加工过程决策的分级动态规划策略

基于在加工过程分析的基础上,建立了适合于加工过程决策的基于表面的零件网络信息模型,在此基础上,探讨了加工过程优化决策的方法,提出分级动态规划的工艺过程的决策策略,研究了加工优化排序的算法。     

智能化CAPP系统的构造方法

提出了基于知识的智能化CAPP系统的构造方法。工艺决策模块使用OPS（official production system）作为专家系统工具，采用演绎推理式的推理机，实现了一系列的工艺决策。     

基于智能Agent的农业智能决策系统研究

提出了一种基于知识的智能Agent逻辑模型,该模型以知识为核心,以推理作为主要规划手段。在此逻辑模型的基础上,提出了以本体为核心的Agent内部知识表示方法。并在任务及实体定义的基础上,结合产生式知识表示方法,构造了规划表示方法,形成了一个完整的智能Agent内部知识模型并基于黑板模型设计了智能Agent的实现结构以及其中的关键算法——规划过程。设计了基于智能Agent及黑板机制的农业智能决策系统的基本框架,并以农场作物生产决策系统证实了该模型能够满足大规模分布式农业决策系统的需求。     

智能化CAPP决策

机械制造系统中联结物质流和信息流的载体CAPP是集成制造系统和柔性制造系统的关键环节。人工智能(AI)技术在CAPP决策中显示了其特殊的优越性,制造技术的发展由原始集成、、低技术经济、规模经济到高技术经济、范围经济,经历了艰难的发展历程。我国机械制造技术要适应多变市场的需求,从根本上解决决生产上的落后状况,不能不加强工艺及相关技术的研究和应用。工艺设计及工艺的智能决策是计算机集成制造系统(CIMS)中同制造紧密相关的一个重要方面。智能化CAPP决策是以推理机和知识库为基础,首先将工艺知识加以描述,建立工艺设计系统的专用推理机和知识库,然后根据推理机的推理机制和知识库的专业系统知识设计零件的加工工艺路线、刀具的运动轨迹、切削参数的合理选择、优化等。因此,推理机和知识库的设计和实现,是智能化CAPP决策的关键。智能化CAPP的评介的主要性能指标就与推理机和知识库的可靠性和适用性有密切的关系。     

基于实例的CAPP策略

从充分利用工艺规程编制实例的角度出发，提出了用基于实例的推理改进ＣＡＰＰ推理方法的策略，并具体介绍了将基于实例的推理与基于知识的推理有机结合的混合推理系统的具体结构、实例的表示、提取方法以及基于知识、实事库的正确性维护与版本管理的实例修改策略。     

基于炮尾的工艺路线设计专家系统研究

针对炮尾加工工艺复杂且传统工艺规程不能直观反映加工工艺路线等问题,提出一种基于炮尾加工工艺路线专家系统的开发方法.在分析炮尾加工路线的基础上建立加工工艺路线知识库,对其加工工艺进行规律性推理,实现专家系统开发必需的知识库和推理机,并利用专家系统开发工具DEST(Design Expert System Tools)实现炮尾工艺路线设计专家系统的开发.     

MEMS版图自动生成与工艺匹配系统研究

根据MEMS工艺设计的特点提出了反馈式专家系统运作方式,并据此给出了MEMS版图自动生成与工艺匹配系统的体系结构.同虚拟MEMS加工工艺相比,该系统可实现工艺流水的逆向推理流程,由此,进一步给出了系统逆向推理所需的专家规则描述方式和推理机实现方法,并实现了一个原型系统,该系统可实现由两种标准MEMS工艺制作器件的版图自动生成与工艺匹配工作,初步验证了系统的可行性.     

工艺知识推理使能模型及其实现技术

重点研究支持知识推理的零件特征信息模型,知识库模型,推理机模型等模型,并详细建立选择毛坯,创成多条可行工艺路线,选择加工设备及工装,计算切削用量和工时定额,编制工艺文件等各类知识推理的使能方法。     

敏捷制造中基于AI及Internet技术的电火花加工工艺决策

以电火花加工为例提出了一种实现敏捷制造技术的新方法。这种方法基于人工智能及因特网技术与传统制造技术的集成，通过网络化电火花加工工艺智能决策系统的软件以低成本远程快速的使用设计制造专家知识，从而实现设计制造的敏捷化。专家知识包括电火花加工工艺参数的智能选择及推理。通过因特网选择不同应用的加工条件与专家进行动态交互。本文得出结论，基于人工智能技术及因特网的敏捷制造技术的未来应用具有潜在的利益。     

使用表达式分析的通用故障诊断系统设计与实现

基于知识的故障诊断专家系统在应用时面临知识获取、知识表达和知识与诊断推理有机融合等几方面的困难。本文论述了一种使用表达式解析的故障诊断方法,并将其用于某型雷达系统的故障诊断保障系统中。该方法将知识表示为逻辑表达式,推理机使用表达式解析的方法推理知识。使用该方法,知识的获取和更新更加方便,并且推理机和表达式相互独立,具有相当的通用性。     

Reducing tardy jobs by integrating process planning and scheduling functions

This paper presents a dynamic approach to reduce tardy jobs through the integration of process planning and scheduling in a batch-manufacturing environment. The developed method aims at re-generating a schedule with fewer tardy jobs, step by step, by exploring the process plan solution space of the tardy jobs. The integrated system comprises a process planning module, a scheduling module, and an integrator module. The process planning module employs an optimisation approach in which the entire plan solution space is first generated and a search algorithm is then used to find the optimal plan, while the scheduling module is based on commonly used heuristics. Based on the job tardiness information of the generated schedule, the integrator module automatically issues a modification order to the process plan solution space of the tardy jobs. The process planning and scheduling modules are then re-run to generate a new plan/schedule solution. Through this iterative process, a satisfactory schedule can be gradually achieved. The uniqueness of this approach is characterised by the flexibility of the process planning strategy, which makes full use of the plan solution space intuitively to achieve a satisfactory schedule. Several examples are presented to confirm the efficacy and the effectiveness of the developed integration system.     

Applying case-based reasoning in assembly sequence planning

Assembly sequence planning (ASP) is the foundation of the assembly process planning which plays a key role in the whole product life cycle. In this paper, a unique ASP reasoning method supported by the artificial intelligent technique of case-based reasoning (CBR) is proposed and developed. First, based on the previous ASP literatures review and the CBR characteristics analysis, the systematic architecture of the CBR based ASP is presented. Then, some key techniques including assembly case modelling, similar case retrieving, case based reasoning, and case base maintenance, etc., are explored thoroughly. To enhance the efficiency and quality of the reasoning process, genetic algorithm (GA) is designed and applied to automatically inferring of the reference assembly sequence. Finally, the corresponding software system with an engineering example is given to demonstrate the effectiveness of the CBR based ASP.     

Process and production planning for circuit board assembly

Process planning output can be post-processed into criteria for job scheduling decisions in printed circuit board assembly using surface-mount technology (SMT). Artificial intelligence-based techniques used in computerized planning and scheduling systems in other industries can be extended to printed circuit board operations. These techniques include blackboard architectures, object-oriented programming systems, and nonmonotonic reasoning systems. These techniques were used to demonstrate a unique architecture of blackboard systems that communicate via object-oriented messages to arrive at a shop-floor process plan and production schedule. The methodology was specialized to the assembly of printed circuit boards using surface-mounted components in a high-variety/low-volume product mix. The assembly facility was represented as a hierarchical object of product, process, and organizational knowledge. The system of working heuristics was integrated within a prototype environment with the practitioners that assisted in its development. The end result is a good working methodology for system development, implementation, and maintenance by knowledge worker involvement.     

GA-based adaptive setup planning toward process planning and scheduling integration

Setup planning of a part for more than one available machine is a typical combinatorial optimisation problem under certain constraints. It has significant impact not only on the whole process planning but also on scheduling, as well as on the integration of process planning and scheduling. Targeting the potential adaptability of process plans associated with setups, a cross-machine setup planning approach using genetic algorithms (GA) for machines with different configurations is presented in this paper. First, based on tool accessibility analysis of different machine configurations, partially sequenced machining features can be grouped into certain setups; then by responding to the requirements from a scheduling system, optimal or near-optimal setup plans are selected for certain criteria, such as cost, makespan and/or machine utilisation. GA is adopted for the combinatorial optimisation, which includes gene pool generation based on tool accessibility examination, setup plan encoding and fitness evaluation, and optimal setup plan selection through GA operations. The proposed approach is implemented in a GA toolbox, and tested using a sample part. The results demonstrate that the proposed approach is applicable to machines with varying configurations, and adaptive to different setup requirements from a scheduling system due to machine availability changes. It is expected that this approach can contribute to process planning and scheduling integration when a process plan is combined with setups for alternative machines during adaptive setup planning.     

A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems

Integrated process planning and scheduling (IPPS) is a manufacturing strategy that considers process planning and scheduling as an integrated function rather than two separated functions performed sequentially. In this paper, we propose a new heuristic to IPPS problem for reconfigurable manufacturing systems (RMS). An RMS consists mainly of reconfigurable machine tools (RMTs), each with multiple configurations, and can perform different operations with different capacities. The proposed heuristic takes into account the multi-configuration nature of machines to integrate both process planning and scheduling. To illustrate the applicability and the efficiency of the proposed heuristic, a numerical example is presented where the heuristic is compared to a classical sequential process planning and scheduling strategy using a discrete-event simulation framework. The results show an advantage of the proposed heuristic over the sequential process planning and scheduling strategy.     

Enriched machining feature-based reasoning for generic machining process sequencing

This paper presents an enriched machining feature (EMF)-based reasoning approach to generic machining process sequencing for distributed process planning (DPP). An EMF is represented by combining its machining volume with surface, geometric and volume features, as well as other technological information needed to machine the feature. The information embedded in the EMF is retrieved progressively for machining sequence generation. Following an introduction of EMF and its representation scheme, the problems in determining machine-independent feature groups (set-ups) in DPP and their machining sequences to be followed for a given part are investigated. Based on the EMF concept, five reasoning rules are formulated and the algorithms developed. As the set-ups and sequences are generated based on manufacturing constraints and datum references but separated from specific resources, they are generic and applicable to machine tools with varying configurations and capabilities. This approach is further validated through a case study.     

Feature-based geometric reasoning for process planning

We present a framework based on Domain Independent Form (DIF) features for automatic evaluation of manufacturability and process planning for machining. The framework enables interpretation of a common product model with respect to each task in the transition from design to manufacture. A key idea here is to generate the interpretation suitable for each task in two steps. In the first step, DIF features that are defined through feature enumeration are automatically extracted from the geometric model. The extracted DIF features are then mapped into features meaningful for individual tasks through geometric reasoning based on domain dependent knowledge. The formal approach to feature definitions and separation of the domain specific reasoning from the general geometric reasoning enable us to overcome the bottlenecks reported in features technology. Work reported in this paper has been funded in part by grants from Aeronautical Development Agency and the Department of Science and Technology.     

基于实例的模糊工艺推理技术研究

介绍了智能型CAPP系统的体系结构,研究了工艺实例分类索引树机制和基于模糊推理的工艺实例匹配算法,解决了具有复杂特征属性的工艺实例相似检索问题,实现了基于实例模糊推理的智能CAPP系统.     

轴瓦类零件的CAPP系统开发

本文介绍了一套适用于轴瓦行业的CAPP系统。叙述了基于工序代码优先数和工艺决策自动生成轴瓦类零件的加工工艺以及基于GT代码和工序图特征图形要素库自动生成轴瓦零件加工工序图的方法。