A STEP AP 203–214-based machinable volume identifier for identifying the finish-cut machinable volumes from rough-machined parts

This paper presents a STEP AP203–214-based machinable volume identifier (MVI) to identify the finish-cut machinable volume in prismatic parts by deducting the rough-machined part from the final part. The MVI provides an intermediate link between rough and finish machining computer-aided process planning system for automatic generation of process plans while machining prismatic parts. To calculate the machinable volumes of manufacturing features, the MVI utilizes the output of the feature identifier which contains the information about the dimensional details, edge loops, edges, vertices, coordinate points, and location planes of the features. In this research, a total of 234 features have been considered; out of which, 32 are normal and 202 are tapered. To calculate the machinable volumes for these features, generalized methodologies are developed for 17 basic feature types, each having a varying number of specific features. Initially, the pattern strings are generated for the front and back face of the rough-machined feature and final feature. Then, MVI uses the predefined syntactic pattern strings stored in the strings database and checks with the generated strings of the feature to determine the shape of the machinable volume stored in the volumes database. After determining the shape, one relevant methodology or more (for features having combination of more than one taper) are selected from among the 17 “feature type” specific methodologies developed for finish-cut machinable volume identification. In this article, methodology is presented for one basic feature type which covers 14 features and explained through one case study. The final output from this module is stored as a text file with full dimensional details of machinable volumes for later use inside the machining planning module. The proposed MVI can be used in Computer Integrated Manufacturing Industries as an intermediate linker to achieve a robust manufacturing environment.     

基于自适应蚁群算法的工艺路线优化

针对计算机辅助工艺设计中最优方案选择,提出一种以制造资源更换率最低为目标的自适应蚁群优化方法(Adaptive ant colony algorithm,AACA)。通过分析零件特征,根据精度要求对制造特征进行分解,提出加工元概念。加工元被定义为特定的制造特征、加工阶段、加工方法、制造资源、装夹位置的集合,工艺路线的确定被转换为对加工元的优化顺序安排问题。以制造特征之间的几何位置约束,各加工阶段的先后顺序约束为基本元素,构造加工元优选约束矩阵,给出基于优选约束矩阵的加工元优选原则。在加工元优先顺序约束和可用制造资源的共同约束下,将缩短加工周期、提高加工质量和降低加工成本的综合目标表达为制造资源更换率最低,进行优化目标函数的数学建模。指出加工元优化排序可类比旅行商问题,并选择AACA进行优化求解。实例分析表明提出的方法可以可靠和有效地得到符合生产实际的工艺路线。     

Object-oriented and fuzzy-set-based approach for set-up planning

Set-up planning is not only a key element of computer-aided process planning (CAPP), but also a key factor for the integration of CAPP with computer-aided fixture planning (CAFP) and production planning and scheduling (PPS). In this work, an object-oriented and fuzzy-set-based approach is developed for set-up planning. Object-oriented technology is employed to represent set-up planning knowledge and generate alternative set-ups which can respond to the dynamic manufacturing resource and facilitate the CAPP/PPS integration. Based on fuzzy-set theory, a fuzzy evaluation function is proposed to produce optimal set-ups. Combined with a real project, a set-up planning system is developed to generate alternative and optimal set-up plans for the machining of prismatic parts.     

Efficient computation of smooth minimum time trajectory for CNC machining

Previous computation approaches of smooth trajectory for computer numerical control machining either cannot fully utilize the maximum ability of machines, fail to minimum machine time, or cannot be computed efficiently because of the nonlinearity formulation of problem. In this paper, an efficient computation approach is proposed that has been designed as solving a convex optimization problem to generate smooth minimum time trajectory. Since smoothness of trajectory can be adjusted by confining the value of jerk (rate of acceleration change), the proposed approach uses a pseudo-jerk to approximate the real jerk and to realize smoothing trajectory. The difference between the real jerk and pseudo-jerk is that the proposed pseudo-jerk can be linearity formulation in the problem. By using pseudo-jerk constraints, smooth minimum time trajectory planning problem is formulated as a convex optimization problem. Since of convexity of the optimization problem, the solution process can be efficient by using ordinary gradient-based optimization techniques and the optimal numerical solution is uniqueness. The effectiveness of the approach is demonstrated in machining simulations of two plane patterns.     

Analysis of flexible manufacturing systems with distinct repeated visits: DrQ

This article examines the performance effects caused by repeated part visits at the workstations of a flexible manufacturing system (FMS). Such repeated part visits to the same workstations are commonly associated with fixture changes for machining complex parts, reclamping, and remounting or reorienting them. Since each of the repeated visits to a workstation may require different processing requirements, the resulting queueing network does not have a product form solution. We therefore develop an approximate mean value analysis model for performance evaluation of an FMS that may produce multiple part types with distinct repeated visits.We provide numerical examples and validate the accuracy of our solution algorithm against simulation. These examples show that the proposed model produces accurate throughput and utilization predictions with minimal computational efforts. These examples reveal that increasing the total pallet population may result in a reduction of the aggregate throughput, and that the FMS's performance could be more sensitive to the mix of pallets and part routes than to the total number of pallets. Our model will be of use, in particular, when managers wish to control individual operations (e.g., to adjust individual operation times to achieve economic savings in tool wear and breakage costs) or to investigate the performance implications of route changes due to alternate assignments of particular manufacturing tasks to certain workstations.     

Optimization of machining economics and energy consumption in face milling operations

Metal cutting (or machining) is one important aspect of the manufacturing system. Selecting optimal cutting conditions for machining is then a crucial process planning task for manufacturing. Traditionally, solving such machining problems was only focused on economic objectives such as maximizing profit or minimizing production time requirement. In the recent decade, however, minimizing energy consumption in manufacturing processes has attracted increased attention due to increasing energy costs and concern with greenhouse gas emissions. Energy loss could be avoided by carefully selecting cutting parameters. This paper develops a multi-objective mathematical model to minimize unit production costs along with energy consumption for face milling operations. In addition, an evolutionary strategy (ES)-based optimization approach is used to identify optimal cutting conditions for the proposed model.     

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.     

A computer-aided inspection planning system for on-machine measurement — part I: Global inspection planning —

Computer-Aided Inspection Planning (CAIP) is the integration bridge between CAD/CAM and Computer Aided Inspection (CAI). A CAIP system for On-Machine Measurement (OMM) is proposed to inspect the complicated mechanical parts efficiently during machining or after machining. The inspection planning consists of Global Inspection Planning (GIP) and Local Inspection Planning (LIP). In the GIP, the system creates the optimal inspection sequence of the features in a part by analyzing the various feature information such as the relationship of the features, Probe Approach Directions (PAD), etc. Feature groups are formed for effective planning, and special feature groups are determined for sequencing. The integrated process and inspection plan is generated based on the sequences of the feature groups and the features in a feature group. A series of heuristic rules are developed to accomplish it. In the LIP of Part II, the system generates inspection parameters. The integrated inspection planning is able to determine optimum manufacturing sequence for inspection and machining processes. Finally, the results are simulated and analyzed to verify the effectiveness of the proposed CAIP.     

An Adaptive Genetic Algorithm for Manufacturing Cell Formation

An adaptive genetic approach is proposed as an effective means of providing the optimal solution to the manufacturing cell formation problem in the design of cellular manufacturing systems. The proposed approach generates the optimal formation of machine cells and part families by sequencing the rows and columns of a machine-part incidence matrix, so as to maximise the bond energy of the incidence matrix. In order to enhance the performance of the genetic search process, an adaptive scheme is adopted, so that the genetic parameters can be adjusted during the genetic search process. The effectiveness of the proposed approach is demonstrated by applying it to two numerical examples and 11 benchmark problems obtained from the literature. The computational results show that the proposed approach provides a powerful but simple means of solving the manufacturing cell formation problem and thus facilitates the design of cellular manufacturing systems.     

基于智能水滴算法的装夹规划方法

针对计算机辅助工艺设计中的装夹规划问题,提出一种基于智能水滴算法的装夹规划方法。通过对加工特征进行分析来定义零件的操作单元及其约束关系,构建装夹规划的模型。将零件的各个操作单元与智能水滴算法中流动路径的各个节点相关联,利用水滴所携带的泥土量来表征操作单元之间的相似度,以此来构建智能水滴算法的适应度函数。在经过操作单元顺序约束矩阵所筛选出的可行解空间内进行迭代求解,通过水滴多次冲刷路径中的泥土量来筛选最优解,解码后获得了最优的装夹规划方案。以典型零件的装夹规划为例,验证了该方法的有效性和可行性。     

Optimal curvature-smooth transition and efficient feedrate optimization method with axis kinematic limitations for linear toolpath

In industrial areas, the machining performance with linear G01 code is a crucial indicator to evaluate the computer numerical control (CNC) systems and many researchers have presented various methods to deal with the corner tracking issue. However, the axis jerk limitations are not satisfied well and the different axis kinematic limitations are not considered in most researches, which will reduce the machining efficiency and machining quality simultaneously. In this paper, a novel method including trajectory planning, feedrate scheduling, and interpolating is proposed to obtain better machining quality and higher machining efficiency. In trajectory planning, a B-spline curve is utilized to smooth the linear toolpath and obtain a curvature-smooth trajectory, which is third-order geometry continuous. Thereby, a time-optimal method for the geometric continuous trajectory is proposed based on linear programming algorithm in the feedrate scheduling and the bounded multi-constraints, including axis velocity, axis acceleration, axis jerk, and feedrate. Moreover, it can be seen that the proposed method is near Bang-bang control. To reduce the computation time of the optimal numerical method, an efficient method with a look-ahead window around the transition B-spline curve is applied. In the interpolation stage, a novel interpolation method about arc-length is proposed to improve computation efficiency. Finally, simulation and experiment are conducted to show superiorities of the proposed method to the already existing approaches. The results show that the cycling time of the proposed method is reduced by more than 7% than G² method and 20% than G³ method with better contour performance.     

Generating Alternative Interpretations of Machining Features

One of the major difficulties in extracting machining features is the lack of a systematic methodology to generate alternative ways of manufacturing a machined part. Most of the early research in feature extraction and process planning has not considered this aspect, and has focused on the generation of a single interpretation. In this paper, we propose a feature-based approach to generating alternative interpretations of machining features from a feature-based design model. The proposed approach simplifies the generation of alternative machining feature models by using information on feature which is captured and maintained during feature-based model-ling and machining feature extraction. A set of machining features is incrementally extracted during the feature-based design process of a machined part. A feature conversion process converts each design feature into a machining feature or a set of machining features by using information on the geometry and the feature. Using reorientation, reduction, and/or splitting operations, alternative models are generated from the sets of extracted machining features. During the execution of each operation, unpromising models are pruned by using criteria such as minimising the number of accessibility directions. The machining features and their precedence relationships are represented in a STEP-based machining feature graph for the purpose of data exchange.     

Tool deflection error compensation in five-axis ball-end milling of sculptured surface

Manufacturing of aircraft structural parts have the characteristics of complex process, large variety, small batch, and frequent change of production status. In order to shorten lead time and reduce cost, high-efficiency communication and collaboration among manufacturing departments are required. However, in the existing research literature, tool/fixture design, manufacturing simulation, and online machining process monitoring are not fully taken into account for collaboration. In order to address these challenging issues, this paper proposes a collaborative manufacturing framework based on machining features and intelligent software agents. The components of the proposed framework include a machining process planning agent, a numerical control (NC) programming agent, a simulation and verification agent, a tool/fixture design agent, a cost estimation agent, a production management agent, and an online machining process monitoring agent. Machining features are used as information carrier for communication and collaboration among these agents. This paper particularly focuses on the collaboration between tool/fixture design and NC programming, as well as the collaboration between online machining processes and related departments. The proposed approach has been implemented through a prototype system and tested in a large aircraft manufacturing enterprise with some very promising results.     

Material bead deposition with 2 + 2 ½ multi-axis machining process planning strategies with virtual verification for extruded geometry

Process planning for hybrid manufacturing, where additive operations can be interlaced with machining operations, is in its infancy. New plastic- and metal-based hybrid manufacturing systems are being developed that integrate both additive manufacturing (AM) and subtractive (machining) operations. This introduces new process planning challenges. The focus of this research is to explore process planning solution approaches when using a hybrid manufacturing approach. Concepts such as localized AM build ups, adding stock to a CAD model or section for subsequent removal, and machining an AM stock model are investigated and illustrated using virtual simulations. A case study using a hybrid laser cladding process is used to demonstrate the opportunities associated with a hybrid solution. However, unlike machining, the process characteristics from system to system vary greatly. These are portrayed via a high power, high material deposition feed rate laser cladding system. There are unique challenges associated with AM processes and hybrid manufacturing. New tools and design rules need to be developed for this manufacturing solution to reach its potential.     

基于禁忌制造特征动态调整的STEP-NC工艺路线蚁群优化方法

针对STEP-NC制造特征的加工工艺路线生成与优化问题,提出了一种以机床、夹具及刀具更换率最低为目标函数的禁忌制造特征动态更新的工艺路线蚁群优化方法。首先,根据加工工艺对制造特征进行分解,将工艺路线优化问题转化为对制造特征的排序;然后根据制造特征在加工过程中存在的加工遮挡关系以及刚性工艺性约束,提出禁忌制造特征动态更新的工艺路线生成方法;最后将禁忌制造特征动态更新方法与Ant-Cycle模型的蚁群算法相结合,求解制造特征工艺路线的优化问题。实例应用表明,提出的方法能较好解决加工工艺路线优化过程中的刚性约束处理问题。     

基于制造资源能力的装夹工艺分层设计技术

为发展创成式的计算机辅助工艺设计,研究了计算机辅助工艺设计系统中工件装夹规划的自动生成算法.基于扩展有向图,建立了零件的公差信息和基准-加工特征关系的数学表示模型,基于公差分析和制造资源能力模型,建立了从单件层到多件层的工件装夹工艺生产算法.该数学模型和算法可自动识别工件的加工特征、装夹基准,并根据制造资源能力和公差分析对装夹进行优化分组,实现装夹分组对工件加工精度的影响最小化,进而生成装夹规程.最后以实例证明了该方法的可行性.     

Selecting optimal set of tool sequences for machining of multiple pockets

In computer-aided manufacturing domain, the selection of cutters significantly affects the machining time. However, it is difficult for humans to determine the optimal or near-optimal set of cutting tools, in particular for complex parts with multiple features. Traditionally, every feature is machined one-by-one, and no cutter sharing is taken into consideration. To perform machining operations efficiently, both the number and size of cutters should be selected carefully. Therefore, this paper proposes a new approach to determine optimal cutting tool sequences for machining multiple features in a single setup, which has been implemented and integrated with a feature-based process planning system for the generation of a numeric control code. To seek a general approach to compute the coverable area of each cutter for machining arbitrary region shapes in the case of multiple cutters combination, a new algorithm is introduced by improving the scan line-based polygon filling algorithm. To do so, the features with the nonpolygon profiles have to be approximated as polygons according to the requirement of the tolerance. The proposed method has been verified by experiments.     

A combinatorial manufacturing resource planning model for long-term CNC machining industry

Combinatorial resource planning for both machines and human resources in a computer numerical controlled (CNC) machining workstation has become a crucial factor for long-term manufacturing management. This paper not only mathematically constructs the combinatorial manufacturing resource planning (CMRP) model with the conception of balancing the machine productivity and the human capability, but also develops a step-by-step algorithm to reach the maximum-profit solution under the deterministic market demand. Additionally, to extend the applicability of the CMRP model, a computer program written with MATLAB is comprehensively prepared to achieve the optimum manufacturing resource planning under the forecasts of probabilistic market demand and product sales price. The versatility and adaptability of this study are exemplified through a numerical illustration from real-world industry. This paper contributes to the problem of conclusively scheduling machine productivity and human resources, as well as comprehensively optimising the production profit for the existing CNC machining industry .     

Particle swarm optimization (PSO) algorithm for optimal machining allocation of clutch assembly

Nowadays tolerance optimization is increasingly becoming an important tool for manufacturing and mechanical design. This seemingly, arbitrary task of assigning dimension tolerance can have a large effect on the cost and performance of manufactured products. With the increase in competition in today’s market place, small savings in cost or small increase in performance may determine the success of a product. In practical applications, tolerances are most often assigned as informal compromises between functional quality and manufacturing cost. Frequently the compromise is obtained interactively by trial and error. A more scientific approach is often desirable for better performance. In this paper particle swarm optimization (PSO) is used for the optimal machining tolerance allocation of over running clutch assembly to obtain the global optimal solution. The objective is to obtain optimum tolerances of the individual components for the minimum cost of manufacturing. The result obtained by PSO is compared with the geometric programming (GP) and genetic algorithm (GA) and the performance of the result are analyzed .     

混合生产中作业计划体系研究

对于兼有离散和连续生产特征的混合生产行业,由于其计划体系的复杂性和多样性,尚未形成成熟的具有通用意义的应用方案。分析了混合生产的特征和混合生产计划体系的特点,提出了研究混合生产计划体系的思路,构建了该作业计划体系框架并描述了功能要求及信息联系,研究了前连续后离散这种混合生产模式的作业计划排程问题的离散制造与连续制造分解逻辑,并以毛纺织行业为背景,进行了连续生产环节计划排程优化算法研究,探讨了毛纺织混合生产计划体系对物料清单、工艺路线等基础数据管理的增强要求。