Operating invalid feature-based models

A valid feature-based representation is one where instantiated features in a model agree with the features' expected behaviours, available and defined as a library. Invalid feature-based models happen when manipulations on the model change the interrelationship among features therefore changing the behaviour of an instantiated feature. Freedom of manipulation is an intrinsic advantage of using a CAD system and it is taken for granted. However, even the most basic manipulation, such as "adding" a feature to a model, is capable of disrupting the validity of a representation. Furthermore, invalid models could compromise the usefulness of any following analysis on it. Thus, identifying means to operate on an invalid model to make it valid, through "revalidation operations", is a necessity in Feature-based CAD systems. It allows conventional CAD systems (usually more preoccupied with representing and producing feature-like shapes within a geometrically constrained environment) to interface more easily for example with CAPP systems (usually more preoccupied with planning problems than with the correctness of the representation). The framework of a feature-based validation system, called FRIEND (Feature-based Reasoning system for Intent-driven Engineering Design), and a discussion on representation validity analysis is presented with emphasis on identifying and discussing "revalidation operations”

A Divide-and-Conquer Algorithm for Machining Feature Recognition over Network

In this paper, a divide-and-conquer algorithm for machining feature recognition over network is presented. The algorithm consists of three steps. First, decompose the part and its stock into a number of sub-objects in the client and transfer the sub-objects to the server one by one. Meanwhile, perform machining feature recognition on each sub-object using the MCSG based approach in the server in parallel. Finally, generate the machining feature model of the part by synthesizing all the machining features including decomposed features recognized from all the sub-objects and send it back to the client. With divide-and-conquer and parallel computing, the algorithm is able to decrease the delay of transferring a complex CAD model over network and improve the capability of handling complex parts. Implementation details are included and some test results are given

Implementation of hierarchical design for manufacture rules in manufacturing processes

In order to shorten the product development cycle time, minimise overall cost and smooth transition into production, early consideration of manufacturing processes is important. Design for Manufacture (DFM) is the practice of designing products with manufacturing issues using an intelligent system, which translates 3D solid models into manufacturable features. Many existing and potential applications, particularly in the field of manufacturing, require various aspects of features technology. In all engineering fields geometric modelling wluch accurately represents the shape of a whole engineering component has become accepted for a wide range of applications. To apply DFM rules or guidelines in manufacturing processes, they have to be systematised and organised into a hierarchical rule system. Rules at the higher level of the hierarchical system are applied to more generic manufacturing features, and specific rules are applied to more detailed features. This enables the number of rules and amount of repetition to be minimsed. Violation of the design for manufacture rules in the features, their characteristics and manufacturing capabilities are further examined in this hierarchical system. Manufacturabillty analysis, such as production type, materials, tolerances, surface finish, feature characteristics and accessibility, are also taken into consideration. Consideration of process capabilities and limitations during the design process is necessary in order to minimise production time and as a result, rnanufactunng cost. The correct selection of manufacturing processes is also important as it is related to the overal cost. As a result of this research, a hierarchical design for manufacture rule system is proposed which would aid designers in avoiding designs that would lead to costly manufacturing processes

CAD/CAM integration based on machining features for prismatic parts

The development of CAD and CAM technology has significantly increased efficiency in each individual area. The independent development, however, greatly restrained the improvement of overall efficiency from design to manufacturing. The simple integration between CAD and CAM systems has been achieved. Current integrated CAD/CAM systems can share the same geometry model of a product in a neutral or proprietary format. However, the process plan information of the product from CAPP systems cannot serve as a starting point for CAM systems to generate tool paths and NC programs. The user still needs to manually create the machining operations and define geometry, cutting tool, and various parameters for each operation. Features play an important role in the recent research on CAD/CAM integration. This thesis investigated the integration of CAD/CAM systems based on machining features. The focus of the research is to connect CAPP systems and CAM systems by machining features, to reduce the unnecessary user interface and to automate the process of tool path preparation. Machining features are utilized to define machining geometries and eliminate the necessity of user interventions in UG. A prototype is developed to demonstrate the CAD/CAM integration based on machining features for prismatic parts. The prototype integration layer is implemented in conjunction with an existing CAPP system, FBMach, and a commercial CAD/CAM system, Unigraphics. Not only geometry information of the product but also the process plan information and machining feature information are directly available to the CAM system and tool paths can be automatically generated from solid models and process plans

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

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

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

Feature-based validation reasoning for intent-driven engineering design

Feature based modelling represents the future of CAD systems. However, operations such as modelling and editing can corrupt the validity of a feature-based model representation. Feature interactions are a consequence of feature operations and the existence of a number of features in the same model. Feature interaction affects not only the solid representation of the part, but also the functional intentions embedded within features. A technique is thus required to assess the integrity of a feature-based model from various perspectives, including the functional intentional one, and this technique must take into account the problems brought about by feature interactions and operations. The understanding, reasoning and resolution of invalid feature-based models requires an understanding of the feature interaction phenomena, as well as the characterisation of these functional intentions. A system capable of such assessment is called a feature-based representation validation system. This research studies feature interaction phenomena and feature-based designer's intents as a medium to achieve a feature-based representation validation system. [Continues.

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

A Geometric Approach to Converting CAD Models to CAM Models: an Application on Aeronautical Structure Parts

"RÉSUMÉ:" La conversion d'un modèle de CAO en un modèle de FAO est la première étape de fabrication intégrée par ordinateur. Les principaux problèmes qui concernent la conversion sont les suivants: définir des volumes de matériau amovible géométriquement, vérifier les accessibilités aux volumes ainsi obtenus, associer les opérations d'usinage avec ces volumes individuellement, sélectionner les outils de coupe, mettre en séquençage les opérations d'usinage et assigner une machine pour exécuter le processus. La détermination des volumes individuels de matériel amovible est le premier problème de la conversion. Dans les dernières décennies, de nombreuses approches ont été développées avec d'énormes efforts, mais aucune étude à ce jour a examiné de manière exhaustive les approches pour générer des volumes de matériau amovible pour traiter des pièces complexes, telles que celles qu’on rencontre dans en aéronautique dans la partie structurelle. Dans la perspective de définir les volumes du matériau amovible, les méthodes existantes se limitent aux fonctions prismatiques. L'objectif principal de cette recherche était de développer des approches systématiques, pour générer automatiquement l'ensemble des volumes de matières amovibles selon les modèles 3D d’une pièce aéronautique structurelle. Il faut alors partir du brut (un morceau de matière première) et usiner toutes les surfaces requises. Grâce à l'outil mathématique disponible des opérations booléennes, il est possible de séparer des géométries volumiques très complexes en volumes plus petits relativement simples. La décomposition du volume delta présente des avantages dans la création des volumes amovibles. Dans cette recherche, les approches de décomposition de volume ont été développées dans le but que chaque volume de matériau puisse être usiné en une seule opération d'usinage. Des arêtes concaves impliquent éventuellement des opérations d'usinage différentes. La détection du bord concave est la première étape de la décomposition de volume intérieur. Dans cette étude, une approche mathématique a été développée afin de vérifier la concavité d'une arête dans la limite d'un modèle solide 3D et une approche de détection des bords concaves est proposée. Générer des faces de séparation est une étape clé pour définir un volume décomposé. Selon la complexité de l'élément de construction, les algorithmes sont conçus pour créer différents types de décompositions de faces correspondant à des formes locales de la pièce à décomposer.----------"ABSTRACT:" Conversion of a CAD model to a CAM model is the initial step of computer integrated manufacturing. Main issues concerning the conversion are as follows: defining volumes of removable material geometrically, verifying accessibilities to so obtained volumes, associating machining operations with these volumes individually, selecting cutting tools, sequencing machining operations, and assign a machine to perform the process. Determination of individual volumes of removable material is the first issue of the conversion. In the past decades many approaches have been developed by enormous efforts but no study up to date has comprehensively discussed approaches to generate volumes of removable material for producing a complex aeronautical structural part. In the perspective of volumetric definition of removable material, existing methods are limited to prismatic features. The main objective of this research was to develop systematic approaches to generating automatically the complete set of volumes of removable material according to the 3D models of both an aeronautical structural part to be produced and the stock (a piece of raw material) to be machined. Due to powerful mathematical tool of Boolean operations available for separating very complex volumetric geometries into relatively simple smaller volumes, delta volume decomposition has advantages in generating removable volumes. In this research volume decomposition approaches were developed for the purpose that every volume of material can be machined in one machining operation. Concave edges imply possible requirement of different machining operations. Detecting concave edge is the premier step of interior volume decomposition. In this study a mathematical approach was developed to verify the concavity of an edge in the boundary of a 3D solid model. Approaches to detecting concave edges were proposed. Generating splitting faces is the key step to define a decomposed volume. According to the complexity of the structural component, algorithms are developed to create different kinds of splitting faces corresponding to local shapes of the part to perform decompositions. Face union is a powerful tool to separate volumes bounded by faces of complex geometries. This research proposed recursive procedures of decomposition. Using the proposed approaches the 3D design model of an aeronautic structural component is converted into volumes of removable material (named sub delta volume and denoted SDV in this research) by means of delta volume decomposition