A genetic algorithm application for sequencing operations in process planning for parallel machining

Parallel numerically controlled machines can perform multiple machining operations simultaneously using combinations of interacting workholding and tool holding devices. One type of parallel machine, the Mill/Turn, also has the ability to perform both turning and milling operations in the same setup. These machines, in addition to being suitable for large volume machining, also have the potential for efficiently producing small batch sizes. Consequently, Mill/Turns can be used as a rapid prototyping tool. One of the major hurdles to integrating Mill/Turns into manufacturing environments is the absence of computer-aided processing planning systems. This problem is more acute in the parallel-machining domain because process plans for parallel machining are more complicated than their sequential counterparts. In this paper we discuss various aspects of parallel machining that influence the generation of process plans, and describe a process planner that uses a genetic algorithm for sequencing operations. Implementation results are also included.     

Part-machine cell formation in group technology using a simulated annealing-based meta-heuristic

The part-machine cell formation problem (PMCFP) is a crucial step in the design of a cellular manufacturing system and has received considerable research attention over the last five decades. This study proposes a simulated annealing-based meta-heuristic for solving the PMCFP. The effectiveness of the proposed approach is compared to conventional algorithms across a set of PMCFPs available in the literature. Computational results using four types of performance measures show that the proposed simulated annealing-based meta-heuristic is highly effective by comparison with conventional algorithms for PMCFPs on the same test problems.     

Product portfolio planning: a metaheuristic-based simulated annealing algorithm

Product portfolio diversity is prominent for customers, but critical for manufacturers. From the manufacturers’ perspective, diversity must be maintained at a level where engineering costs do not exceed the acquired advantages of increased market share. Therefore, in this paper we consider the problem of product portfolio planning to simultaneously maximise market share and minimise manufacturing engineering costs. Since this problem belongs to the NP-hard class of problems, exact algorithms are incapable of rendering an optimal solution. Therefore, we used metaheuristic-based simulated annealing to deal with the problem. Our proposed algorithm consists of two parts, i.e. a random search and a predetermined rule to generate the next possible neighbours (product portfolio). In order to have a robust algorithm, we calibrated different levels of our problem's parameters using the Taguchi method. This method picks the best levels of different parameters, conducting the least possible experiments. To evaluate the performance of our proposed algorithm, we compared it with a strong algorithm – the genetic algorithm. We used this comparison as the basis of our research. The obtained computational results clearly demonstrate the efficiency and effectiveness of our proposed algorithm.     

Metric-driven mesh optimization using a local simulated annealing algorithm

We report on results obtained with a metric-driven mesh optimization procedure for simplicial meshes based on the simulated annealing (SA) method. The use of SA improves the chances of removing pathological clusters of bad elements, that have the tendency to lock into frozen configurations in difficult regions of the model such as corners and complex face intersections, prejudicing the overall quality of the final grid. A local version of the algorithm is developed that significantly lowers the computational cost. Numerical examples illustrate the effectiveness of the proposed methodology, which is compared to a classical greedy Gauss–Seidel optimization. Substantial improvement in the quality of the worst elements of the grid is observed for the local simulated annealing optimization. Furthermore, the method appears to be robust to the choice of the algorithmic parameters. Copyright © 2006 John Wiley & Sons, Ltd.     

A boundary-integral algorithm for adaptive motion planning

An algorithm is developed for adaptively computing the path of an object in a domain with arbitrary geometry in the presence of stationary or arbitrarily moving obstacles. The methodology hinges on an analogy with heat transfer where hard walls and obstacles are cold surfaces while targeted positions such as doorways or laboratory benches are hot surfaces. At any instant, the navigator moves in a direction that maximizes the rate of inward heat transport so as to get warmer. A central part of the algorithm is the fast solution of the heat transfer problem using a boundary-element method. Illustrative examples are provided for stationary and moving obstacles in sample domain geometries.     

A knowledge-based approach for automated process planning

Automated process planning (APP) is a critical interface to both design and manufacturing. Several evolving systems have employed artificial intelligence (AI) procedures to capture the basic logic used by a process planner. However, no effort has been directed to systemize the knowledge in the field of process planning. In this paper process planning knowledge extraction and formalization will be discussed. A framework for the development of intelligent APP systems capable of learning through user interaction is also presented.     

基于SA算法的行星齿轮减速器离散变量优化设计

考虑制造工艺要求,将所有设计变量均视为离散变量,包括一般离散变量和伪离散变量,并就这两种情况下状态产生函数的设计原理进行深入研究,解决了将模拟退火算法用于离散变量函数优化的关键技术问题,介绍了一种基于模拟退火算法的离散变量函数优化的新方法。行星齿轮传动中各齿轮的齿数受传动比条件、同轴条件和装配条件的限制而不能任意取值,齿轮的模数也要受国家标准的制约只能取一些离散值,用以数学规划理论为基础的经典约束优化方法求解效果很差,用基于模拟退火算法的离散变量优化设计方法则可以方便快捷地获得满足各方面要求的最优设计方案。     

基于改进模拟退火算法的复合材料层合板频率优化

针对复合材料层合板频率优化问题,结合可行规则法和直接搜索模拟退化算法,提出了一种自适应模拟退火(SA)改进算法。层合板优化目标是基频、频率带隙以及给定基频和带隙约束的层合板厚度。设计变量包括铺层角度和铺层数两种离散变量。改进算法的自适应新点产生模块采用依赖温度的动态调整搜索半径,改善了直接搜索模拟退化(DSA)算法易陷入局部极值的缺陷,而可行规则法的引入提高了SA算法求解约束问题的效率和简易性。采用Ritz法进行频率响应分析以考虑弯扭耦合影响。不同铺层数、角度增量和长宽比时的层合板3类算例结果显示:改进算法能有效求解层合板频率优化,可获得更多或更好的铺层顺序全局优化解。     

Efficient hybrid evolutionary algorithm for optimization of a strip coiling process

This article proposes an efficient metaheuristic based on hybridization of teaching–learning-based optimization and differential evolution for optimization to improve the flatness of a strip during a strip coiling process. Differential evolution operators were integrated into the teaching–learning-based optimization with a Latin hypercube sampling technique for generation of an initial population. The objective function was introduced to reduce axial inhomogeneity of the stress distribution and the maximum compressive stress calculated by Love's elastic solution within the thin strip, which may cause an irregular surface profile of the strip during the strip coiling process. The hybrid optimizer and several well-established evolutionary algorithms (EAs) were used to solve the optimization problem. The comparative studies show that the proposed hybrid algorithm outperformed other EAs in terms of convergence rate and consistency. It was found that the proposed hybrid approach was powerful for process optimization, especially with a large-scale design problem.     

A parallel particle swarm optimization algorithm for multi-objective optimization problems

In this article, a new proposal of using particle swarm optimization algorithms to solve multi-objective optimization problems is presented. The algorithm is constructed based on the concept of Pareto dominance, as well as a state-of-the-art ‘parallel’ computing technique that intends to improve algorithmic effectiveness and efficiency simultaneously. The proposed parallel particle swarm multi-objective evolutionary algorithm (PPS-MOEA) is tested through a variety of standard test functions taken from the literature; its performance is compared with six noted multi-objective algorithms. The computational experience gained from the first two experiments indicates that the algorithm proposed in this article is extremely competitive when compared with other MOEAs, being able to accurately, reliably and robustly approximate the true Pareto front in almost every tested case. To justify the motivation behind the research of the parallel swarm structure, the computational results of the third experiment confirm the PPS-MOEA's merit in solving really high-dimensional multi-objective optimization problems.     

A CAD/CAM system for process planning and optimization in LOM (Laminated Object Manufacturing)

Rapid Prototyping (RP) technologies have emerged as a powerful set of manufacturing technologies in recent years. While these technologies invariably provide tremendous time savings over traditional methods for the manufacture of design prototypes, most are still quite inefficient. This paper proposes two ideas: (i), that these processes can be significantly optimized by using better process planning; and (ii), that several of these technologies use similar core planning technologies for optimization. The first hypothesis is verified in this paper by presenting an improved process planning system for one RP technology, Laminated Object Manufacturing (LOM). The second observation led us to develop an open architecture planning system for a host of RP technologies. A testbed software system using these ideas has been developed and is presented in this paper. While the methodologies developed can work with the current industry standard STL format for storing object CAD data, the software is planned purely to work using exact solid models and direct slicing methods.     

Optimal process planning for a combined punch-and-laser cutting machine using ant colony optimization

A machine that performs both punching and laser-cutting operations is referred as combined punch-and-laser machine. Such a machine has been in the market for about two decades. Although process-planning tools have been used on such a combined machine, the optimization of process planning dedicated to combined machines, based on our literature search results, has never been directly studied. This work addresses the process-planning problem for the combined punch-and-laser machine by integrating knowledge, quantitative analysis, and numerical optimization approaches. The proposed methodology helps making decisions on following issues: (i) which type of operation should be applied to each feature, and (ii) what is the optimal operation sequence (tool path) to achieve the maximum manufacturing efficiency. The ant colony optimization (ACO) algorithms are employed in searching the optimal tool path. Sensitivities of control parameters of ACO are also analysed. Through applications, the proposed method can significantly improve the operation efficiency for the combined punch-and-laser machine. The method can also be easily automated and integrated with the nesting and G-code generation processes. Some issues and possible future research topics have also been discussed.     

A simulated annealing approach for optimization of multi-pass turning operations

In this paper, an optimization algorithm based on the simulated annealing (SA) algorithm and the Hooke-Jeeves pattern search (PS) is developed for optimization of multi-pass turning operations. The cutting process is divided into multi-pass rough machining and finish machining. Machining parameters are determined to optimize the cutting conditions in the sense of the minimum unit production cost under a set of practical machining constraints. Experimental results indicate that the proposed nonlinear constrained optimization algorithm, named SA/PS, is effective for solving complex machining optimization problems. The SA/PS algorithm can be integrated into a CAPP system for generating optimal machining parameters.     

A V-detector optimization algorithm

针对人工免疫系统中V-detector否定选择算法造成的检测器集合黑洞和检测器高重叠率等问题,借鉴生物免疫系统对免疫细胞的调节机制,提出了V-detector优化算法.该算法从父代产生候选检测器子代并通过检测器之间以及检测器与自体集合之间的亲和力对比更新检测器集合,使得检测器集合对非自体空间的覆盖更加合理.通过二维仿真实验和KDD CUP 99数据集实验测试,经优化后的检测器集合对非自体空间的覆盖性能有了显著提高,有效提高了系统的检测性能.     

Feature-based design for process planning of machining processes with optimization using genetic algorithms

The development of a feature-based design environment that can be applied in the concept-to-manufacturing stages of the machining process is explained. It is broadly divided into four modules, namely, feature-based design (FBD) environment, virtual factory environment (VFE), operation-based feature mapping (OBFM) and optimization using genetic algorithms (GA). The feature-based design environment module is used for the design, modelling, synthesis, representation and validation of the components for machining application. It uses integrated features, which are predefined as feature templates in the feature library. While instancing these integrated features, they get/derive the information required for the design, modelling, process planning and manufacturing stages of the components as their attributes, from the user/knowledge base. After creating the component, integrated features present in it are validated with respect to its application, namely machining process. The VFE module defines the mathematical model of the factory in the computer, which provides the database for operations, machines, cutting tools, work pieces, etc. The knowledge base maps validated features of the component into operation sets in the first phase of the OBFM stage. Each operation in the operation sets can be carried out using different machines and cutting tools in the factory. All these possible choices are obtained in the second phase of OBFM. GA is used to find the optimal sequence of operations, machines and cutting tools for different criteria. Provisions are also available to generate NC codes for operations, which are to be carried out with NC or CNC machines, if selected. Thus, the optimal process plan for the selected criteria with respect to the given factory environment is found for the modelled component. The feature-based design system developed is built on existing CAD, programming and spread-sheet software tools, namely CATIA®, MS-Visual Basic® and MS-Excel®, which not only save developmental effort, but also make full use of the functionalities of these commercial softwares. This paper explains the developed system with a case study.     

A green productivity based process planning system for a machining process

In the metal cutting industry, manufacturers have strived to increase energy efficiency and to reduce environmental burdens through the use of dust collectors and waste disposers. It is more beneficial and efficient to apply the front-of-pipe technology that prevents the sources of pollutants and minimises energy use through the redesign of products and the change of process planning and machining operations. In particular, process planning for the environment, called eco-process planning, is central to increasing energy efficiency and reducing environmental burdens because process planning decisions greatly influence machining performance. At present, greenability, a term used to indicate environmental friendliness, has been little considered as a major concern in the process planning stage because process planning decisions have focused on improving productivity aspects that include speed, cost and quality. Thus, it is essential to develop an eco-process planning approach that enables the harmonisation and enhancement of greenability performance while improving productivity performance, termed green productivity (GP). This paper presents the development of a GP-based process planning algorithm that enables the derivation of process parameters for improving GP in machining operations. The core mechanism of the algorithm is the realisation of the process improvement cycle that measures GP performance by the collection of machining data, quantifies this performance by categorical representation and predicts the performance through prediction models. To show the feasibility and applicability of the proposed algorithm, we have conducted an experiment and implemented a prototype system for a turning machining process.     

A new optimization algorithm for solving complex constrained design optimization problems

This article presents the performance of a very recently proposed Jaya algorithm on a class of constrained design optimization problems. The distinct feature of this algorithm is that it does not have any algorithm-specific control parameters and hence the burden of tuning the control parameters is minimized. The performance of the proposed Jaya algorithm is tested on 21 benchmark problems related to constrained design optimization. In addition to the 21 benchmark problems, the performance of the algorithm is investigated on four constrained mechanical design problems, i.e. robot gripper, multiple disc clutch brake, hydrostatic thrust bearing and rolling element bearing. The computational results reveal that the Jaya algorithm is superior to or competitive with other optimization algorithms for the problems considered.     

A new simulated annealing algorithm for the facility layout problem

In this paper we present an application of simulated annealing to facility layout problems with single and multiple floors. The facility layout problem is highly combinatorial in nature and generally exhibits many local minima. These properties make it a suitable candidate for simulated annealing. Using a new candidate layout generation routine and spacefilling curves, we develop an improvement-type layout algorithm based on simulated annealing that considers an expanded set of department exchanges. The resulting algorithm achieves low-cost solutions that are much less dependent on the initial layout than other approaches. We compare the performance of the simulated-annealing based algorithm with both steepest-descent and randomized approaches from the literature. Unlike other simulated annealing papers which typically present a statistical experiment to evaluate the effect of numerous control settings, all the experiments presented in this paper were conducted with control settings that are constant or easily specified. This approach facilitates the application of the proposed algorithm to real-life facility layout problems in both single and multiple floor facilities. Although the algorithm presented here can be applied to many types of facilities, our primary focus is on production facilities.     

A novel metaheuristic for continuous optimization problems: Virus optimization algorithm

A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called ‘antivirus’) to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.