Development of Feature Recognition Algorithm for Automated Identification of Duplicate Geometries in CAD Models

This research presents a feature recognition algorithm for the automated identification of duplicate geometries in the CAD assembly. The duplicate geometry is one of the seven indicators of the lazy parts mass reduction method. The lazy parts method is a light weight engineering method that is used for analyzing parts with the mass reduction potential. The duplicate geometry is defined as any geometries lying equal to or within the threshold distance with the user-defined orientation between them and have the percentage similarity that is equal to or greater than the threshold value. The feature recognition system developed in this research for the identification of duplicate geometries is also extended to retrieve the weighted bipartite graph of part connections for the assembly time estimation. The weighted bipartite graph is used as input for the part connectivity based assembly time estimation method. The SolidWorks API software development kit is used in this research to develop a feature recognition system in SolidWorks CAD software package using C++ programming language. The feature recognition system built in the SolidWorks CAD software uses a combination of topology and geometric data for the evaluation of duplicate geometry. The measurement of distances between the sampling points strategy is used for the duplicate geometry feature recognition. The feature recognition algorithm has three phases of evaluation: first, is the evaluation for threshold distance condition of parts in the CAD assembly. Second, the part pairs that have satisfied the threshold distance condition are evaluated for the orientation condition. The threshold distance and orientation are the necessary but not the sufficient conditions for duplicate geometries. In the third phase, the geometries that have satisfied orientation condition are evaluated for the percentage similarity condition. The geometries that satisfy the percentage similarity condition are highlighted in order to help designers review the results of the duplicate geometry analysis. The test cases are used to validate the algorithm against the requirements list. The test cases are designed to check the performance of the algorithm for the evaluation of the threshold distance, orientation, and percentage similarity condition. The results indicate that the duplicate geometry algorithm is able to successfully conduct all the three phases of evaluation. The algorithm is independent of the geometric type and is able to analyze planar, cylindrical, conical, spherical, freeform, and toroidal shapes. The number of sampling points generated on the faces of parts for the orientation and percentage similarity evaluation has the significant effect on the analysis time. The worst case complexity of the algorithm is the big O (nC2x m12 x m22x p4), where n = the number of parts in the assembly m1 = the number of faces in the parts that meet the threshold distance condition m2 = the number of faces that meet the orientation condition p = the number of sampling points on the face The duplicate geometry feature recognition approach is used to demonstrate the applicability in the extraction of assembly relations for the part connectivity based assembly time estimation method. The algorithm is also able to extract part connectivity information for the patterns. Further research is required to automate the identification of other laziness indicators in order to make the lazy parts method a completely automated tool. With regards to the complete automation of part connectivity based assembly time estimation method, the duplicate geometry feature recognition system needs integration with the algorithm for the computation of bipartite graph of part connections for the prediction of assembly time

Transform-based surface analysis and representation for CAD models

In most Computer-Aided Design (CAD) systems, the topological and geometrical information in a CAD model is usually represented by the edge-based data structure. With the emergence of concurrent engineering, such issues as product design, manufacturing, and process planning are considered simultaneously at the design stage. The need for the development of high-level models for completely documenting the geometry of a product and supporting manufacturing applications, such as automating the verification of a design for manufacturing (DIM) rules and generating process plans, becomes apparent;This dissertation has addressed the development of a generalized framework for high-level geometric representations of CAD models and form features to automate algorithmic search and retrieval of manufacturing information;A new wavelet-based ranking algorithm is developed to generate surface-based representations as input for the extraction of form features with non-planar surfaces in CAD models. The objective of using a wavelet-based shape analysis approach is to overcome the main limitation of the alternative feature extraction approaches, namely their restriction to planar surfaces or simple curved surfaces;A transform-invariant coding system for CAD models by multi-scale wavelet representations is also presented. The coding procedure is based on both the internal regions and external contours of topology entities---faces

Feature based recognition of incomplete CAD models for providing design assistance

Most current CAD systems have the tools to allow users to generate models using Boolean combinations of features. While current research has explored several directions for next generation CAD systems with a wider range of applications, there has been little work in providing assistance to the user for generating the models. The present research aims at using 3D object recognition techniques to recognize incomplete CAD models and thereby determine the user\u27s intent to facilitate model development. A system has been developed to recognize complete and incomplete models belonging to a particular category for which the system stores a construction tree that describes the sequence in which features must be added in order to generate a model of the category. The construction tree of a model is analogous to the sequence of operations that would have to be performed to manufacture the part. The input CAD model is checked against the construction tree of the object in question using certain rules to obtain a confidence level representing the similarity of the input model to the object. The rules used by the system are classified as Shape Rules, Dimension Rules, Similarity Rules and Placement and Orientation Rules. The system recognizes models belonging to the category Gear, with sub-categories as Spur Gear (internal & external), Rack Gear and Straight Bevel Gear. Test cases are provided to display the system\u27s competence and capability

Modeling of an automatic CAD-based feature recognition and retrieval system for group technology application

In recent time, many researches have come up with new different approaches and means for Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) integration. Computer-Aided Process Planning (CAPP) is considered to be a bridge that connects these both technologies. CAPP may involve such an important technique as automatic feature extraction - a procedure that is engaged in process plans generation to be used in producing a designed part. Also in terms of CAD, the feature extraction procedure facilitates a cooperative design and process planning within the entire product development process. The main objective of the thesis is to present a new automatic feature extraction and classification system that is able to process mechanical rotational and non-rotational parts from the Opitz Code System point of view. The implemented system takes Standard for Exchange of Product data (STEP) - a neutral product representation format as input and extracts features of parts required for further manufacturing. The STEP format is used to provide geometrical and topological information about machining parts. A methodology to extract shape features was developed based on these geometrical and topological data. As output, the proposed system codes the extracted part features to Opitz Code System. CAD product files were taken from official manufacturers of mechanical parts in order to evaluate the developed system

Manufacturing Feature Recognition With 2D Convolutional Neural Networks

Feature recognition is a critical sub-discipline of CAD/CAM that focuses on the design and implementation of algorithms for automated identification of manufacturing features. The development of feature recognition methods has been active for more than two decades for academic research. However, in this domain, there are still many drawbacks that hinder its practical applications, such as lack of robustness, inability to learn, limited domain of features, and computational complexity. The most critical one is the difficulty of recognizing interacting features, which arises from the fact that feature interactions change the boundaries that are indispensable for characterizing a feature. This research presents a feature recognition method based on 2D convolutional neural networks (CNNs). First, a novel feature representation scheme based on heat kernel signature is developed. Heat Kernel Signature (HKS) is a concise and efficient pointwise shape descriptor. It can present both the topology and geometry characteristics of a 3D model. Besides informative and unambiguity, it also has advantages like robustness of topology and geometry variations, translation, rotation and scale invariance. To be inputted into CNNs, CAD models are discretized by tessellation. Then, its heat persistence map is transformed into 2D histograms by the percentage similarity clustering and node embedding techniques. A large dataset of CAD models is built by randomly sampling for training the CNN models and validating the idea. The dataset includes ten different types of isolated v features and fifteen pairs of interacting features. The results of recognizing isolated features have shown that our method has better performance than any existing ANN based approaches. Our feature recognition framework offers the advantages of learning and generalization. It is independent of feature selection and could be extended to various features without any need to redesign the algorithm. The results of recognizing interacting features indicate that the HKS feature representation scheme is effective in handling the boundary loss caused by feature interactions. The state-of-the-art performance of interacting features recognition has been improved

Intelligent techniques for automatic feature recognition in CAD models

The solutions suggested in this research are implemented in a prototype AFR system and its performance verified on commonly used benchmarking parts that are composed of machining features.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Simulation-Based and Data-Driven Approaches to Industrial Digital Twinning Towards Autonomous Smart Manufacturing Systems

A manufacturing paradigm shift from conventional control pyramids to decentralized, service-oriented, and cyber-physical systems (CPSs) is taking place in today’s Industry 4.0 revolution. Generally accepted roles and implementation recipes of cyber systems are expected to be standardized in the future of manufacturing industry. Developing affordable and customizable cyber-physical production system (CPPS) and digital twin implementations infuses new vitality for current Industry 4.0 and Smart Manufacturing initiatives. Specially, Smart Manufacturing systems are currently looking for methods to connect factories to control processes in a more dynamic and open environment by filling the gaps between virtual and physical systems. The work presented in this dissertation first utilizes industrial digital transformation methods for the automation of robotic manufacturing systems, constructing a simulation-based surrogate system as a digital twin to visually represent manufacturing cells, accurately simulate robot behaviors, promptly predict system faults and adaptively control manipulated variables. Then, a CPS-enabled control architecture is presented that accommodates: intelligent information systems involving domain knowledge, empirical model, and simulation; fast and secured industrial communication networks; cognitive automation by rapid signal analytics and machine learning (ML) based feature extraction; and interoperability between machine and human. A successful semantic integration of process indicators is fundamental to future control autonomy. Hence, a product-centered signature mapping approach to automated digital twinning is further presented featuring a hybrid implementation of smart sensing, signature-based 3D shape feature extractor, and knowledge taxonomy. Furthermore, capabilities of members in the family of Deep Reinforcement Learning (DRL) are explored within the context of manufacturing operational control intelligence. Preliminary training results are presented in this work as a trial to incorporate DRL-based Artificial Intelligence (AI) to industrial control processes. The results of this dissertation demonstrate a digital thread of autonomous Smart Manufacturing lifecycle that enables complex signal processing, semantic integration, automatic derivation of manufacturing strategies, intelligent scheduling of operations and virtual verification at a system level. The successful integration of currently available industrial platforms not only provides facile environments for process verification and optimization, but also facilitates derived strategies to be readily deployable to physical shop floor. The dissertation finishes with summary, conclusions, and suggestions for further work

Intelligent techniques for automatic feature recognition in CAD models

The solutions suggested in this research are implemented in a prototype AFR system and its performance verified on commonly used benchmarking parts that are composed of machining feature

Modelamento de dados para o desenvolvimento e representação de peças: estudo de casos /

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico.No desenvolvimento do processo de projeto de produto, os dados e informações do projeto de peças podem ser estruturados e sistematizados com auxílio de uma ferramenta computacional. Os elementos funcionais e conceituais que integram a alternativa de solução do produto, alimentam ageração de alternativas de solução na concepção de cada uma das peças que o compõe