基于坐标变换的功能梯度材料空间映射建模研究

随着3D打印零件复杂性和多样性的日益增加,单一材质的零件已无法满足各类特殊性能要求,功能梯度材料零件逐渐成为快速成型制造行业的研究热点。为满足功能梯度材料的制造要求,提出了一种基于坐标变换的功能梯度材料空间映射建模方法,该方法的关键是将几何信息与材料信息有效结合。首先,采用单梯度源法、多梯度源法构建功能梯度材料模型的材料空间,在遇到交叉梯度源模型时利用特定的相交算子以一定的权重比进行材料分布计算。然后,在几何空间与材料空间结合的过程中,巧妙地通过坐标变换将材料空间与几何空间的坐标系重合,实现将材料空间映射至几何空间,从而得到带有材料信息的功能梯度材料模型。通过修改梯度源、材料分布函数以及坐标变换方法即可获得所需的功能梯度材料模型。利用Visual Studio 2019软件和OpenGL编程语言对实例模型进行可视化分析的结果表明,所提出的建模方法相比于大部分估值建模、非估值建模方法可大大缩短建模时间,从根本上解决了某些算法所造成的储存空间不足和建模过程繁琐等问题。基于坐标变换的功能梯度材料空间映射建模方法为增材制造行业提供了一种新的建模方法,具有良好的应用价值。     

Formation of independent flow-line cells based on operation requirements and machine capabilities

In this paper we study the generalized grouping problem of cellular manufacturing. We propose an operation-sequence-based method for forming flow-line manufacturing cells. Process planning in the form of selection of the machine for each operation is included in the problem formulation. Input requirements include the set of operation requirements for each part type, and operation capabilities for all available machine types. The objective is to find the minimum-cost set of flow-line cells that is capable of producing the desired part mix. A similarity coefficient based on the longest common operation subsequence between part types is defined and used to group parts into independent, flow-line families. An algorithm is developed for finding a composite operation supersequence for each family. Given machine options for each operation in this sequence, the optimal machine sequence and capacity for each cell is then found by solving a shortest path problem on an augmented graph. The method is shown to be efficient and computational results are reported.     

The impact of ignored attributes on a CMS design

The formation of part families and machine cells is an important design step in implementing a Cellular Manufacturing System (CMS). The effectiveness of this design step, for a practical CMS, depends heavily on the proper consideration of the relevant set of attributes. Generally, the relevant attribute set consists of both geometrical and manufacturing attributes of parts. Most published CMS design procedures use a very small number of manufacturing attributes for two reasons. First, precise information about relevant attributes may not be available, and second, considering an adequate number of attributes causes computational intractability. This paper identifies the sources of imprecision and offers an efficient sensitivity analysis procedure for examining the impact of ignored attributes on a CMS design. The sensitivity analysis is based on the theory of Rough Sets and assumes that an initial design has been obtained by using a computationally efficient CMS design procedure. In addition, this procedure can be used to revalidate the configuration of an existing CMS when product mix or parts design has changed. Background on the theory of Rough Sets is provided and appropriate Rough Sets measures are discussed. A numerical example illustrates the proposed use of Rough Sets theory in CMS design.     

Application of manufacturing constraints to structural optimization of thin-walled structures

Topology optimization can be a very useful tool for creating conceptual designs for vehicles. Structures suggested by topology optimization often turn out to be difficult to implement in manufacturing processes. Presently, rail vehicle structures are made by welding sheet metal parts. This leads to many complications and increased weight of the vehicle. This article presents a new design concept for modern rail vehicle structures made of standardized, thin-walled, closed, steel profiles that fulfil the stress and manufacturing requirements. For this purpose, standard software for topology optimization was used with a new way of preprocessing the design space. The design methodology is illustrated by an example of the topology optimization of a freight railcar. It is shown that the methodology turns out to be a useful tool for obtaining optimal structure design that fulfils the assumed manufacturing constraints.     

Automatic identification of aspect vectors and geometrical validation of 2.5D parts

A part can be viewed from infinite viewpoints, but it may look 2.5D from some specific viewpoints only. Its solid model may be arbitrarily oriented in space as per the designer's convenience in positioning it in an overall assembly. However, this orientation may not be suitable for proper and economic manufacturing. A manufacturing engineer has to reorient the part in such a way that it looks a 2.5D part, if possible. Manufacturing of 2.5D parts is economic and efficient since standard machine tools, cutters and clamping devices are used. An algorithm is presented to automatically determine the machining coordinate system that is convenient for machining. The selected coordinate system specifies the approach directions (aspect vectors) for machining the part. The underlying principle of the algorithm is the generation of machined faces on a three-axis CNC vertical milling machine with a set of flat-bottomed end-milling cutters and standard clamping devices. The part can have only planar and cylindrical faces under such machining conditions. Another algorithm is presented to validate whether or not a part falls under the 2.5D domain. Both algorithms are required since the present state of feature-recognition technology is limited to 2.5D parts. There is a need for a pre-processing system that automatically determines the aspect vectors for a 2.5D part and checks whether or not the design fed to the feature-recognition system is valid.     

Cell formation using a Fuzzy Min-Max neural network

This paper proposes the application of a Fuzzy Min-Max neural network for part family formation in a cellular manufacturing environment. Once part families have been formed, a minimum cost flow model is used to form the corresponding machine cells. For simplicity, the input data are in the form of a binary part- machine incidence matrix, although the algorithm can work with an incidence matrix with continuous values. The application of Fuzzy Min-Max is interpreted in physical terms and compared with a related neural network applied previously for cell formation, the Fuzzy ART network. Both neural networks have similarities and differences that are outlined. The algorithms have been programmed and applied to a large set of problems from the literature. Fuzzy Min-Max generally outperforms Fuzzy ART, and the computational times are small and similar in both algorithms.     

Feature vector: a graph-based feature recognition methodology

Recognition of machining features is a vital link for the effective integration of various modules of computer integrated manufacturing systems (CIMS). Graph-based recognition is the most researched method due to the sound mathematical background of graph theory and a graph's structural similarity with B-Rep computer-aided design modellers’ database. The method, however, is criticized for its high computational requirement of graph matching, its difficulty in building a feature template library, its ability to handle only polyhedral parts and its inability to handle interacting features. The paper reports a new edge classification scheme to extend the graph-based algorithms to handle test parts with curved faces. A unique method of representing a feature, called a feature vector, is developed. The feature vector generation heuristic results in a recognition system with polynomial time complexity for any arbitrary attributed adjacency graph. The feature vector can be generated automatically from B-Rep modellers. This helps in building incrementally a feature library as per the requirements of the specific domain. The proposed system is implemented in VC++ using an ACIS® 3D solid modelling toolkit.     

Manufacturing requirements in design: The RTM process in aeronautics

A sub-unit of an aeronautical structure (fuselage, fin, wing, etc.) consists of a set of components fixed rigidly together. One of today’s major industrial challenges is to produce these sub-units out of composite materials in order to increase the level of integration and reduce mass and cost. This article describes a procedure to assist in the industrialisation of aeronautical components produced from composite materials in a design for manufacturing (DFM) context. In a multi-expertise approach, the problem of optimising integration is combined with the feasibility of injection for the resin transfer molding (RTM) process. This approach then takes into account admissible manufacturing deviations, defined from a classification of the structure parts. The limits set for admissible deviations guarantee the mechanical behaviour of the assembled component and the requirements of the assembly as a whole. Finally, an industrialisation solutions space is defined. A constraint satisfaction problem solver is used to carry out this research with a spar from a horizontal plane in an aircraft used to illustrate the procedure.     

增材制造中基于零件特征的分段自适应分层算法

对一类不同部位有明显精度区别的零件进行增材制造时，相较于传统等厚分层算法，现有的分段等厚分层算法和自适应分层算法在弱化阶梯效应及减少分层和成型时间上各有所长，但它们在协调成型精度和成型效率方面并未达到最佳。为此，根据这类零件的立体光刻（stereo lithography，STL）模型，结合分段分层和自适应分层原理，提出了基于零件特征的分段自适应分层算法。沿成型方向按不同的精度要求对模型进行分段，并根据不同精度，利用以最大尖端高度调整分层厚度的方法对各段进行自适应分层。对斗齿类、链轨节类零件进行分层模拟试验，结果表明：基于零件特征的分段自适应分层算法在满足零件形状和精度等要求的前提下，较现有的分段等厚分层算法和自适应分层算法，其成型效率更高，稳定性及灵活性更好，且易于实现；在实际工程应用中可缩短零件的成型时间，促进增材制造向高效率方向发展。     

Jointly solving the group scheduling and machining speed selection problems: A hybrid tabu search and simulated annealing approach

In a cellular manufacturing environment, once the machines and parts have been grouped the remaining tasks are sequencing part families and scheduling operations for the parts within each part family so that some planning goals such as minimization of tardiness can be achieved. This type of problem is called group scheduling and will be analysed in this paper. The solution of the group scheduling is affected by the machining speed specified for each operation since the completion time of each operation is a function of machining speed. As such, the group scheduling and machining speed selection problems should be simultaneously solved to provide meaningful solutions. This, however, further complicates the solution procedure. In view of this, a hybrid tabu-simulated annealing approach is proposed to solve the group scheduling problem. The main advantage of this approach is that a short term memory provided by the tabu list can be used to avoid solution re-visits while preserving the stochastic nature of the simulated annealing method. The performance of this new method has been tested and favourably compared with two other algorithms using tabu search and simulated annealing alone.     

HEURISTIC NONSERIAL DYNAMIC PROGRAMMING FOR LARGE PROBLEMS

Dynamic programming is an extremely powerful optimization approach used for the solution of problems which can be formulated to exhibit a serial stage-state structure. However, many design problems are not serial but have highly connected interdependent structures. Existing methods, for the solution of nonserial problems require the problem to possess a certain structure or limit the size of the problem due to storage and computational time requirements. The aim of this paper is to show that nonserial problems can be solved by the use of dynamic programming incorporating algorithms based on heuristics. Two such algorithms are developed using artificial intelligence concepts of estimating the likelihood of future results on present decisions. The algorithms are explained in detail, A small problem is solved and the results of testing them on large scale problems are given. The method is then used to solve a problem drawn from the literature.     

Analysis and solution to the single-level batch production smoothing problem

Many companies use mixed-model production systems running under the Just-in-Time philosophy in order to efficiently meet customer demands for a variety of products. Such systems require demand be stable and production sequence be leveled. The production smoothing problem aims at finding level schedules in which the appearances of products are dispersed over the horizon as uniformly as possible. In this paper, the production smoothing problem is extended to a more general manufacturing environment where a single machine can be identified as either the final or the bottleneck stage of the system and products may have arbitrary non-zero setup and processing time requirements on this single machine. An optimization model is built for the problem and a two phase solution methodology is developed. The first phase problem is shown to be NP-hard and a parametric heuristic procedure is proposed for its solution. In contrast, the second phase problem is shown to be efficiently solvable and currently available solution methods are adopted from the literature. A computational study is designed to test the proposed two phase solution methodology and also the parametric heuristic procedure. Computational results show that the proposed two phase solution methodology enables effective and efficient control of the studied manufacturing system, and the heuristic procedure developed for the first phase problem is time efficient and promises near optimal solutions for a variety of test instances.     

电弧增材制造航空钛合金构件组织及力学性能研究现状

航空航天领域通常将钛合金作为承力结构件使用,对其性能和可靠性都有很高的要求,大型结构件的整体化制造是实现这些需求的有效途径。电弧增材制造技术因效率高、成本低、致密度高,在制备大型结构件方面具有一定优势。综述了国内外电弧增材制造钛合金组织的研究现状,介绍了改变形核条件以及引入轧制、超声等外场辅助技术调控后所得的电弧增材制造钛合金组织。对电弧增材制造钛合金的拉伸性能和疲劳性能进行了综述,总结了拉伸性能和疲劳性能的特点及断裂的原因。最后,对航空航天用钛合金电弧增材制造的组织及力学性能的关系进行了分析,并且对两者的调控前景进行了展望。     

A PSO-based approach to cell formation problems with alternative process routings

Group technology (GT) has been extensively applied to cellular manufacturing system (CMS) design for decades due to many benefits such as decreased number of part movements among cells and increased machine utilisation in cells. This paper considers cell formation problems with alternative process routings and proposes a discrete particle swarm optimisation (PSO) approach to minimise the number of exceptional parts outside machine cells. The approach contains two main steps: machine partition and part-routing assignment. Through inheritance and random search, the proposed algorithm can effectively partition machines into different cells with consideration of multiple part process routings. The computational results are compared with those obtained by using simulated annealing (SA)-based and tabu search (TS)-based algorithms. Experimental results demonstrate that the proposed algorithm can find equal or fewer exceptional elements than existing algorithms for most of the test problems selected from the literature. Moreover, the proposed algorithm is further tailed to incorporate various production factors in order to extend its applicability. Four sample cases are tested and the results suggest that the algorithm is capable of solving more practical cell formation problems.     

Tool requirements planning in a flexible manufacturing system: Minimizing tool costs subject to a makespan constraint

We consider a tool requirements planning problem (TRPP) of determining the number of tool copies for each tool type in a flexible manufacturing system with an automatic tool transporter. In this paper, four heuristic algorithms are developed for the TRPP with the objective of minimizing the total tool purchase cost subject to a makespan constraint. In the algorithms, starting from an initial (infeasible) solution, the numbers of tool copies for certain tool types are increased at each iteration until the makespan constraint becomes satisfied. In these algorithms, information obtained from discrete event simulation is used to select tool types of which the numbers of copies are to be increased. Computational experiments are performed on randomly generated test problems and results show that good tool requirements plans can be obtained with the heuristic algorithms in a reasonable amount of computation time.     

Heuristic procedures for the parallel machine problem with tool switches

We address the problem of scheduling a set of parts with given processing times and tool requirements on m identical parallel machines. The problem is to find an allocation of the machines to the parts, a proper sequence for the parts assigned to each machine, and a corresponding tool-switching plan for each machine so as to minimize the makespan. It is demonstrated that this problem is np-hard , and three heuristic procedures are proposed for solving it. The first procedure is a multi-start local improvement procedure, and various neighborhood structures and search strategies are discussed in this context. The second procedure is a variation of the list-processing routine that is commonly used for the parallel machine problem. Finally, the last procedure is an adaptation of a well-known constructive procedure for the k -travelling salesman problem. Results of a limited computational experiment are also presented in which the makespan obtained via each heuristic procedure is compared with a proposed lower bound and with other reference values.     

A constructive algorithm and a simulated annealing approach for solving flowshop problems with missing operations

In many practical cases of flowshop environments and especially in flowline manufacturing cells, some or all jobs may not require processing on all machines. Hence, this paper focuses on the flowshop scheduling problem with missing operations. A modification of the constructive NPS-set heuristic is proposed, which generates non-permutation schedules effectively. Furthermore, a two-phase simulated annealing (SA) method is presented that specifically considers missing operations in its procedure. The modified NPS-set heuristic and the two-phase SA are tested and statistically evaluated by an extensive computational study for total flow time criteria. The results show that the modified NPS-set heuristic as well as the specific consideration of missing operations can enhance the algorithms’ performance significantly.     

Monte Carlo gradient estimation in high dimensions

A Monte Carlo procedure to estimate efficiently the gradient of a generic function in high dimensions is presented. It is shown that, adopting an orthogonal linear transformation, it is possible to identify a new coordinate system where a relatively small subset of the variables causes most of the variation of the gradient. This property is exploited further in gradient‐based algorithms to reduce the computational effort for the gradient evaluation in higher dimensions. Working in this transformed space, only few function evaluations, i.e. considerably less than the dimensionality of the problem, are required. The procedure is simple and can be applied by any gradient‐based method. A number of different examples are presented to show the accuracy and the efficiency of the proposed approach and the applicability of this procedure for the optimization problem using well‐known gradient‐based optimization algorithms such as the descent gradient, quasi‐Newton methods and Sequential Quadratic Programming. Copyright © 2009 John Wiley & Sons, Ltd.     

Route selection and flow control in a multi-stage manufacturing system with heterogeneous machines within stages

With increasing market competition and the need to serve diverse market segments, many companies are responding to customer needs by manufacturing parts in small batches. Unless properly planned, batch production can lead to high material handling time. In this paper, a methodology for selecting the best routeing for a batch in a multi-stage production system with material handling considerations is presented. Each stage consists of multiple, functionally identical machines that have non-identical characteristics. The model determines the optimal routeing for the batch and generates the number of parts to handle or transport at a time as a unit load between machines in order to minimize total batch manufacturing time. The result from the model provides the optimal routeing (i.e., the machine to use at each production stage) and the number of parts to move at a time as a sub-lot. The solution procedure developed is easy to use and can be incorporated as a daily shop management tool.     

A multi-objective genetic algorithm approach to the design of cellular manufacturing systems

In this paper, a multi-objective integer programming model is constructed for the design of cellular manufacturing systems with independent cells. 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. There are some features that make the proposed algorithm different from other algorithms used in the design of cellular manufacturing systems. These include: (1) a systematic uniform design-based technique, used to determine the search directions, and (2) searching the solution space in multiple directions instead of single direction. Four problems are selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method in designing the manufacturing cells.