装配仿真关键技术的研究和实现

装配顺序规划和装配路径规划 ,是进行模型预装配的两项关键技术。本文提出了实施装配顺序规划的优先关系约束半角矩阵法 ,并利用Pro/TOOLKIT提供的动画功能 ,基于Pro/E建立了一种装配仿真系统。该系统能够形象、直观地进行产品预装配 ,验证和改进产品的装配工艺。     

采用边界拉伸的干涉检验方法与装配顺序规划

干涉检验及装配顺序规划为虚拟装配中的关键技术和重要研究内容。干涉检验的一般算法是用离散运动轨迹求得动态干涉,但其运动步长很难掌握。提出一种基于边界拉伸的干涉检验方法,将离散干涉检验转化为连续干涉检验,减少了运算量,提高了效率。并在此干涉检验算法基础上,进一步对装配顺序进行规划。实践表明,方法行之有效。     

基于规则的装配顺序规划方法研究

本文首先分析了将产品中各级零部件的层次结构关系和装配约束关系紧密结合为一体的产品装配结构树模型（Product Assembly Structural Model Tree)，并在此基础上提出了一套基于规则的面向装配设计的产品装配顺序规划方法。     

面向装配工序交叉的虚拟装配工艺信息模型

虚拟装配工艺规划中目前大都采用装配顺序描述装配过程,但尚无法表达装配工序交叉和设计中的安装布置'捌整等问题.考虑设计阶段和制造阶段的装配要求,建立了基于装配任务的装配工艺信息模型.该模型以多层次的装配进程表达产品装配工艺过程,通过任务对象的交叉安装表达工序交叉和安装布置凋整,并给出了装配调整策略;针对冗余装配任务提出了装配任务合并方法.最后通过实例验证了文中模型的正确性和有效性.     

基于图的子装配识别与装配序列规划

本文基于图建立了装配模型,并提出了一种基于配合关联图的子装配识别和装配序列 规划方法．直接从配合关联图出发识别出围绕箱体、轴和螺纹联接等典型零件所形成的子装 配;对配合关联图进行了简化,完成了基于配合关联图的装配序列规划．     

面向产品装配序列规划的智能优化算法库

针对复杂产品装配规划的组合爆炸和盲目搜索难题,以及单个智能优化算法各自存在的缺点,提出一种用装配序列智能优化算法库解决装配序列规划问题的方法.装配序列规划智能优化算法库主要由算法顾问和算法池构成,算法顾问依据装配规划问题的描述、算法可量化性能的主要参考指标和经验公式,向装配规划人员推荐解决规划任务的最合适算法;算法池包括改进的遗传算法、蚁群算法和模拟退火算法等3种智能优化算法.建立了统一的装配序列规划优化模型和智能算法评价指标体系,并给出了装配序列规划智能优化算法库的具体操作流程.最后结合瓶塞开启机实例,验证了该算法库为装配规划人员推荐的智能优化算法是合理的.     

机器人装配操作的规划与控制

机器人装配操作是机器人研究与应用的一个重要领域．与机器人焊接、喷涂等作业 不同,机器人的装配操作是一种约束运动类操作,因此在规划和控制时必须考虑接触约束和 各种不确定性因素的影响．本文对机器人装配操作的规划和控制策略进行了详细的综述,并 对一些典型的装配操作任务进行了分析．文末提出了需要进一步深入研究的一些问题．     

面向机器人装配设计与规划的集成框架

本文针对面向机器人装配的产品设计与装配规划中的关键问题,分析实际产品设计与 装配规划的一般过程,提出了将面向机器人装配的产品设计与装配规划进行系统集成的框架 ,并对系统的各个模块的功能和实现作了研究探讨,通过在工程实践中的应用验证了其正确 性和有效性．     

基于配合特征的机械装配自动规划

本文根据机械装配配合特征确定装配操作优先顺序,并设计了相应的算法。提出利用布尔矢量表示装配优先顺序,利用图的拓扑排序实现装配任务的自动规划。     

计算机辅助装配顺序规划技术概述

装配顺序是描述产品装配过程的一个重要信息.对装配顺序的设计,一直以来都是根据设计者的经验,采用手工方法完成,这很难形成统一的评价标准,并且会遗漏好的装配方案,造成装配成本、时间的浪费.如何利用计算机辅助技术建立最佳装配顺序方案,是目前工艺领域的研究热点.     

Geometric computation based assembly sequencing and evaluating in terms of assembly angle, direction, reorientation, and stability

Assembly sequence matters much to the performance in assembly production. Focusing on the spatial assembly sequencing and evaluating, a set of geometric computation methods and algorithms are studied systematically. A method entitled 3D geometric constraint analysis (3D-GCA) is proposed based on the planar GCA method combined with the techniques of oriental bounding boxes and the separation axis theorem. With 3D-GCA, the assembly precedence relations and the spatial geometric feasible assembly sequences can be reasoned out correctly and automatically. Furthermore, four evaluation criteria, viz. assembly angle, assembly direction, reorientation, and stability, and related algorithms are defined for evaluating the assembly’s complexity. For selecting the optimal sequence, a comprehensive evaluation function is constructed by integrating the four criteria and the weights are quantitatively allocated referring to fuzzy set theory, clustering analysis, and entropy theory. In addition, a software prototype system is developed and two case assemblies are studied. The analysis results and findings demonstrate that the proposed approaches and algorithms can provide significant assistance in the spatial assembly sequencing and the optimal sequence selection.     

A hybrid genetic algorithm for multi-objective product plan selection problem with ASP and ALB

Facing current environment full of a variety of small quantity customized requests, enterprises must provide diversified products for speedy and effective responses to customers’ requests. Among multiple plans of product, both assembly sequence planning (ASP) and assembly line balance (ALB) must be taken into consideration for the selection of optimal product plan because assembly sequence and assembly line balance have significant impact on production efficiency. Considering different setup times among different assembly tasks, this issue is an NP-hard problem which cannot be easily solved by general method. In this study the multi-objective optimization mathematical model for the selection of product plan integrating ASP and ALB has been established. Introduced cases will be solved by the established model connecting to database statistics. The results show that the proposed Guided-modified weighted Pareto-based multi-objective genetic algorithm (G-WPMOGA) can effectively solve this difficult problem. The results of comparison among three different kinds of hybrid algorithms show that in terms of the issues of ASP and ALB for multiple plans, G-WPMOGA shows better problem-solving capability for four-objective optimization.     

An integrated job shop scheduling and assembly sequence planning approach for discrete manufacturing

This paper proposes an integrated job shop scheduling and assembly sequence planning (IJSSASP) approach for discrete manufacturing, enabling the part processing sequence and assembly sequence to be optimized simultaneously. The optimization objectives are to minimize the total production completion time and the total inventory time of parts during production. The interaction effects between the job shop schedule and the assembly sequence plan in discrete manufacturing are analyzed, and the mathematical models including the objective functions and the constraints are established for IJSSASP. Based on the above, a non-dominated sorting genetic algorithm-II (NSGA-Ⅱ) with a hybrid chromosome coding mechanism is applied to solve the IJSSASP problem. Through the case studies and comparison tests for different scale problems, the proposed IJSSASP approach is verified to be able to improve the production efficiency and save the manufacturing cost of the discrete manufacturing enterprise more effectively.     

A systematic optimization approach for assembly sequence planning using Taguchi method, DOE, and BPNN

Research in assembly planning can be categorised into three types of approach: graph-based, knowledge-based and artificial intelligence approaches. The main drawbacks of the above approaches are as follows: the first is time-consuming; in the second approach it is difficult to find the optimal solution; and the third approach requires a high computing efficiency. To tackle these problems, this study develops a novel approach integrated with some graph-based heuristic working rules, robust back-propagation neural network (BPNN) engines via Taguchi method and design of experiment (DOE), and a knowledge-based engineering (KBE) system to assist the assembly engineers in promptly predicting a near-optimal assembly sequence. Three real-world examples are dedicated to evaluating the feasibility of the proposed model in terms of the differences in assembly sequences. The results show that the proposed model can efficiently generate BPNN engines, facilitate assembly sequence optimisation and allow the designers to recognise the contact relationships, assembly difficulties and assembly constraints of three-dimensional (3D) components in a virtual environment type.     

A physically based approach with human–machine cooperation concept to generate assembly sequences

Assembly plan is considered one of the important stages to minimize the cost of manufacturer and to ensure the safety of assembly operation, the main problem of assembly sequence planning approach is how to reduce the deviation from the real manufacture conditions. In this paper, we have extensively investigated a novel approach to automatically generate the assembly sequences for industrial field, which is especially applied to other large-scale structures. A physically based assembly representation model includes not only the pre-determined basic assembly information, such as precedence relations between parts or subassemblies, geometric constraints, different assembly types, and also the dynamic real-time physical properties, such as the center position of gravity, the force strength of the part, et al. This representation model considered the influences on optimum sequences by assembly operations will be modified by the feedback from interactive virtual environment. Then, we select the safety, efficiency and complexity as the optimization objectives. A hybrid search approach may be used to find the optimum assembly sequence, which will be integrated into an interactive assembly virtual environment (IAVE). It means that the results of assembly interaction can be provided to update the assembly planning model as a feedback, by which the approach will take advantages of the immune memory for local optimum search. The user can adjust the assembly sequences with obvious good objective by interaction with IAVE to improve the performance of the search algorithm. We describe human–machine cooperation (HMC) method for ASP in this work, by which human also can play a pivotal role instead of pure soft-computing. A series of numerical experiments are done to validate the performance of the physically based approach (PBA) to generate assembly sequence, which shows the efficiency and the operability to guide the assembly work.     

Analysis of assembly operations’ difficulty using enhanced expert high-level colored fuzzy Petri net model

Majority of the products can be assembled in several ways that means the same final product can be realized by different sequences of assembly operations. Different degree of difficulty is associated with each sequence of assembly operation and such difficulties are caused by the different mechanical constraints forced by the different sequences of operations. In the past, few notable attempts have been made to represent and enumerate the degree of difficulty associated with an assembly sequence (in the form of triangular fuzzy number) by using the concept of assembly graph. However, such representation schemes do not possess the capabilities to model the user's reasoning and preferences. In this research, an intelligent Petri net model that combines the abilities of modelling, planning and performance evaluation for assembly operation is presented. This modelling tool can represent the issues concerning degree of difficulty associated with assembly sequences. The proposed mechanism is enhanced expert high-level colored fuzzy Petri net (EEHLCFPN) that is a hybrid of knowledge-based system and colored Petri net. An example encompassing assembly of subassemblies is considered to efficiently delineate the modelling capabilities of proposed hybrid petri net model.     

Tool selection-embedded optimal assembly planning in a dynamic manufacturing environment

This paper presents an approach for tool selection-embedded optimal assembly planning in dynamic manufacturing environments. It aims to embed assembly tools into the planning process of assembly sequences in a dynamic shop-floor. The experimental results demonstrate that the developed approach is efficient and practical for a high fidelity assembly sequence with alternatives of assembly-tool sets. The dynamic assembly planning can efficiently support product assembly by generating feasible assembly sequences. It provides an effective design-aiding tool to virtually deal with various what-if scenarios regarding product assembly. In particular, the Web-based application developed in this research can be incorporated into a high-performance design and manufacturing environment on the Web, forming a distributed, collaborative and globally networked tool for product assembly planning.     

Using requirement-functional-logical-physical models to support early assembly process planning for complex aircraft systems integration

The assembly line process planning connects product design and manufacturing through translating design information to assembly integration sequence. The assembly integration sequence defines the aircraft system components installation and test precedence of an assembly process. This activity is part of the complex systems integration and verification process from a systems engineering view. In this paper, the complexity of modern aircraft is defined by classifying aircraft system interactions in terms of energy flow, information data, control signals and physical connections. At the early conceptual design phase of assembly line planning, the priority task is to understand these product complexities, and generate the installation and test sequence that satisfies the designed system function and meet design requirements. This research proposes a novel method for initial assembly process planning that accounts for both physical and functional integrations. The method defines aircraft system interactions by using systems engineering concepts based on traceable RFLP (Requirement, Functional, Logical and Physical) models and generate the assembly integration sequence through a structured approach. The proposed method is implemented in an industrial software environment, and tested in a case study. The result shows the feasibility and potential benefits of the proposed method.     

Single assembly sequence to flexible assembly plan by Autonomous Constraint Generation

The factory of the future is steering away from conventional assembly line production with sequential conveyor technology, towards flexible assembly lines, where products dynamically move between work-cells. Flexible assembly lines are significantly more complex to plan compared to sequential lines. Therefore there is an increased need for autonomously generating flexible robot-centered assembly plans. The novel Autonomous Constraint Generation (ACG) method presented here will generate a dynamic assembly plan starting from an initial assembly sequence, which is easier to program. Using a physics simulator, variations of the work-cell configurations from the initial sequence are evaluated and assembly constraints are autonomously deduced. Based on that the method can generate a complete assembly graph that is specific to the robot and work-cell in which it was initially programmed, taking into account both part and robot collisions. A major advantage is that it scales only linearly with the number of parts in the assembly. The method is compared to previous research by applying it to the Cranfield Benchmark problem. Results show a 93% reduction in planning time compared to using Reinforcement Learning Search. Furthermore, it is more accurate compared to generating the assembly graph from human interaction. Finally, applying the method to a real life industrial use case proves that a valid assembly graph is generated within reasonable time for industry.