives is both effective and efficient, and can be utilized in solving complex turning optimization problems and adds intelligence in production planning process.     

Selection of machining datum and allocation of tolerance through tolerance charting technique

Tolerance charting is an effective tool to determine the optimal allocation of working dimensions and working tolerances such that the blueprint dimensions and tolerances can be achieved to accomplish the cost objectives.The selection of machining datum and allocation of tolerances are critical in any machining process planning as they directly affect any setup methods/machine tools selection and machining time.This paper mainly focuses on the selection of optimum machining datums and machining tolerances simultaneously in process planning.A dynamic tolerance charting constraint scheme is developed and implemented in the optimization procedure.An optimization model is formulated for selecting machining datum and tolerances and implemented with an algorithm namely Elitist Non-Dominated Sorting Genetic Algorithm(NSGA-II).The computational results indicate that the proposed methodology is capable and robust in finding the optimal machining datum set and tolerances.     

A constraint-based inference system for satisfying design constraints

We propose an efficient algorithm for the purpose of satisfying a wide range of design constraints represented with equality and inequality equations as well as production rules. The algorithm employs simulated-annealing and a production rule inference engine and works on design constraints represented with networks. The algorithm fulfills equality constraints through constraint satisfaction processes like variable elimination while taking into account inequality constraints and inferring production rules. It can also reduce the load of the optimization procedure if necessary. We demonstrate the implementation of the algorithm with the result on machine tool design.     

复杂曲面环形刀五轴端铣加工的刀具轨迹规划方法

为了降低复杂曲面类零部件加工的刀具路径,减小刀具路径条数,提高加工效率,提出了一种新的复杂曲面环形刀五轴端铣加工刀具轨迹优化方法。在局部可铣性基础上对刀轴矢量角进行自适应优化,采用新型加工带宽计算方法——等残留高度算法,给出了等残留高度算法的刀具轨迹生成具体步骤。仿真结果表明:与传统等残留高速算法相比,刀具轨迹优化算法的刀具路径更短、条数更少,能够有效提高复杂曲面加工效率。     

Optimization of multi-pass face milling using a fuzzy particle swarm optimization algorithm

In this paper, a simple methodology to distribute the total stock removal in each of the rough passes and the final finish pass and a fuzzy particle swarm optimization (FPSO) algorithm based on fuzzy velocity updating strategy to optimize the machining parameters are proposed and implemented for multi-pass face milling. The optimum value of machining parameters including number of passes, depth of cut in each pass, speed, and feed is obtained to achieve minimum production cost while considering technological constraints such as allowable machine power, machining force, machining speed, tool life, feed rate, and surface roughness. The proposed FPSO algorithm is first tested on few benchmark problems taken from the literature. Upon achieving good results for test cases, the algorithm was employed to two illustrative case studies of multi-pass face milling. Significant improvement is also obtained in comparison to the results reported in the literatures, which reveals that the proposed methodology for distribution of the total stock removal in each of passes is effective, and the proposed FPSO algorithm does not have any difficulty in converging towards the true optimum. From the given results, the proposed schemes may be a promising tool for the optimization of machining parameters.     

Optimization of rotations for six-axis machining

Five-axis machines with three translational and two rotation axes are becoming increasingly popular in serving the needs of the mass production industry due to their ability to handle geometrically complex workpieces using the rotational axes. Theoretically, the combination of the five axes offers a minimal number of the degrees of freedom required to transport the tool into a prescribed spatial position and establish a required orientation. However, the rotation axes lead to an inevitable nonlinearity of the tool tip trajectory and the so-called kinematics errors appearing due to the specific kinematics of the machine. Eventually, one arrives at an interesting question. Is it possible to compensate this error by introducing an additional rotation axis? In other words, ??does an additional rotation axis offer any optimization benefits in the sense of the above mentioned error??? In this paper, we answer this question positively by analyzing a hypothetical six-axis milling machine with two rotation axes on the table and one additional rotation axis on the tool. The sixth axis is build on the top of the existing five-axis machine MAHO600E by Deckel Gildemeister. We present an extension of an optimization algorithm developed earlier by the authors for five-axis machining based on an optimal angle sequencing (the shortest path optimization). The extension is a combination of the shortest path strategy and the use of the additional axis. The algorithm leads to an increase in the machining accuracy, in particular, for rough milling. Numerical experiments and cutting by a virtual six-axis machine built in Vericut 5.0 validates the results of the optimization. The proposed optimization procedure is capable of upgrading the existing five-axis G-codes to the case of six-axis machine.     

基于最早开始时间的项目进度关键链搜索算法

针对项目进度优化调度问题,提出了一种面向资源受限约束的关键链搜索算法(ESCCPM)。当存在资源冲突时,该算法以最早开始时间优先为原则,对项目资源进行分配,并根据资源标记修改有资源冲突工序的紧前工序集。由于修改后的紧前工序集合中不仅包含由时序关系确定的工序,还包含由资源关联确定的工序,证明了该算法能够建立项目最优关键链。最后通过算例分析说明了算法执行过程,并与其他资源分配方法对比,验证了算法的有效性和正确性。     

基于不可微问题优化的四面体网格光顺算法

提出一种基于不可微问题优化的四面体网格光顺算法。针对四面体网格光顺的最小最大约束优化问题,应用一类不可微优化问题的有效解法,提出与不可微目标函数等价的可微目标函数,进一步转化为无约束极小优化问题,进而调用现有的优化程序库进行网格优化。该算法实现了多点并发优化技术,能够有效地实现四面体网格的质量优化,特别是能够有效地解决非孤立劣质单元优化问题。算例表明,该算法计算效率高,且易于实现,能够优化得到较高质量的四面体网格。     

Real-coded genetic algorithm for stochastic optimization: A tool for recipe qualification of semiconductor manufacturing under noisy environments

This paper considers an interesting topic of recipe qualification during each fabrication step of semiconductor manufacturing. In particular, this type of stochastic optimization scheme within a discrete-event simulation process is discussed and a new optimization method, with the capability to anchor robust recipes for batch processing, is proposed, implemented and evaluated. A particular real-coded genetic algorithm (GA) suitable for resolving continuous optimization problems in noisy environments is developed. Four test functions appearing in the literature are used as a test-bed to assess the proposed genetic algorithm via a variety of experimental studies, showing that the new method can produce much more accurate estimates of the true optimum points than the other two optimization procedures, the Nelder-Mead (NM) simplex search procedure and a well-known variant (RS+S9) of Nelder-Mead suitable for stochastic optimization. As such, the new method could serve as a useful tool for process recipe optimization in noisy semiconductor manufacturing environments. Finally, the chemical mechanical planarization (CMP) process, a turnkey process during semiconductor fabrication, is simulated from batch to batch based on the real-data equipment model and the presented algorithm is employed to seek the optimal recipe profile while processing each batch of wafers sequentially through the CMP tool.     

成批生产计划调度的集成建模与优化

针对多品种批量生产类型,建立了调度约束的生产计划与调度集成优化模型。模型的目标函数是使总调整费用、库存费用及生产费用之和最小,约束函数包括库存平衡约束和生产能力约束,同时考虑了调度约束,即工序顺序约束和工件在单机上的加工能力约束,保证了计划可行性。该模型为两层混合整数规划模型,对其求解综合运用了遗传算法和启发式规则,提出了混合启发式求解算法。最后,针对某机床厂多品种批量生产类型车间进行了实例应用,对车间零件月份作业计划进行分解,得到各工段单元零件周作业计划,确定了零件各周生产批量与投产顺序。     

基于空间填充法的刀具路径生成算法及其仿真

针对空间填充曲线法的网格只具有正则矩形,生成的刀具路径存在频繁转向、长度冗长、生成时间长等缺点,提出一种基于空间填充法的刀具路径生成算法。选用T样条曲面为造型曲面,生成具有非正则矩形的网格并进行回路的规划;用改进的Hamiltonian算法初步生成刀具路径;用改进的倒圆角算法进行拐角优化,获得最终的刀具路径。开发出了基于该算法的仿真系统,对算法进行了仿真验证和实际的加工实验,结果表明所提出算法有效可行,生成的刀具路径长度及生成时间都得到缩短。     

RTDS implementation of an improved sliding mode based inverter controller for PV system

This paper proposes a novel approach for testing dynamics and control aspects of a large scale photovoltaic (PV) system in real time along with resolving design hindrances of controller parameters using Real Time Digital Simulator (RTDS). In general, the harmonic profile of a fast controller has wide distribution due to the large bandwidth of the controller. The major contribution of this paper is that the proposed control strategy gives an improved voltage harmonic profile and distribute it more around the switching frequency along with fast transient response; filter design, thus, becomes easier. The implementation of a control strategy with high bandwidth in small time steps of Real Time Digital Simulator (RTDS) is not straight forward. This paper shows a good methodology for the practitioners to implement such control scheme in RTDS. As a part of the industrial process, the controller parameters are optimized using particle swarm optimization (PSO) technique to improve the low voltage ride through (LVRT) performance under network disturbance. The response surface methodology (RSM) is well adapted to build analytical models for recovery time (R_t), maximum percentage overshoot (MPOS), settling time (T_s), and steady state error (E_ss) of the voltage profile immediate after inverter under disturbance. A systematic approach of controller parameter optimization is detailed. The transient performance of the PSO based optimization method applied to the proposed sliding mode controlled PV inverter is compared with the results from genetic algorithm (GA) based optimization technique. The reported real time implementation challenges and controller optimization procedure are applicable to other control applications in the field of renewable and distributed generation systems.     

Optimization of cutting parameters in micro end milling operations in dry cutting condition using genetic algorithms

Micro end milling with fine grained carbide end mills is an economical way to machine small and medium lots of micro components. Considering the sensitivity of the slender end mills which are very costly compared to conventional end mills, it is imperative that the machining parameters are chosen appropriately so as to ensure maximum tool life and minimum possible production cost without violating any of the imposed constraints. Unlike in conventional end milling operations the tool life in micro end milling operations increases with axial depth of cut till it equals the tool diameter and this makes it even difficult to ascertain the appropriate depth of cut to machine a specific component. In this paper the influence of depth of cut on tool life is illustrated and depth of cut is also considered as one of the decision variables in the optimization problem. More over in this paper Genetic Algorithms (GA) based on natural laws of evolution is used to optimize the cutting parameters. Finally a test case is presented to give a clear picture of the application of the optimization algorithm.