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 simulated annealing-based optimization algorithm for process planning

Computer-aided process planning (CAPP) in the past typically employed knowledge-based approaches, which are only capable of generating a feasible plan for a given part based on invariable machining resources. In the field of concurrent engineering, there is a great need for process planning optimization. This paper describes an approach that models the constraints of process planning problems in a concurrent manner. It is able to generate the entire solution space by considering multiple planning tasks, i.e. operations (machine, tool and tool approach direction), selection and operations sequencing simultaneously. Precedence relationships among all the operations required for a given part are used as the constraints for the solution space. The relationship between an actual sequence and the feasibility of applying an operation is also considered. An algorithm based on simulated annealing (SA) has been developed to search for the optimal solution. Several cost factors including machine cost, tool cost, machine change cost, tool change cost and set-up change cost can be used flexibly as the objective function. The case study shows that the algorithm can generate highly satisfying results.     

Reverse engineering of machining operation planning

Computer-aided process planning (CAPP) is becoming increasingly crucial to today's computer-integrated manufacturing (CIM) and rapid production. To automate the process planning, feature-based operation planning systems have been suggested and studied extensively. In such a system, given a machining feature, the operator requires practical machining operation data for the feature. In this research, a system of reverse engineering is proposed to extract machining features and their associated machining operation data. Furthermore, a machining know-how database containing the extracted data is created for future operation planning. Since successful NC programs contain the machining know-how of skilled workers, the system is aimed at extracting the machining know-how data from the NC programs through reverse engineering. The extraction of the machining features and feature topologies has been addressed previously. The present paper deals with the extraction of machining operation data, including operation sequence, cutting conditions, machining type and cutting mode. A prototype of the system is developed and a machining know-how database is generated. The extraction of machining features and their associated machining operations has been verified through a variety of NC programs.     

Geometry-based machining precedence reasoning for feature-based process planning

This paper presents a method to generate machining precedence relations systematically based on the geometric information of the part. The feature recognition method using Alternating Sum of Volumes with Partitioning (ASVP) Decomposition is applied to obtain a Form Feature Decomposition (FFD) of a part model. Form features are classified into a taxonomy of atomic machining features to which machining process information has been associated. Geometry-based precedence relations between features are systematically generated using the face dependency information obtained by ASVP Decomposition and the features' associated machining process information. Multiple sets of precedence relations are generated as alternative precedence trees based on the feature types and machining process considerations. These precedence trees can be further enhanced with precedence relations from tolerance specifications and machining expertise. Machining sequence planning can be performed for each of these precedence trees while minimizing the number of tool changes. The precedence trees may then be evaluated based on machining cost and other criteria. The precedence-reasoning module is currently being implemented within a comprehensive computer-aided process planning system.     

Optimisation of cutting parameters using a multi-objective genetic algorithm

This paper presents a new optimisation technique based on genetic algorithms (GA) for determination of cutting parameters in machining operations. The cutting parameters considered in this study are cutting speed, feed rate and cutting depth. The effect of these parameters on production time, production cost and roughness is mathematically formulated. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. Experimental results show that the proposed algorithm is both effective and efficient, and can be integrated into an intelligent process planning system for solving complex machining optimisation problems.     

基于细菌觅食和蚁群算法的工艺路线优化

针对工艺路线规划中满足多重约束的最优方案选择问题,提出一种细菌觅食和蚁群优化（bacteria foraging ant colony optimization,BFACO）算法。首先,将工艺路线规划转化为对加工元顺序的优化问题,构造满足多种工艺准则的加工元拓扑优先顺序图,并构建了在缩短加工周期、提高加工质量和降低加工成本目标下的最低加工资源更换成本的目标函数;其次,设计加工元序列与加工资源两个搜索阶段的蚁群搜索,拓扑优先顺序图可弥补加工元序列搜索阶段信息素匮乏的缺点,而在加工资源搜索阶段引入细菌觅食优化算法的复制与趋向操作,可使加工元在多个可选加工资源的情况下获得加工资源更换成本最低的加工序列;最后,基于细菌觅食与蚁群算法的融合优化,完成多个加工元序列的信息素积累并输出最优解,解决蚁群算法局部收敛且计算速度慢的问题。将BFACO算法应用于实例并与其他优化算法的优化结果进行对比,结果显示BFACO算法在工艺路线优化方面较其他优化算法具有较高的计算效率,验证了BFACO算法的可行性与有效性。研究表明,BFACO算法可有效应用于同时考虑工艺约束与加工资源更换成本的工艺规划,为实际生产提供高效且灵活的工艺路线的优化选择。     

A CAPP framework with optimized process parameters for rotational components

Process planning, as a critical stage integrating the design and manufacturing phase in a manufacturing environment, has been automated to meet the needs for higher productivity and lower production cost. Being an input to various systems such as scheduling and routing, process planning results are of great importance in the manufacturing stage. Though feature extraction and sequence optimization have been given much attention, the process parameters are rarely dealt with. This paper focuses on the development of a new generative computer aided process planning (CAPP) framework for rotational components. The developed framework includes modules for feature extraction based on CAD application programming interfaces, determination of the optimum sequence and generation of optimum process parameters. The optimization of the machining operations is achieved using the evolutionary technique. The approach resulted in the reduction and prediction of machining time and cost. The framework is demonstrated with a case study.     

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.     

Development of an integrated model for process planning and parameter selection for machining processes

The concept of ‘do it right the first time’ in the machining industry not only expects the best quality products but also at the best possible cost. The cost of machining depends on intelligent process planning and selection of machining parameters such as speed, feed, and depth of cut. The problem of machining parameter selection has received great attention by researchers and many techniques have been developed. A review of these techniques reveals that the selection of the machine and cutting tool is done before the process of cutting parameter selection and process sequencing, and often the selection is based on experience. The current research is an attempt to develop an integrated model (ExIMPro: Expert system based Integrated model for Machining Processes) which finds the sequence of operations with set of machines, tools, and other process parameters to minimise the cost of machining for a cylindrical part. This system consists of existing expert system Machining Parameter SELection (MPSEL) for machine and tool selection and a Microsoft Excel® and Visual Basic® based parameter selection model. The present model focuses on turning and cylindrical grinding operations but other processes can be incorporated with little modification to the software.     

Integrating assembly and machining planning using graph-based representation models

Traditionally, assembly planning and machining planning are considered as two independent tasks. In assembly planning, the components to be sequenced are considered as machined and finished. In machining planning, the focus has been on machining each individual component. In previous research, machining and assembly planning are analysed separately. However, in order to achieve some design specifications, the assembly and machining operations may need to be mixed in an integrated sequence. For example, a machining operation may need to be performed on a subassembly formed by a group of components in order to complete certain geometric features. In other cases, an assembly operation cannot be performed unless certain geometric features are completely machined. Therefore, the assembly and machining operations need to be planned in a combined sequence. In this research, new graph-based representation models were developed to integrate assembly and machining planning. First, an assembly-machining operation graph was developed to represent the spatial relationships between the components as well as to express the operational precedence of the machining and assembly operations. Next, the integrated assembly and machining sequences were generated using a tree structure called the assemblymachining sequence tree. Using the graph-based methodology, all the feasible integrated assembly and machining sequences can be generated and evaluated. The main objective is to provide a complete model for integrating assembly and machining sequences. A combined evaluation can be performed to find the best sequence based on certain time and cost objectives. The presented methodology is implemented on a personal computer and several example parts are discussed.     

Optimizing cutting parameters in process planning of prismatic parts by using genetic algorithms

The determination of optimal cutting parameters, such as the number of passes, depth of cut for each pass, cutting speed and feed, which are applicable for assigned cutting tools, is one of the vital modules in process planning of metal parts, since the economy of machining operations plays an important role in increasing productivity and competitiveness. The present paper introduces a 'system software' developed to optimize the cutting parameters for prismatic parts. The system is mainly based on a powerful artificial intelligence (AI) tool, called genetic algorithms (GAs). It is implemented using C programming language and on a PC. It can be used as standalone system or as the integrated module of a process planning system called OPPS-PRI (Optimized Process Planning System for PRIsmatic parts) that was also developed for prismatic parts and implemented on a vertical machining centre (VMC). With the use of GAs, the impact and power of AI techniques have been reflected on the performance of the optimization system. The methodology of the developed optimization system is illustrated with practical examples throughout the paper.     

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 GA-based approach to machining operation sequencing for prismatic parts

This paper presents a study of using a genetic algorithm (GA) method to select the machining operation sequence for prismatic parts. Four types of process planning rules including precedence rules, clustering rules, adjacent order rules and optimization rules are considered and are encompassed quantitatively in the fitness calculations for alternative operation sequences. The impact of variations of the rules on the result of operation sequencing and that of GA parameters on the solution efficiency are discussed through analysis of examples and experiments. The proposed genetic algorithm proves effective for machining operation sequencing of prismatic parts, by incorporating various production environment considerations into process planning.     

Extraction of milling know-how from NC programs through reverse engineering

To automate machining process planning, the acquisition and representation of machining knowledge or know-how in a reusable way is needed. The machining know-how is implied in NC programs made by experienced workers. In this paper, the methodology and system for extracting machining know-how in milling operations have been developed. With the system, machining features, operations and their associated cutting conditions (depth of cut, feed rate and spindle speed, etc.) and machining method can be extracted by analysing NC programs in conjunction with the tools used and workpiece blank model. The milling know-how is represented as a collection of these extracted data that can be used in future machining operations. To verify the system, actual NC programs for milling have been analysed and the milling know-how has been extracted successfully.     

Computer-aided optimization of multi-pass turning operations for continuous forms on CNC lathes

The Computer Numerical Control (CNC) machine is one of the most effective production facilities used in manufacturing industry. Determining the optimal machining parameters is essential in the machining process planning since the machining parameters significantly affect production cost and quality of machined parts. Previous studies involving machining optimization of turning operations concentrated primarily on developing machining models for bar components. Machined parts on the CNC lathes, however, typically have continuous forms. In this study, we formulate an optimization model for turned parts with continuous forms. Also, a stochastic optimization method based on the simulated annealing algorithm and the pattern search is applied to solve this machining optimization problem. Finally, the applications of the developed machining model and the proposed optimization algorithm are established through the numerical examples.     

Optimal tolerance re-allocation for the generative process sequence

In generative process planning, the sequence of machining processes is decided according to the specifications of parts, such as tolerance values. However, in order to obtain the minimal manufacturing cost, the machining process sequence needs to be considered before tolerances are assigned. It is therefore difficult to assign optimal tolerances so that a minimum manufacturing cost is achieved. This paper presents an iterative approach for reallocation of tolerance within the given functional constraints to minimize the manufacturing cost. With the given values of tolerance and corresponding process sequences, which are derived from a handbook or a designer's experience as initial inputs, each iteration of tolerance re-allocation tries to improve the total cost by shifting tolerances along the different processes in the current sequence. The re-allocation problem is formulated as a mixed integer nonlinear programming problem. The Lagrange Multiplier method has been used to solve nonlinear programming, and an exhaustive search method has been adopted to guarantee the global optimum in solving the zero-one algorithm. A prototype system has been implemented in an object-oriented programming environment and a case study is presented to demonstrate the capability of the system.     

Decision algorithm for selection of high-speed machining,EDM or a combination for the manufacture of injection moulds

The paper presents the development of a decision algorithm for a prototype computer-aided process planning system IMOLD_CAPP for the manufacture of an injection mould. A combination of a generative and plan template approach is used for the development of the system. The input to the system is a 3D solid model developed using IMOLD TM . Successful implementation of the prototype IMOLD_CAPP depends on its incorporation to an appropriate machining algorithm. Unfortunately, there is no one standard algorithm to choose the appropriate machining process from high-speed machining (HSM), electrical discharge machining (EDM) or a combination of the two. Consequently, this research undertook the development of a decision algorithm for the selection of the appropriate machining process. The algorithm decides whether HSM, EDM or a combination should be chosen for a particular case. Several case studies are conducted and the economics of these processes have been analysed to verify the suitability of the algorithm.     

Flexible optimization of machining variables using constrained R-T characteristic curve

An algorithm is developed to find optimal machining variables for multiple machining environments. The cutting ratetool life (R-T) characteristic curve presents the general loci of optima and is useful for flexible machining operations planning. The R-T characteristic curve for machining economics problems with a linear-logarithmic tool life model may be determined by applying sensitivity analysis to log-dual problems. Three cases of changes of machining environments are considered. An end-milling example is constructed to illustrate the algorithm.     

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.