保证公差要求的装夹和加工顺序自动规划

提出了一个保证公差要求的装夹方案和特征加顺序自动规划方法。该方法根据机床的加工精度确定关键公差特征,以关键公差特征和唯一加工方法特征为基础,将零件的特征模型分解为相同装夹特征集合。该方法具有较快的规划与排序的速度,与设计系统紧密集成,并可在规划的过程中实现零件的整体可制造性评价功能。     

某发动机多个机匣零件通用铣槽夹具设计与应用

本文针对一组结构类似的发动机机匣零件,对零件的结构和尺寸和装夹特点进行分析,设计了一套5个机匣零件通用的型槽铣加工夹具,满足各不同尺寸零件的装夹和定位要求,通过零件的加工试验,验证了该夹具结构方案的可行性。     

FMS环境下的创成式工艺设计

本文介绍了一个在FMS环境下的箱体类零件创成式CAPP系统。该系统采用创成的思想,能对复杂的箱体类零件通过逻辑决策的方法,自动进行装夹方案设计、加工方法选择,加工顺序排定、刀具及切削用量选择等一系列工艺设计的工作,生成完整而实用的工艺文件。文中详细介绍了该系统的零件信息描述方法及工艺设计原理。     

基于遗传算法对箱体零件的工步序列优化

针对箱体零件提出了基于遗传算法的工艺路线优化方法.对箱体零件特征进行分类与定义,确定箱体零件加工的先后顺序,并自动生成了约束矩阵和工艺路线.在计算机辅助工艺过程设计平台中,定义了缸孔、缸面等关键部位的加工工艺.在遵循工艺设计原则的基础上,实现了对加工工艺路线的优化.     

某安装座的加工与车床夹具设计

本文介绍了加工面有角度与偏心的安装座在车床上加工的方法,工艺方案制定的依据,重点说明了车削工装的设计、零件装夹定位方式等内容。对于一些特殊零件在车床上加工有一定的借鉴作用,为充分挖掘普通机床的加工能力,提高零件的加工效率有重要意义。     

浅谈高精度U型深槽类零件加工(二)

高精度U型深槽零件因其加工工艺性差、加工应力变形、装夹困难等原因,一直困扰着机加工企业。本文结合实例从工艺结构优化、稳定处理、装夹方法三各方面对高精度U型深槽零件的加工方案进行了有益探索。     

浅谈高精度U型深槽类零件加工(一)

高精度U型深槽零件因其加工工艺性差、加工应力变形、装夹困难等原因,一直困扰着机加工企业。本文结合实例从工艺结构优化、稳定处理、装夹方法三各方面对高精度U型深槽零件的加工方案进行了有益探索。     

谈输油臂90°旋转弯头加工夹具设计

输油臂中具有许多弯头管道,其作用也各不相同,常用于高温、低温、强腐蚀性介质输送管道的连结。一般铸造成型。为了尺寸统一和提高生产效率,通常在数控车床上加工,但是该零件形状不规则,需要重复定位,装夹有一定的难度。根据加工需要,必须采用工装夹具进行装夹。文章主要阐述了该零件的工装设计制造。使用该工装缩短了装夹弯头零件的时间,大大提高了生产效率。     

大型结构件数控加工在位计量检测技术研究

在位计量检测以加工机床为硬件载体,实现加工、测量一体化,对提高产品质量和生产效率有重要意义。介绍了数控加工在位计量检测系统的组成与方案的实现过程,对系统的性能进行了现场试验,结果表明大型结构件数控加工在位计量检测系统可对大型数控加工零件工艺过程中的尺寸和形位误差进行准确在位测量,大大缩短了大型数控加工件的定位时间,有效提高了一次装夹加工的效率和合格率,避免了工件的二次装夹,降低了生产成本,为数字化装配提供有力保证。     

基于图和体分解的异型零件加工特征识别方法

针对异型零件具有众多复杂的相交特征，加工特征难以自动识别，进而影响其高效、高质量自动编程设计的问题，提出了一种基于图和体分解的加工特征识别方法。首先，以STEP中性文件作为输入，获取异型零件的几何、拓扑信息，以构造其毛坯特征和体积特征；同时，采用布尔减运算得到异型零件的加工体积特征，并将其分解为只含凸边关系的单一特征。然后，设计了一种基于特征面关系的几何特征矩阵（geometric feature matrix, GFM）和拓扑特征矩阵（topological feature matrix, TFM）构建方法，其中GFM侧重描述零件模型中面与面之间的位置关系，TFM侧重描述零件模型中面与面之间相邻的凹凸性，该方法较好地解决了传统属性邻接矩阵（attribute adjacency matrix, AAM）易出现无法准确表达零件加工特征的问题。最后，将异型零件的GFM和TFM与加工特征库进行匹配，以实现其加工特征的识别。在Visual Studio 2019平台中开发基于图和体分解的异型零件加工特征识别系统并开展验证实验。结果表明，所提出的方法能准确识别异型零件的所有加工特征，这可为异型...     

Setup planning for machining the features of prismatic parts

This paper focus on the development of a formalized procedure for automatic generation of feasible setups and then to select an optimal setup plan for machining the features of a given prismatic part. The proposed work simultaneously considers the basic concepts of setup planning from both machining and fixturing viewpoints in order to formulate feasible setup plans. The tasks that are performed are: (a) identifying groups of features that can be machined in a single setup, (b) determining a suitable work piece orientation, i.e. the suitable datum planes for each setup, (c) determining all the feasible setup plans to machine the given set of features of prismatic parts, and (d) evaluating the feasible setup plans on the basis of technological (available tolerance) and economical conditions (total setup time). For the proposed work the authors have introduced four new concepts namely, ‘surface fit for location’, ‘primary group’ ‘secondary group’, and ‘eligible group’. The first concept plays an important role in the identification of suitable faces of the part for positioning, clamping and supporting. The other three help in clustering of features, which can be converted in to candidate setups.     

Process planning for multi-axis NC machining of free surfaces

Process planning for the manufacture of free surfaces is mainly concerned with determination of machining configuration. In this paper, we investigate the machining configuration problem stated as: For a given surface model, find (1) The minimum number of control axes, (2) The minimum range of the rotational axes, and (3) The workpart setup orientation. In deriving the solution algorithm, we ensured implementability by casting the problem into an algebraic domain. Using a digitized visibility map, called the binary spherical map, the machining configuration problem can be solved in a unified fashion without falling into numerical complexity. The solution algorithms are implemented and validated via computer simulations, indicating they can be implemented on the CAD/CAM system as a process planner.     

An integrated setup/fixture planning approach for machining prismatic parts

Setup/fixture planning for machining prismatic parts is usually carried out by grouping features/operations into setups, determining suitable locating surfaces for each setup, and sequencing setups and operations in each setup. The presently developed setup/fixture planning methods can be classified into three categories: multi-constraints, fixture driven, and tolerance analysis planning methods. The multi-constraints planning is theoretically sound, but very difficult to use. In fixture driven planning, the generated setup plans are highly practical, but may not be optimal. Tolerance analysis based planning cannot be used to generate the optimal solution by itself. In this research, a new integrated setup/fixture planning approach is developed to identify the optimal and practical setup/fixture plan. The planning is conducted in four steps. (1) The dissimilarity degree matrix (DDM), operation precedence graph (OPG), and hybrid graph are used to describe the requirements and constraints for setup/fixture planning. (2) A setup precedence graph (SPG) is used to describe precedence constraints between setups. The SPG is generated by the vertex clustering algorithm. Precedence loops among setups are avoided by checking whether two serial vertex clusters can generate a loop. (3) Suitable locating surfaces are selected for each setup in a SPG. When the fixture locating surface and design datum surface are different, two types of dimension recalculations are employed to obtain the dimensions against the locating surfaces. (4) All the candidate setup/fixture plans are evaluated based on the number of setups and recalculation of dimensions. An example is given to demonstrate the effectiveness of the developed approach.     

Optimization of machining datum selection and machining tolerance allocation with genetic algorithms

In machining process planning, selection of machining datum and allocation of machining tolerances are crucial as they directly affect the part quality and machining efficiency. This study explores the feasibility to build a mathematical model for computer aided process planning (CAPP) to find the optimal machining datum set and machining tolerances simultaneously for rotational parts. Tolerance chart and an efficient dimension chain tracing method are utilized to establish the relationship between machining datums and tolerances. A mixed-discrete nonlinear optimization model is formulated with the manufacturing cost as the objective function and blueprint tolerances and machine tool capabilities as constraints. A directed random search method, genetic algorithm (GA), is used to find optimum solutions. The computational results indicate that the proposed methodology is capable and robust in finding the optimal machining datum set and tolerances. The proposed model and solution procedure can be used as a building block for computer automated process planning.     

Tolerance analysis for setup planning: A graph theoretical approach

Setup planning is the act of preparing detailed work instructions for setting up a part. The purpose of setting up a part is to ensure its stability during machining and, more importantly, the precision of the machining processes. Therefore, tolerance control can be achieved proactively via setup planning. This fact was somewhat overlooked in the computer-aided process planning (CAPP) research community. While many researchers focused their attention on tolerance chart analysis, the issue of tolerance analysis for setup planning was relatively unexplored. To systematically solve the setup planning problem, a graph theoretical approach is proposed. The design specification of a part is represented as a graph. The problem of identifying the optimal setup plan is transformed into a graph search problem. A setup planning algorithm for rotational parts was then developed. The algorithm was evaluated and found to be both efficient and effective.     

Automated setup planning in CAPP: a modified particle swarm optimisation-based approach

Comprehensive process planning is the key technology for linking design and the manufacturing process and is a rather complex and difficult task. Setup planning has a basic role in computer-aided process planning (CAPP) and significantly affects the overall cost and quality of machined parts. This paper presents a generative system and particle swarm optimisation algorithm (PSO) approach to the setup planning of a given part. The proposed approach and optimisation methodology analyses constraints such as the TAD (tool approach direction), the tolerance relation between features and feature precedence relations, to generate all possible process plans using the workshop resource database. Tolerance relation analysis has a significant impact on setup planning to obtain part accuracy. Based on technological constraints, the PSO algorithm approach, which adopts the feature-based representation, optimises the setup planning using cost indices. To avoid becoming trapped in local optima and to explore the search space extensively, several new operators have been developed to improve the particles’ movements, combined into a modified PSO algorithm. A practical case study is illustrated to demonstrate the effectiveness of the algorithm in optimising the setup planning.     

基于有限元分析的薄壁圆环零件加工工艺研究

目的 针对薄壁圆环零件刚性差、强度弱、加工过程中易发生受力变形的难题,基于薄壁圆环零件加工成形工艺,优化零件的加工工艺和装夹方式。方法 考虑到工艺对零件加工质量的影响,对零件的加工方法和装夹方式进行研究,提出一种新的加工工装,运用ANSYS软件对零件装夹受力情况和振动变形进行有限元模拟仿真分析。结果 该工装不仅能够防止零件发生应力集中,还提升了零件加工精度和表面质量,工装设计为一夹一顶方式,只需调整顶尖压紧力便可确保圆环件在加工中不会发生变形,同时拆装方便,提高了生产效率。结论 对于薄壁圆环件的数控加工,可通过科学设计零件加工工艺流程和装夹工装,解决零件受力变形问题,保证薄壁圆环零件的尺寸、形位公差和表面粗糙度符合要求,提高了生产加工效率,满足产品批量生产使用要求,为类似薄壁件的加工提供了参考。     

Algorithms for grouping machining operations and planning workpiece location under dynamic machining conditions

In this paper, algorithms are presented to develop an integrated system to generate machining groups for set-ups and to plan workpiece location layouts for fixture design. The objective of the proposed system is to overcome the deficiencies of existing set-up planning and fixture design approaches. Parts are described in terms of machining features that consist of attributes required for automation and integration. The approaches of machining feature-based design and precedence matrix algorithm are combined to automate the process-planning task and to generate the machining groups for set-ups. An algorithm for selecting the locating and clamping positions of the workpiece is presented to configure the fixture in terms of modular elements. Fixture configuration verification analysis is carried out for time-varying machining forces to ensure that the workpiece will be held against cutting and clamping forces. The chip-removal effect is taken into account for stability analysis using finite-element analysis technique. An example is included to illustrate the application of proposed system.     

A hybrid approach for the capacitated lot sizing problem with setup carryover

The capacitated lot sizing problem with setup carryover deals with the issue of planning multiple products on a single machine. A setup can be carried over from one period to the next by incorporating the partial sequencing of the first and last product. This study proposes a novel hybrid approach by combining Genetic Algorithms (GAs) and a Fix-and-Optimise heuristic to solve the capacitated lot sizing problem with setup carryover. Besides this, a new initialisation scheme is suggested to reduce the solution space and to ensure a feasible solution. A comparative experimental study is carried out using some benchmark problem instances. The results indicate that the performance of the pure GAs improves when hybridised with the Fix-and-Optimise heuristic. Moreover, in terms of solution quality, promising results are obtained when compared with the recent results in the literature.