Application of genetic algorithm to computer-aided process planning in preliminary and detailed planning

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in the computer integrated manufacturing (CIM) environment. A good process plan of a part is built up based on two elements: (1) optimized sequence of the operations of the part; and (2) optimized selection of the machine, cutting tool and tool access direction (TAD) for each operation. On the other hand, two levels of planning in the process planning is suggested: (1) preliminary and (2) secondary and detailed planning. In this paper for the preliminary stage, the feasible sequences of operations are generated based on the analysis of constraints and using a genetic algorithm (GA). Then in the detailed planning stage, using a genetic algorithm again which prunes the initial feasible sequences, the optimized operations sequence and the optimized selection of the machine, cutting tool, and TAD for each operation are obtained. By applying the proposed GA in two levels of planning, the CAPP system can generate optimal or near-optimal process plans based on a selected criterion. A number of case studies are carried out to demonstrate the feasibility and robustness of the proposed algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature for most problems. The main contribution of this work is to emerge the preliminary and detailed planning, implementation of compulsive and additive constraints, optimization sequence of the operations of the part, and optimization selection of machine, cutting tool and TAD for each operation using the proposed GA, simultaneously.     

Learning-based multi-pass adaptive scheduling for a dynamic manufacturing cell environment

Because the essential attributes are uncertain in a dynamic manufacturing cell environment, to select a near-optimal subset of manufacturing attributes to enhance the generalization ability of knowledge bases remains a critical, unresolved issue for classical artificial neural network-based (ANN-based) multi-pass adaptive scheduling (MPAS). To resolve this problem, this study develops a hybrid genetic /artificial neural network (GA/ANN) approach for ANN-based MPAS systems. The hybrid GA/ANN approach is used to evolve an optimal subset of system attributes from a large set of candidate manufacturing system attributes and, simultaneously, to determine configuration and learning parameters of the ANN according to various performance measures. In the GA/ANN-based MPAS approach, for a given feature subset and the corresponding topology and learning parameters of an ANN decoded by a GA, an ANN was applied to evaluate the fitness in the GA process and to generate the MPAS knowledge base used for adaptive scheduling control mechanisms. The results demonstrate that the proposed GA/ANN-based MPAS approach has, according to various performance criteria, a better system performance over a long period of time than those obtained with classical machine learning-based MPAS approaches and the heuristic individual dispatching rules.     

Hierarchically merging plans in decomposable domains

Recent works in domain-independent plan merging have shown that the optimal plan merging problem is NP-hard, and heuristic algorithms have been proposed to generate near-optimal solutions. We have developed a plan merging methodology that merges partial-order plans based on the notion of plan fragments. In contrast to previous works, mergeability classes no longer necessarily form a partition over the set of actions in the input plans. This methodology applies to a class of planning domains which are decomposable. We also investigate the previously unexplored notion of alternative actions in domain-independent plan merging, which can improve the quality of the resulting merged plan, and a novel approach is presented for removing cyclic dependencies that may result during the plan merging process. We provide theoretical analyses of several algorithms for computing the minimum cost cover of plan fragments, a central component of the methodology. We support the theoretical analysis of these algorithms with experimental data to show that a greedy approach is near-optimal and efficient     

Feature-based generation of machining process plans for optimised parts manufacture

Efficacious integration of such CAx technologies as CAD, CAM and CAPP still remains a problem in engineering practice which constantly attracts research attention. Design by feature model is assumed as a main factor in the integration effort in various engineering and manufacturing domains. It refers principally to feature clustering and consequently operation sequencing in elaborated process plan designs. The focus of this paper is on CAPP for parts manufacture in systems of definite processing capabilities, involving multi-axis machining centres. A methodical approach is proposed to optimally solve for process planning problems, which consists in the identification of process alternatives and sequencing adequate working steps. The approach involves the use of the branch and bound concept from the field of artificial intelligence. A conceptual scheme for generation of alternative process plans in the form of a network is developed. It is based on part design data modelling in terms of machining features. A relevant algorithm is proposed for creating such a network and searching for the optimal process plan solution from the viewpoint of its operational performance, under formulated process constraints. The feasibility of the approach and the algorithm are illustrated by a numerical case with regard to a real application and diverse machine tools with relevant tooling. Generated process alternatives for complex machining with given systems, are studied using models programmed in the environment of Matlab^® software.     

Acceptance sampling design by computer — Optimality concerns

There is a great deal of literature dealing with the use of the computer in designing acceptance sampling plans. The general approach is to use some approximation technique to generate minimum sample size plans whose OC curves will approximate the desired Producer's Risk and Consumer's Risk levels. Since only rarely will a single such approximation satisfy both (1-) and β requirements some means, either averaging or selection, is used to select a plan. Plans so determined are acceptable but often not optimal, and planes with significantly smaller sample size may exist which are very close to optimal.

This paper reports the development and use of a computer program which may be used to design single sampling plans using either the binomial or Poisson distribution. The program also finds alternate plans with smaller sample size, and gives a measure of the proximity of such alternate plan to optimality. Some rudimentary artificial intelligence techniques are employed in the search and selection of optimal plans and the near-optimal alternative plans.

An extended version of the program also supports experimentation with a variety of criteria of optimality for the selection of candidate plans from those generated.

The main program is written for use under MS/PC-DOS in both Turbo Pascal and Turbo C. The extended version uses only Pascal.     

复杂产品3D-CAPP中工艺方案仿真关键技术与平台研究*

现有的CAPP侧重于工艺方案的规划,方案是否正确与最优则需通过实践检验。为保证CAPP所规划的工艺方案是合理及最优的,提出了在现有CAPP系统中集成用于模拟检验的可进行几何仿真与部分物理仿真的加工过程仿真部分,根据规划的工艺方案模拟加工过程,并根据仿真结果来修正工艺方案,从而保证规划出来的工艺方案合理及最优。基于VC与OpenGL开发了一个原型仿真系统,实现了部分加工过程仿真功能。     

Using genetic algorithms in process planning for job shop machining

This paper presents a novel computer-aided process planning model for machined parts to be made in a job shop manufacturing environment. The approach deals with process planning problems in a concurrent manner in generating the entire solution space by considering the multiple decision-making activities, i.e., operation selection, machine selection, setup selection, cutting tool selection, and operations sequencing, simultaneously. Genetic algorithms (GAs) were selected due to their flexible representation scheme. The developed GA is able to achieve a near-optimal process plan through specially designed crossover and mutation operators. Flexible criteria are provided for plan evaluation. This technique was implemented and its performance is illustrated in a case study. A space search method is used for comparison     

e-CAPP: A distributed collaborative system for internet-based process planning

Advances in distributed technologies have enabled engineers to communicate more effectively, collaborate, obtain, and exchange a wide range of design resources during product development. Shared internet-based virtual environments allow experts in remote locations to analyze a virtual prototype, together and simultaneously in centers in which the product is being developed.This paper presents a system for distributed and collaborative environment which could assist manufacturing enterprises and experts in discussing, suggesting, evaluating and selecting best process plans for family of manufacturing parts. The represented e-CAPP system enables the implementation of expert knowledge in an appropriate knowledge repository. The knowledge from this repository is integrated into intra-company CAPP systems and used while generating process plans for new products. The proposed internet-based collaborative environment, dedicated to distributed process planning, is yet another step in the direction advancing of distributed manufacturing.     

Process parameters optimization for micro end-milling operation for CAPP applications

In Computer Aided Process Planning (CAPP), process parameters selection for the given manufacturing feature is the final activity and it is the key area for research and development. In this work, an attempt has been made to optimize parameters for micro end-milling operation as a part of CAPP system development for micromachining processes using Artificial Intelligence (AI) approach. Genetic Algorithm (GA) has been found to be the robust and efficient tool to solve nonlinear optimization problems involved in process planning. Microfeatures of size 0.7 and 1 mm are considered and polymethyl methacrylate is chosen as the work material due to its potential application in microparts fabrication. Initially, experimental investigation has been carried out to analyze the impact of process conditions such as spindle speed and feed rate on surface roughness and machining time. Further multiobjective optimization for minimization of responses is carried out using GA. Finally, confirmation experiments are carried out to validate the accuracy of GA results. The optimized process parameters are stored in the database and it ensures foolproof parameters for micro end-milling operation for CAPP applications apart from manuals and catalogues. The proposed approach can be repeated for various other end mill features and for different work and tool material combination to ensure a complete parameters selection module for CAPP system applications.     

Integration of product design,process planning,scheduling, and FMS control using XML data representation

An efficient model for communications between CAD, CAPP, and CAM applications in distributed manufacturing planning environment has been seen as key ingredient for CIM. Integration of design model with process and scheduling information in real-time is necessary in order to increase product quality, reduce the cost, and shorten the product manufacturing cycle. This paper describes an approach to integrate key product realization activities using neutral data representation. The representation is based on established standards for product data exchange and serves as a prototype implementation of these standards. The product and process models are based on object-oriented representation of geometry, features, and resulting manufacturing processes. Relationships between objects are explicitly represented in the model (for example, feature precedence relations, process sequences, etc.). The product model is developed using XML-based representation for product data required for process planning and the process model also uses XML representation of data required for scheduling and FMS control. The procedures for writing and parsing XML representations have been developed in object-oriented approach, in such a way that each object from object-oriented model is responsible for storing its own data into XML format. Similar approach is adopted for reading and parsing of the XML model. Parsing is performed by a stack of XML handlers, each corresponding to a particular object in XML hierarchical model. This approach allows for very flexible representation, in such a way that only a portion of the model (for example, only feature data, or only the part of process plan for a single machine) may be stored and successfully parsed into another application. This is very useful approach for direct distributed applications, in which data are passed in the form of XML streams to allow real-time on-line communication. The feasibility of the proposed model is verified in a couple of scenarios for distributed manufacturing planning that involves feature mapping from CAD file, process selection for several part designs integrated with scheduling and simulation of the FMS model using alternative routings.     

基于供应链的制造执行系统

针对以离散型加工装配企业为核心的供应链中各企业间信息共享的实际需求,开发了一种基于供应链的制造执行系统．它涵盖订货计划和制造执行两项主体功能．主机厂通过三层数据架构体系将订货计划或调整计划下达给外协厂,外协厂依据计划进行生产,并通过RFID技术将订单执行情况及时反馈给主机厂,为主机厂的下一步决策提供数据支持．     

An enhanced genetic algorithm for automated assembly planning

Automated assembly planning reduces manufacturing manpower requirements and helps simplify product assembly planning, by clearly defining input data, and input data format, needed to complete an assembly plan. In addition, automation provides the computational power needed to find optimal or near-optimal assembly plans, even for complex mechanical products. As a result, modern manufacturing systems use, to an ever greater extent, automated assembly planning rather than technician-scheduled assembly planning. Thus, many current research reports describe efforts to develop more efficient automated assembly planning algorithms. Genetic algorithms show particular promise for automated assembly planning. As a result, several recent research reports present assembly planners based upon traditional genetic algorithms. Although prior genetic assembly planners find improved assembly plans with some success, they also tend to converge prematurely at local-optimal solutions. Thus, we present an assembly planner, based upon an enhanced genetic algorithm, that demonstrates improved searching characteristics over an assembly planner based upon a traditional genetic algorithm. In particular, our planner finds optimal or near-optimal solutions more reliably and more quickly than an assembly planner that uses a traditional genetic algorithm.     

Robust manufacturing system design using multi objective genetic algorithms,Petri nets and Bayesian uncertainty representation

Decisions involving robust manufacturing system configuration design are often costly and involve long term allocation of resources. These decisions typically remain fixed for future planning horizons and failure to design a robust manufacturing system configuration can lead to high production and inventory costs, and lost sales costs. The designers need to find optimal design configurations by evaluating multiple decision variables (such as makespan and WIP) and considering different forms of manufacturing uncertainties (such as uncertainties in processing times and product demand). This paper presents a novel approach using multi objective genetic algorithms (GA), Petri nets and Bayesian model averaging (BMA) for robust design of manufacturing systems. The proposed approach is demonstrated on a manufacturing system configuration design problem to find optimal number of machines in different manufacturing cells for a manufacturing system producing multiple products. The objective function aims at minimizing makespan, mean WIP and number of machines, while considering uncertainties in processing times, equipment failure and repairs, and product demand. The integrated multi objective GA and Petri net based modeling framework coupled with Bayesian methods of uncertainty representation provides a single tool to design, analyze and simulate candidate models while considering distribution model and parameter uncertainties.     

An enhanced model for the integrated production and transportation problem in a multiple vehicles environment

Solving an integrated production and transportation problem (IPTP) is a very challenging task in semiconductor manufacturing with turnkey service. A wafer fabricator needs to coordinate with outsourcing factories in the processes including circuit probing testing, integrated circuit assembly, and final testing for buyers. The jobs are clustered by their product types, and they must be processed by groups of outsourcing factories in various stages in the manufacturing process. Furthermore, the job production cost depends on various product types and different outsourcing factories. Since the IPTP involves constraints on job clusters, job-cluster dependent production cost, factory setup cost, process capabilities, and transportation cost with multiple vehicles, it is very difficult to solve when the problem size becomes large. Therefore, heuristic tools may be necessary to solve the problem. In this paper, we first formulate the IPTP as a mixed integer linear programming problem to minimize the total production and transportation cost. An efficient genetic algorithm (GA) is proposed next to tackle the problem when it becomes too complicated. The objectives are to minimize total costs, where the costs include production cost and transportation cost, under the environment with backup capacities and multiple vehicles, and to determine an appropriate production and distribution plan. The results demonstrate that the proposed GA model is an effective and accurate tool.     

A constraint-based operation sequencing for a knowledge-based process planning

Process planning is a decision-making process. Decisions on machining operations for a particular feature have to be made on various independent conditions such as which operation should be performed with which tools and under what cutting parameters. An integrated knowledge-based CAPP system called ProPlanner has been developed. The system has five modules namely information acquisition, feature recognition, machining operation planning and tool selection, set-up planning, and operation sequencing. Most process-planning systems do not produce alternative process plans. Usually, a fixed sequence created by a process plan is not necessarily the best possible sequence. Therefore, the aim should be to generate all possible operation sequences and use some optimality criteria to obtain the best sequence for the given operating environment. This paper presents an efficient heuristic algorithm, belongs to the system's operation sequencing module, for finding near-optimal operation sequences from all available process plans in a machining set-up. The costs of the various machining schemes are calculated and the machining scheme with the lowest cost is chosen. All feasible cutting tools are identified for each particular feature and the corresponding machining operations. This process is repeated for all the features in the machining set-up. All possible feature sequence combinations allowed by the current feature constraints are then generated. Appropriate cutting tools are identified and assigned to different operations. The feature sequence with the smallest number of tool changes is adopted.     

A modified genetic algorithm for distributed scheduling problems

Genetic algorithms (GAs) have been widely applied to the scheduling and sequencing problems due to its applicability to different domains and the capability in obtaining near-optimal results. Many investigated GAs are mainly concentrated on the traditional single factory or single job-shop scheduling problems. However, with the increasing popularity of distributed, or globalized production, the previously used GAs are required to be further explored in order to deal with the newly emerged distributed scheduling problems. In this paper, a modified GA is presented, which is capable of solving traditional scheduling problems as well as distributed scheduling problems. Various scheduling objectives can be achieved including minimizing makespan, cost and weighted multiple criteria. The proposed algorithm has been evaluated with satisfactory results through several classical scheduling benchmarks. Furthermore, the capability of the modified GA was also tested for handling the distributed scheduling problems.     

Progressive Parametric Query Optimization

Commercial applications usually rely on pre-compiled parameterized procedures to interact with a database. Unfortunately, executing a procedure with a set of parameters different from those used at compilation time may be arbitrarily sub-optimal. Parametric query optimization (PQO) attempts to solve this problem by exhaustively determining the optimal plans at each point of the parameter space at compile time. However, PQO is likely not cost-effective if the query is executed infrequently or if it is executed with values only within a subset of the parameter space. In this paper we propose instead to progressively explore the parameter space and build a parametric plan during several executions of the same query. We introduce algorithms that, as parametric plans are populated, are able to frequently bypass the optimizer but still execute optimal or near-optimal plans.     

Fuzzy-set-based approach for concurrent constraint set-up planning

Material removal processes are integral parts of many manufacturing systems. They are either primary machining processes or an important part of preparing toolings for subsequent forming and moulding processes. Manufacturing process planning identifies the type of material removal processes and the machining parameters, cutting tools and fixtures needed to generate the features on a part. Previous research in manufacturing process planning has concentrated mainly on the role of features, in the derivation of the associated process and fixture plans. Many computer-aided process planning (CAPP) and computer-aided fixture planning (CAFP) systems derive process and fixture plans from the features on a part, on the basis that these features are context-free. However, manufacturing operations are interdependent processes. In the author's computer-aided set-up planning (CASP) system, a different perspective is adopted. Feature relations form the core of the conceptual structure. These features relations, which are often imprecise, are used in deriving set-up plans. The feature relations, which may be due to geometrical constraints, tolerance requirements, etc., are modelled using fuzzy sets and fuzzy relations. This paper presents the various feature relations considered in the present system and proposes a practical planning algorithm for set-up planning.     

A metamodel-based Monte Carlo simulation approach for responsive production planning of manufacturing systems

Production planning is concerned with finding a release plan of jobs into a manufacturing system so that its actual outputs over time match the customer demand with the least cost. For a given release plan, the system outputs, work in process inventory (WIP) levels and job completions, are non-stationary bivariate time series that interact with time series representing customer demand, resulting in the fulfillment/non-fulfillment of demand and the holding cost of both WIP and finished-goods inventory. The relationship between a release plan and its resulting performance metrics (typically, mean/variance of the total cost and the fill rate) has proven difficult to quantify. This work develops a metamodel-based Monte Carlo simulation (MCS) method to accurately capture the dynamic, stochastic behavior of a manufacturing system, and to allow real-time evaluation of a release plan's performance metrics. This evaluation capability is then embedded in a multi-objective optimization framework to search for near-optimal release plans. The proposed method has been applied to a scaled-down semiconductor fabrication system to demonstrate the quality of the metamodel-based MCS evaluation and the results of plan optimization.     

Integration of process planning and scheduling using chaotic particle swarm optimization algorithm

Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi-objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.