Using mixed-type reasoning in computer-aided process planning for feature interactions

This paper describes a process-planning model using mixed-type reasoning designed for processing prismatic parts on CNC machine tools in a batch manufacturing environment. This mixed-type reasoning handles feature interactions by combining forward chaining for feature sequencing and backward chaining for the construction of a process plan. In such a model, the human problem-solving strategies are decoupled from the tools for analysis and sorting algorithms. Two databases are used to contain the results from forward and backward chaining. The process-planning algorithm combines the processes of modeling the given information in four stages: (1) defining the important information for features and feature-related concerns; (2) interpreting and rearranging the given feature according to the given constraints and sorting guideline; (3) sequencing the features; (4) attaching the needed operations to the features in machine/process/feature/set-up/tool/time/cost format.     

Integrated process planning using tool/process capabilities and heuristic search

CAD–CAM integration has involved either design with standard manufacturing features (feature-based design), or interpretation of a solid model based on a set of predetermined feature patterns (automatic feature recognition). Thus existing approaches are limited in application to predefined features, and also disregard the dynamic nature of the process and tool availability in the manufacturing shop floor. To overcome this problem, we develop a process oriented approach to design interpretation, and model the shape producing capabilities of the tools into tool classes. We then interpret the part by matching regions of it with the tool classes directly. In addition, there could be more than one way in which a part can be interpreted, and to obtain an optimal plan, it is necessary for an integrated computer aided process planning system to examine these alternatives. We develop a systematic search algorithm to generate the different interpretations, and a heuristic approach to sequence operations (set-ups/tools) for the features of the interpretations generated. The heuristic operation sequencing algorithm considers features and their manufacturing constraints (precedences) simultaneously, to optimally allocate set-ups and tools for the various features. The modules within the design interpretation and process planner are linked through an abstracted qualitative model of feature interactions. Such an abstract representation is convenient for geometric reasoning tasks associated with planning and design interpretation.     

A case-based reasoning approach for automating disassembly process planning

One of the first processes for preparing a product for reuse, remanufacture or recycle is disassembly. Disassembly is the process of systematic removal of desirable constituents from the original assembly so that there is no impairment to any useful component. As the number of components in a product increases, the time required for disassembly, as well as the complexity of planning for disassembly rises. Thus, it is important to have the capability to generate disassembly process plans quickly in order to prevent interruptions in processing especially when multiple products are involved. Case-based reasoning (CBR) approach can provide such a capability. CBR allows a process planner to rapidly retrieve, reuse, revise, and retain solutions to past disassembly problems. Once a planning problem has been solved and stored in the case memory, a planner can retrieve and reuse the product's disassembly process plan at any time. The planner can also adapt an original plan for a new product, which does not have an existing plan in case memory. Following adaptation and application, a successful plan is retained in the case memory for future use. This paper presents the procedures to initialize a case memory for different product platforms, and to operate a CBR system, which can be used to plan disassembly processes. The procedures are illustrated using examples.     

An application-independent methodology of feature recognition with explicit representation of feature interaction

The existing feature-based design and feature recognition methods cannot fulfil the requirements of automated process planning. It is now recognized that satisfactory modelling of interactions between features is necessary for developing an automated process planning system. The selection of an optimum manufacturing process for a part needs to be considered at the conceptual design phase to incorporate the capabilities and constraints of the process in design. This paper describes a methodology of feature recognition that is independent of manufacturing process and explicitly generates geometric feature interactions in a part. The paper illustrates generation of feature sets for shape-forming processes, and describes a method to convert the process-independent features into machinable volumes and tool paths for material removal processes.     

基于光照不变感知特征的打火石检测识别

为了在生产过程中确定打火石的方向,提高打火石安装定位的效率,根据打火石圆柱状外形及两端面的颜色特征,使用特定的棋盘形工装定位装置对打火石进行定位,并利用彩色机器视觉技术对打火石的端面颜色进行检测识别。为了准确识别出打火石的品红色端面,在拮抗色感知特征基础上,针对现场环境中光线不均等问题,进行了光照不变的拮抗色感知特征变换,降低了光照不均对检测结果的影响。对于打火石另一端面银白色特征,根据其高亮的特性,采用Otsu自适应阈值法进行分割,并在颜色通道的统计基础上,对银白色特征提取过程进一步进行约束。实验结果表明,采用光照不变的拮抗色感知特征提取算法以及阈值分割相结合的方法,能够有效对打火石的端面颜色进行识别,在不出现误检的情况下,品红色端面的识别准确率为99.87%,银白色端面的识别准确率为99.62%,满足了打火石视觉检测识别系统的性能要求。     

Feature extraction and feature based design approaches in the development of design interface for process planning

The quest for completely automated process planning systems has exposed the lack of techniques capable of automatically understanding the stored CAD models in a manner suitable for process planning. Most current generations of process planning systems have used the ability of humans to translate the part drawing requirements into a form suitable for computer aided process planing. Recently, research advances have been made to improve the understanding of computer stored 3-D part models. The two approaches used are feature recognition and feature based design. This paper presents a state of the art review of feature recognition techniques developed and presents feature based design as an alternative. Process planning systems developed using both approaches are presented.     

A repository for design, process planning and assembly

This paper provides an introduction to the Design, Planning and Assembly Repository available through the National Institute of Standards and Technology (NIST). The goal of the Repository is to provide a publically accessible collection of 2D and 3D CAD and solid models from industry problems. In this way, research and development efforts can obtain and share examples, focus on benchmarks, and identify areas of research need. The Repository is available through the World Wide Web at URL http://www.parts.nist.gov/parts.     

CaBMA: a case-based reasoning system for capturing,refining, and reusing project plans

In this paper, we present CaBMA, a prototype of a knowledge-based system designed to assist with project planning tasks using case-based reasoning. CaBMA introduces a novel approach to project planning in that, for the first time, a knowledge layer is added on top of traditional project management software. Project management software provides editing and bookkeeping capabilities. CaBMA enhances these capabilities by automatically capturing project plans in the form of cases, refining these cases over time to avoid potential inconsistency between them, reusing these cases to generate plans for new projects, and indicating possible repairs for project plans when they derive away from existing knowledge. We will give an overview of the system, provide a detailed explanation on each component, and present an empirical study based on synthetic data.     

A knowledge-based inspection process planning system for coordinate measuring machines

The coordinate measuring machine (CMM) is one of the most effective geometry inspection facilities used in manufacturing industry. To fully utilize its capabilities in a computer-integrated manufacturing (CIM) environment, we should integrate CMM with other systems and facilities. This paper presents the development of a knowledge-based inspection planner based on the fundamental principles of AI planning to integrate computer-aided design systems and CMMs. The issues involved in CAD-directed inspection process planning are examined; the task of inspection process planning is decomposed into a number of sub-tasks. According to the task decomposition, a knowledge-based planning system was designed with several modules. Each of these modules consists of a knowledge base, a control operator, a context and a communication interface. The knowledge base is the local knowledge source for problem solving; the control operator determines when and where the knowledge is applied; the context contains the initial planning state which is essential input part information, the intermediate planning states which result from the tentative decision made by the modules, and the goal state. The module interfacing was realized by directly calling procedures defined in other modules to pass the planning tasks and decisions. Examples are included to explain the planning knowledge and strategy.     

A case-based reasoning system development for statistical process control: Case representation and retrieval

Statistical process control (SPC) is a sub-area of statistical quality control. Considering the successful results of the SPC applications in various manufacturing and service industries, this field has attracted a large number of experts. Despite the development of knowledge in this field, it is hard to find a comprehensive perspective or model covering such a broad area and most studies related to SPC have focused only on a limited part of this knowledge area. According to many implemented cases in statistical process control, case-based reasoning (CBR) systems have been used in this study for developing of a knowledge-based system (KBS) for SPC to organize this knowledge area. Case representation and retrieval play an important role to implement a CBR system. Thus, a format for representing cases of SPC and the similarity measures for case retrieval are proposed in this paper.     

A PDES/STEP-based information model for computer-aided process planning

Computer Aided Process Planning (CAPP) constitutes one of the most essential elements in Computer Integrated Manufacturing (CIM). Although many CAPP systems have been reported in literature during the last two decades, few of them are compatible enough to integrate easily with other systems in the CIM environment. One major reason is the lack of an effective method to represent the information required by CAPP, and to unify such information with the information of other systems in the CIM environment. Indeed, this problem has received relatively inadequate attention in the recent research of CAPP systems. In this paper, an information model for CAPP is developed by using the object-oriented modeling and the Product Data Exchange Step/STandard of Exchange Product data (PDES/STEP) techniques. The model consists of the part information model, the process plan information model, and the production resource information model. The EXPRESS language or the EXPRESS-G diagram is used to represent these models. Indeed, the proposed information model will greatly improve the CAPP system's capability of effective integration with other systems in the CIM environment, and, ultimately, to facilitate the implementation of the whole CIM strategy in manufacturing enterprises.     

Fuzzy-based discriminative feature representation for children's speech recognition

Automatic recognition of the speech of children is a challenging topic in computer-based speech recognition systems. Conventional feature extraction method namely Mel-frequency cepstral coefficient (MFCC) is not efficient for children's speech recognition. This paper proposes a novel fuzzy-based discriminative feature representation to address the recognition of Malay vowels uttered by children. Considering the age-dependent variational acoustical speech parameters, performance of the automatic speech recognition (ASR) systems degrades in recognition of children's speech. To solve this problem, this study addresses representation of relevant and discriminative features for children's speech recognition. The addressed methods include extraction of MFCC with narrower filter bank followed by a fuzzy-based feature selection method. The proposed feature selection provides relevant, discriminative, and complementary features. For this purpose, conflicting objective functions for measuring the goodness of the features have to be fulfilled. To this end, fuzzy formulation of the problem and fuzzy aggregation of the objectives are used to address uncertainties involved with the problem.The proposed method can diminish the dimensionality without compromising the speech recognition rate. To assess the capability of the proposed method, the study analyzed six Malay vowels from the recording of 360 children, ages 7 to 12. Upon extracting the features, two well-known classification methods, namely, MLP and HMM, were employed for the speech recognition task. Optimal parameter adjustment was performed for each classifier to adapt them for the experiments. The experiments were conducted based on a speaker-independent manner. The proposed method performed better than the conventional MFCC and a number of conventional feature selection methods in the children speech recognition task. The fuzzy-based feature selection allowed the flexible selection of the MFCCs with the best discriminative ability to enhance the difference between the vowel classes.     

Acquiring,storing and utilizing process planning knowledge using neural networks

An approach is formulated for the automated acquisition of process selection and within-feature process sequencing knowledge from examples using neural networks. Network architecture, problem representation and performance issues are discussed.     

基于Gabor特征与加权协同表示的人脸识别算法

针对基于稀疏表示分类方法的训练样本于与类别标签信息提取不足,特别是在训练样本和待测样本都受到噪声污染的情况下将会明显下降及算法复杂度较高的问题,提出以Gabor特征以及加权协同为基础的人脸识别算法;最初需要对人脸图像内所包含的各个尺度以及方向的Gabor特征完成提取,在稀疏表示中引入Gabor特征,将降维后的Gabor特征矩阵作为超完备字典,再用稀疏表示增强加权协同表示得到该字典下的的稀疏表示系数,然后利用增强系数与训练样本的标签矩阵完成对测试样本进行分类识别,从而得到Gabor特征以及加权的协同表示分类方法,在Yale人脸数据库、Extended Yale B和AR人脸数据库上以及在FERET人脸数据库对人脸姿态变化的实验表明新算法具有更好的识别率和较短的计算时间.     

DPP: An agent-based approach for distributed process planning

A changing shop floor environment characterized by larger variety of products in smaller batch sizes requires creating an intelligent and dynamic process planning system that is responsive and adaptive to the rapid adjustment of production capacity and functionality. In response to the requirement, this research proposes a new methodology of distributed process planning (DPP). The primary focus of this paper is on the architecture of the new process planning approach, using multi-agent negotiation and cooperation. The secondary focus is on the other supporting technologies such as machining feature-based planning and function block-based control. Different from traditional methods, the proposed approach uses two-level decision-making—supervisory planning and operation planning. The former focuses on product data analysis, machine selection, and machining sequence planning, while the latter considers the detailed working steps of the machining operations inside of each process plan and is accomplished by intelligent NC controllers. By the nature of decentralization, the DPP shows promise of improving system performance within the continually changing shop floor environment.     

多模态时空特征表示及其在行为识别中的应用

目的 在人体行为识别研究中,利用多模态方法将深度数据与骨骼数据相融合,可有效提高动作的识别率。针对深度图像信息数据量大、冗余度高等问题,提出一种通过获取关键时程信息动作帧序列降低冗余的算法,即质心运动路径松弛算法,并根据不同模态数据的特点,提出一种新的时空特征表示方法。方法 质心运动路径松弛算法根据质心在相邻帧之间的运动距离,计算图像差分后获得的活跃部分的相似系数,然后剔除掉相似度高的帧,获得足以表达行为的关键时程信息。根据图像动态部分的变化特性、人体各部分在运动中的协同性和局部显著性特征构建一种新的时空特征表示方法。结果 在MSR-Action3D数据集上对本文方法的效果进行验证。在3个子集中进行交叉验证的平均分类识别率为95.743 2%,分别比Multi-fused,CovP3DJ,D3D-LSTM(densely connected 3DCNN and long short-term memory),Joint Subset Selection方法高2.443 2%,4.763 2%,0.343 2%,0.213 2%。本文方法在使用完整数据集的扩展实验中进行交叉验证的分类识别...     

基于稀疏表示与小波特征的人脸识别分层框架

基于稀疏表示的人脸识别研究,非线性特征的选择研究较少。提出分层使用人脸图像的小波特征,进行稀疏表示人脸识别框架。框架首先对样本人脸进行小波变换,构造小波低频和小波高频过完备人脸字典;识别阶段首先使用人脸图像的小波低频特征进行稀疏表示,计算类别模糊稀疏,然后根据模糊系数输出类别标签或进行高频特征的稀疏表示与识别。实验结果表明,基于小波特征和稀疏表示的人脸识别分层框架提高了识别的准确率,且对遮挡很鲁棒。     

Recognition of machining features for cast then machined parts

Mechanical parts are typically manufactured using multiple manufacturing processes. Primary processes such as casting realize the primary shape of the part, while secondary processes such as machining generate more detailed shape of the part. This paper presents a feature recognition method to support machining process planning for cast-then-machined parts. From the part model including the specification of machined faces, we generate the starting workpiece for machining, which represents the casting output in sufficient detail to support machining process planning. The starting workpiece is generated by identifying faces to be made by casting followed by machining, then offsetting the part through these faces by a uniform machining thickness to obtain cast faces, and combining the halfspaces induced by machined faces and the halfspaces induced by their bounding cast faces to enclose removal volumes. Machining features are then recognized from the removal volumes using a volume decomposition method called Alternating Sum of Volumes with Partitioning.