An effective hybrid graph and genetic algorithm approach to process planning optimization for prismatic parts

Computer-aided process planning is an important interface between computer-aided design and computer-aided manufacturing in computer-integrated manufacturing environments. In this paper, the complicated process planning is modeled as a combinatorial optimization problem with constraints, and a hybrid graph and genetic algorithm (GA) approach has been developed. The approach deals with process planning problems in a concurrent manner by simultaneously considering activities such as sequencing operations, selecting manufacturing resources, and determining setup plans to achieve the global optimal objective. Graph theory accompanied with matrix theory, as the basic mathematical tool for operation sequencing, is embedded into the main frame of GA. The precedence constraints between operations are formulated in an operation precedence graph (OPG). The initial population composed of all feasible solutions is generated by an elaborately designed topologic sort algorithm to the OPG. A modified crossover operator guaranteeing only feasible offspring generated is used, two types of mutation strategies are adopted, and a heuristic algorithm is applied to adjust the infeasible plan generated by the mutation operator to the feasible domain. A case study has been carried out to demonstrate the feasibility and efficiency of the proposed approach.     

Application of an efficient modified particle swarm optimization algorithm for process planning

In the modern manufacturing system, many flexible manufacturing system and NC machines are introduced to improve the production efficiency. Therefore, most parts have a large number of flexible process plans. However, a part can use only one process plan in the manufacturing process. So, the process planning problem has become a crucial problem in the manufacturing environment. It is a combinatorial optimization problem to conduct operations selection and operations sequencing simultaneously with various constraints deriving from the practical workshop environment as well as the parts to be processed. It is a NP-hard problem. In order to solve this problem effectively, this paper proposes a novel modified particle swarm optimization (PSO) algorithm to optimize the process planning problem. To improve the performance of the approach, efficient encoding, updating, and random search methods have been developed. To verify the feasibility and effectiveness of the proposed approach, seven cases have been conducted. The proposed algorithm has also been compared with the genetic algorithm and simulated annealing algorithm. The results show that the proposed modified PSO algorithm can generate satisfactory solutions and outperform other algorithms.     

Development of a generative CAPP system for axisymmetric components for a job shop environment

Process planning is a function in a manufacturing organization that selects the manufacturing processes and parameters to be used to transform a part from its initial state to the final form according to the design specifications. It is a bridge between product design and product manufacturing. The activities of process planning include understanding the part specifications or product design data, selection of job material and tool, setup planning, sequencing the operations within a setup, determination of process parameters for each operation, and generation of process sheets. This paper outlines a method to develop a generative computer-aided process planning system for axisymmetric components for a job shop environment. A decision support system is used to perform semi-structured tasks such as setup planning and establishing precedence relationship among various operations.     

GA-based synthesis approach for machining scheme selection and operation sequencing optimization for prismatic parts

To obtain global and near-global optimal process plans based on the combinations of different machining schemes selected from each feature, a genetic algorithm-based synthesis approach for machining scheme selection and operation sequencing optimization is proposed. The memberships derived from the fuzzy logic neural network (FL-NN), which contains the membership function of each machining operation to batch size, are presented to determine the priorities of alternative machining operations for each feature. After all alternative machining schemes for each feature are generated, their memberships are obtained by calculation. The proposed approach contains the outer iteration and nested genetic algorithm (GA). In an outer iteration, one machining scheme for each feature is selected by using the roulette wheel approach or highest membership approach in terms of its membership first, and then the corresponding operation precedence constraints are generated automatically. These constraints, which can be modified freely in different outer iterations, are then used in a constraints adjustment algorithm to ensure the feasibility of process plan candidates generated in GA. After that, GA obtains an optimal process plan candidate. At last, the global and near-global optimal process plans are obtained by comparing the optimal process plan candidates in the whole outer iteration. The proposed approach is experimentally validated through a case study.     

基于神经网络和遗传算法的工序排序算法

加工顺序的安排是工序设计的重要环节，也是工艺设计的难点，它已经成为阻碍CAPP发展的瓶颈之一。提出一种基于Hopfield神经网络与遗传算法的工序排序算法，建立了网络和数学模型，给出了基于排序原则的参数化表达式，通过仿真计算实例验证了算法的可行性。所提出的基于Hopfield神经网络与遗传算法的工序排序方法不仅可实现对加工活动的自动排序，而且易于考虑其它排序原则和工艺约束条件，可提高算法的实用性和工序排序解的满意度。     

基于遗传算法的工步优化排序方法

针对数控加工中心上零件加工工步的排序问题,以辅助加工时间最短为优化目标,使用遗传算法对零件在一次装夹情况下的加工工步进行优化排序。提出了使用特征关系图和特征高度描述待加工特征之间加工的优先顺序、采用工步优先关系矩阵校验工步序列合理性的方法。论述了初始群体的生成、遗传算子以及工步优化排序的过程和算法。实际应用表明,该方法可有效提高工艺规划系统中工步的优化排序能力。     

A hybrid genetic algorithm for integrated process planning and scheduling problem with precedence constraints

Process planning and scheduling are two important functions in a modern manufacturing system. Although integrating decisions related to these functions gives rise to a hard combinatorial problem, due to the impressive improvement in system performance which is resulted through this integration, developing effective methods to solve this problem is of great theoretical and practical importance. In this research, after formulating the integrated process planning and scheduling problem as a mathematical program, we propose a hybrid genetic algorithm (GA) for the problem. In the proposed algorithm, problem-specific genetic operators are designed to enhance the global search power of GA. Also, a local search procedure has been incorporated into the GA to improve the performance of the algorithm. The model considers precedence relations among job operations, based on which feasible process plans for each job can be represented implicitly. A novel neighborhood function, considering the constraints of a flexible job shop environment and nonlinear precedence relations among operations, is presented to speed up the local search process. In experimental study, the performance of the proposed algorithm has been evaluated based on a number of problems adopted from the literature. The experimental results demonstrate the efficiency of the proposed algorithm to find optimal or near-optimal solutions.     

Computer aided manufacturing planning for mass customization: part 2, automated setup planning

Setup planning plays a crucial role in CAPP to ensure product quality while maintaining acceptable manufacturing cost. The tasks of setup planning include identifying manufacturing features and corresponding manufacturing processes, determining the number of setups, part orientation, locating datum and process sequence in each setup, and selecting machine tools and fixtures. An automated setup planning technique and system has been developed based on not only the tolerance analysis, but also the manufacturing resource capability analysis. The automated setup planning is divided into two levels: setup planning in single part level and in machine station level. Algorithms for setup generation and process sequencing have been developed and a case study of setup planning is presented.     

An intelligent process planning system for prismatic parts using STEP features

This paper presents an intelligent process planning system using STEP features (ST-FeatCAPP) for prismatic parts. The system maps a STEP AP224 XML data file, without using a complex feature recognition process, and produces the corresponding machining operations to generate the process plan and corresponding STEP-NC in XML format. It carries out several stages of process planning such as operations selection, tool selection, machining parameters determination, machine tools selection and setup planning. A hybrid approach of most recent techniques (neural networks, fuzzy logic and rule-based) of artificial intelligence is used as the inference engine of the developed system. An object-oriented approach is used in the definition and implementation of the system. An example part is tested and the corresponding process plan is presented to demonstrate and verify the proposed CAPP system. The paper thus suggests a new feature-based intelligent CAPP system for avoiding complex feature recognition and knowledge acquisition problems.     

A Branch and Bound Algorithm Based Process-Planning System for Plastic Injection Mould Bases

This paper describes the use of artificial intelligence in the process planning of plastic injection mould bases. The computer- aided process-planning system, developed for IMOLD? will extract and identify the operations required for machining. These operations are considered together with their precedence constraints and the available machines before the process plan for the mould base plate is generated. The process plan is optimised by a branch and bound based algorithm. Overall machining time has been proposed as the objective function for optimisation. The ability of this algorithm to search intelli-gently for a feasible optimised solution is illustrated by an industrial case study. A brief comparison with a genetic algor-ithm based process planning system is also made. The result of this development will allow users to optimise process plans easily for any given mould base, with options to suit dynamic changes on the manufacturing shop floor.     

Heuristic solution approaches for combined-job sequencing and machine loading problem in flexible manufacturing systems

Job sequencing and machine loading are two vital and interrelated production planning problems in flexible manufacturing systems (FMSs). In this research, attempts have been made to address the combined job sequencing and machine loading problem using minimization of system unbalance and maximization of throughput as objective functions, while satisfying the constraints related to available machining time and tool slots. This research describes two heuristics to deal with the problems. Heuristic I uses predetermined fixed job sequencing rules as inputs for operation allocation decision on machines, whereas heuristic II uses genetic algorithm based approach for simultaneously addressing job sequences and operation machine allocation issues. Performance of these heuristics has been tested on problems representing three different FMS scenarios. Heuristic II (Genetic algorithm based) has been found more efficient and outperformed heuristic I in terms of solution quality.     

An iterative approach to system setup problems in flexible manufacturing systems

System setup problems in flexible manufacturing systems deal with short-term planning problems such as part type selection, machine grouping, operation assignment, tooling, fixture and pallet allocation, and routing. In this article, we consider three of the subproblems: part type selection, machine grouping, and loading. We suggest a heuristic approach to solve the subproblems consistently with the objective of maximizing the expected production rate. The proposed procedure includes routines to generate all possible machine grouping alternatives for a given set of machines, to obtain optimal target workloads for each grouping alternative, and to allocate operations and tools to machine groups. These routines are executed iteratively until a good solution to the system setup problem is obtained. Computational experience is reported.     

A Generative Feature-Based CAPP/CNC System for Hydraulic Manifold Blocks

A generative CAPP/CNC system for hydraulic manifold blocks is proposed to generate the process plan and the NC codes automatically by interfacing with a feature-based product model. A backward recursive method is used to determine the process operations for features, and an optimal searching algorithm is generated for sequencing the process operations. The sequenced process operations can be translated into the appropriate NC codes that can be recognised by a numerical control (NC) machine. In addition, a visual simulation for machining is built to check both the reliability of the NC codes and the feasibility of the design and manufacturing processes. The software is built using an object-oriented approach and it can be run under the Windows environment.     

Parametric process planning based on feature parameters of parts

This paper presents a model parametric process plan that is dependent on feature parameters of parts, and proposes a solution for the automated process planning of part families. Based on the parametric process plan templates for part families and the feature parameters of new parts, a prototype system is developed. First, parts are grouped into families considering their geometric or manufacturing similarities, and the parametric process plan template is pre-created for each family. Then, to plan the process of a new part, the system extracts feature parameters from its feature-based model and generates an a parametric process plan by searching the template library and solving related constraints. Finally, the system outputs the process plan sheets, if necessary. Although the system cannot creatively generate process plans for brand new parts, the system can meet two important requirements of the real industrial world to CAPP systems, that is good system performance and rapid response.     

Unification of the a priori inconsistencies checking among assembly constraints in assembly sequence planning

Sequence planning generation is an important problem in assembly line design. A good assembly sequence can help to reduce the cost and time of the manufacturing process. This paper focuses on assembly sequence planning (ASP) known as a hard combinatorial optimization problem. Although the ASP problem has been tackled via even more sophisticated optimization techniques, these techniques are often inefficient for proposing feasible assembly sequences that satisfy the assembly planners’ preferences. This paper presents an approach that makes easier to check the validity of operations in assembly process. It is based on a model of the assembly planners’ preferences by means of strategic constraints. It helps to check a priori the consistency of the assembly constraints (strategic and operative constraints) given by the assembly system designers before and while running an assembly plan generation algorithm. This approach reduces the solution space significantly. A case study is presented to demonstrate the relevance of the proposed approach.     

Integrated assembly and machining planning for electronic products using a genetic algorithm approach

To produce an electronic product, both assembly operations and machining operations are required in the process plan. In most cases, the assembly operations and machining operations need to be combined in a continued order with an integrated sequence. This is different from the traditional process planning approaches in which machining operations and assembly operations are separated as two independent tasks with no interactions. For an electronic product, the two types of operations and the associated costs may affect each other in an interactive way. Therefore, the sequence planning of assembly operations and machining operations must be analyzed with an integrated model. In this research, a graph-based model is presented to represent the assembly and machining operations in an integrated model. The related operation cost functions are developed to evaluate the costs for the integrated assembly and machining sequences. The integrated sequence planning problem is solved using a genetic algorithm approach with an objective of lowest operation costs. As a result, the assembly operations and machining operations can be planned in an integrated sequence suitable for producing electronic products. The result shows that the developed method using the genetic algorithm approach is efficient for solving the integrated sequence planning problem. Example products are demonstrated and discussed.     

Optimization of flexible process planning by genetic programming

The traditional manufacturing system research literature generally assumed that there was only one feasible process plan for each job. This implied that there was no flexibility considered in the process plan. But, in the modern manufacturing system, most jobs may have a large number of flexible process plans. So, flexible process plans selection in a manufacturing environment has become a crucial problem. In this paper, a new method using an evolutionary algorithm, called genetic programming (GP), is presented to optimize flexible process planning. The flexible process plans and the mathematical model of flexible process planning have been described, and a network representation is adopted to describe the flexibility of process plans. To satisfy GP, it is very important to convert the network to a tree. The efficient genetic representations and operator schemes also have been considered. Case studies have been used to test the algorithm, and the comparison has been made for this approach and genetic algorithm (GA), which is another popular evolutionary approach to indicate the adaptability and superiority of the GP-based approach. The experimental results show that the proposed method ispromising and very effective in the optimization research of flexible process planning.     

Input sequencing and scheduling for a reconfigurable manufacturing system with a limited number of fixtures

We consider the input sequencing and scheduling problems in a reconfigurable manufacturing system, a state-of-the-art manufacturing system designed at the outset for rapid changes in its hardware and software components. Due to the inherent operation and routing flexibilities of the system, each part is processed according to a multiple process plan, i.e., each part can be processed through alternative operations, each of which can be processed on alternative machines. The main decisions are: (a) the sequence of parts to be released into the system; (b) the selection of operation/machine pair; and (c) the sequence of the parts assigned to each machine within the system. In particular, we consider the practical constraint that the number of fixtures is limited and hence a part can be released into the system only when the fixture required for the part is available. To solve the integrated input sequencing and scheduling problems, we suggest a practical priority rule based approach in which the three decisions are done using a combination of dispatching rules, i.e., those for input sequencing, operation/machine selection, and part sequencing. To show the performances of various rule combinations, simulation experiments were done on the data derived from a real system, and the test results are reported.     

A multi-plant assembly sequence planning model with integrated assembly sequence planning and plant assignment using GA

In a multi-plant collaborative manufacturing system, the manufacturing operations and assembly operations for producing a product can be distributed at different plants at various locations. The components are assembled with assembly operations performed in a multi-plant assembly sequence. In this research, a multi-plant assembly sequence planning model is presented by integrating (1) assembly sequence planning, and (2) plant assignment. In assembly sequence planning, the components and assembly operations are sequenced according to the assembly constraints and assembly cost objectives. In plant assignment, the components and assembly operations are assigned to the suitable plants to achieve multi-plant cost objectives. The feasible assembly sequences are generated using the developed graph-based models of assembly precedence graphs and matrices. A genetic algorithm (GA) method is presented to evaluate the multi-plant assembly sequences with an objective of minimizing the total of assembly operational costs and multi-plant costs. The main contribution lies in the new model for multi-plant assembly sequence planning and the new GA encoding scheme for simultaneous assembly sequence planning and plant assignment. Example products are tested and discussed. The test results show that the presented method is feasible and efficient for solving the multi-plant assembly sequence planning problem.     

An improved genetic algorithm for integrated process planning and scheduling

Process planning and scheduling are two of the most important functions involved in manufacturing process and they are actually interrelated; integration of the two is essential to improve the flexibility of scheduling and achieve a global improvement for the performance of a manufacturing system. In order to facilitate the optimization of process planning and scheduling simultaneously, a mathematical model for the integrated process planning and scheduling (IPPS) is established, and an improved genetic algorithm (IGA) is proposed for the problem. For the performance improvement of the algorithm, new initial selection method for process plans, new genetic representations for the scheduling plan combined with process plans and genetic operator method are developed. To verify the feasibility and performance of the proposed approach, experimental studies are conducted and comparisons are made between this approach and others with the makespan and mean flow time performance measures. The results show that the proposed approach on IPPS has achieved significant improvement in minimizing makespan and obtained good results for the mean flow time performance measure with high efficiency.