Extraction of generative processes from B-Rep shapes and application to idealization transformations

International audienceA construction tree is a set of shape generation processes commonly produced with CAD modelers during a design process of B-Rep objects. However, a construction tree does not bring all the desired properties in many configurations: dimension modifications, idealization processes, etc. Generating a non trivial set of generative processes, possibly forming a construction graph, can significantly improve the adequacy of some of these generative processes to meet user's application needs. This paper proposes to extract generative processes from a given B-rep shape as a high-level shape description. To evaluate the usefulness of this description, finite element analyses (FEA) and particularly idealizations are the applications selected to evaluate the adequacy of additive generative processes. Non trivial construction trees containing generic extrusion and revolution primitives behave like well established CSG trees. Advantageously, the proposed approach is primitive-based, which ensures that any generative process of the construction graph does preserve the realizability of the corresponding volume. In the context of FEA, connections between idealized primitives of a construction graph can be efficiently performed using their interfaces. Consequently, generative processes of a construction graph become a high-level object structure that can be tailored to idealizations of primitives and robust connections between them

Automated Volumetric Feature Extraction from the Machining Perspective

Master'sMASTER OF ENGINEERIN

Functional requirements to shape generation in CAD

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, June 2003.Includes bibliographical references (p. 119-121).An outstanding issue in computer-aided design (CAD) is the creation of geometric shapes from the description of functional requirements (FRs). This thesis presents a method that can generate assembled shapes from the given FRs without human intervention. To achieve this goal, the design process follows a V-model of decomposition and integration based on axiomatic design. The V-model consists of three main sub-processes; (1) a top-down decomposition of FRs and design parameters (DPs), (2) mapping of DPs into geometric entities, and (3) a bottom-up integration of the geometric entities. A shape decomposition technique is used in the V-model to generate solid cells from the geometric entities in the CAD models based on FRs. These cells are stored and reused during the integration process. A set of cells mapped to an FR is called a functional geometric feature (FGF) to differentiate it from geometric features defined by only geometric characteristics. Each FGF has mating faces as its pre-defined interfaces. Links of FR-DP-FGF-INTERFACES and their hierarchies are made and stored in the database as fundamental units for automatic assembled shape generation. The retrieval of proper FGF from the database is performed by matching a query FR with stored FRs by a lexical search based on the frequency of words and the sequence of the words in the FR statements using a synonym checking system. The language-matching rate is calculated as a value of FRmetric between 0 and 1. A computer algorithm automatically combines and assembles the retrieved FGFs. Genetic algorithm (GA) searches for the best combination for matching interface types and generates assembly sequences.(cont.) From the highest-valued chromosome, the computer algorithm automatically assembles FGFs by coordinating, orienting, and positioning with reference to the given mating conditions and calculates geometric interface-ability to a value of INTERFACEmetric between 0 and 1. The higher the values of FRmetric and INTERFACEmetric, the better the generated design solution for the given FRs that must be satisfied. The process of top-down decomposition and bottom-up integration reduces the number of possible combinations of interfacing FGFs. Design matrix visually relates FRs to FGFs. The method presented in this thesis has demonstrated that a "functional CAD" can aid designers in generating conceptual design solutions from functional descriptions, in reusing existing CAD models, and in creating new designs.by Jinpyung Chung.Ph.D

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

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

Process planning methodology and evaluation of tool life for micromilling with an application to the fabrication of thin wall structure

Ph. D. Thesis.The scaling down effect on feature geometries and tools used in micromilling results in low feature stiffness and excessive tool wear. To achieve the required costs and tolerances, optimisation of the machining processes and their associated parameters are necessary which requires a thorough understanding of machining characteristics. Furthermore, the compensation must be sought for downscaling issues that arise at the process planning stage. Hence, the effect of the characteristics of the cutting tool, workpiece material and machining parameters are investigated in this research through a critical review of the literature followed by a numerical and experimental study of the impact of process variables. The research findings are used in the development of a process planning methodology for micromilling of components with application to high aspect ratio structures, to assist machine operators and to fill the gap between industrial and academic machining knowledge. From the investigation of machining sequences, the study of machining layer strategy considering the sequence of removal of excess material using numerical simulation, strategic planning of machining layers in relation to feature stiffness is required, in particular to the machining of high aspect ratio features. The results from numerical simulation recommend an improved layer strategy for micromilling of thin wall structures, which were then experimentally validated in relation to machining time and geometrical and surface accuracy. The importance of planning tool entry and exit position in relation to feature rigidity was highlighted. The increase in depth of cut shows to improve the tool engagement reducing the thin wall deflection by 168 μm and appearance of the burr along the wall edge indicated by up to 200% drop in burr width. The investigation of tool paths showed the suitability of strategies for machining of circular and linear geometries. Also, the experimental findings emphasise on considering the feature geometry type in the selection of tool paths to achieve a balance between high-performance machining and improved productivity. This study also investigates tool life, associated with flank wear rate, surface roughness, volumetric tool loss and the degradation of the cutting edge radius for micro endmills where a direct correlation between cutting speed and tool wear rate has been found. The new procedure for tool life prediction in conjunction with clear tool rejection criteria for the micro end mill is recommended. Along with standard procedure for the evaluation of tool change intervals to avoid tool failure and consequential defects in parts produced. In addition to the findings in the literature on machine process planning and findings from the study of machining sequence on the thin wall structure and tool life investigation conducted, a new process planning methodology for micromilling has been proposed. The process planning methodology includes four distinct modules i.e. feature recognition, tool selection, machining parameter selection and machining sequence planning. The feature recognition module proposes a new approach to identify key feature faces and their corresponding machining attributes required for tasks in process planning. In the tool selection module, a new methodology for the evaluation of the machinability index and the tool replacement strategy for micro endmills are proposed to guide the operator in the task of tool selection and estimating tool replacement intervals. The machining parameter module provides a systematic approach for the selection spindle speed, feedrate and depth of cut. The machine sequence planning module assists the operator in selecting a suitable tool path and tool layer strategy along with a compensate technique for tool path errors. An artefact with thin wall features has been fabricated using the methodology proposed and the conventional process planning method. The results show the part processed using the proposed methodology achieved better geometrical tolerance, and improved repeatability. It also show a 17% improvement in mean surface roughness, which demonstrates the effectiveness of the proposed methodology

Automated feature recognition system for supporting engineering activities downstream of conceptual design.

Transfer of information between CAD models and downstream manufacturing process planning software typically involves redundant user interaction. Many existing tools are process-centric and unsuited for selection of a "best process" in the context of existing concurrent engineering design tools. A computer based Feature-Recognition (FR) process is developed to extract critical manufacturing features from engineering product CAD models. FR technology is used for automating the extraction of data from CAD product models and uses wire-frame geometry extracted from an IGES neutral file format. Existing hint-based feature recognition techniques have been extended to encompass a broader range of manufacturing domains than typical in the literature, by utilizing a combination of algorithms, each successful at a limited range of features. Use of wire-frame models simplifies product geometry and has the potential to support rapid manufacturing shape evaluation at the conceptual design stage. Native CAD files are converted to IGES neutral files to provide geometry data marshalling to remove variations in user modelling practice, and to provide a consistent starting point for FR operations. Wire-frame models are investigated to reduce computer resources compared to surface and solid models, and provide a means to recover intellectual property in terms of manufacturing design intent from legacy and contemporary product models. Geometric ambiguity in regard to what is ?solid? and what is not has plagued wire-frame FR development in the past. A new application of crossing number theory (CNT) has been developed to solve the wire-frame ambiguity problem for a range of test parts. The CNT approach works satisfactorily for products where all faces of the product can be recovered and is tested using a variety of mechanical engineering parts. Platform independent tools like Extensible Mark-up Language are used to capture data from the FR application and provide a means to separate FR and decision support applications. Separate applications are composed of reusable software modules that may be combined as required. Combining rule-based and case-based reasoning provides decision support to the manufacturing application as a means of rejecting unsuitable processes on functional and economic grounds while retaining verifiable decision pathways to satisfy industry regulators

Análisis y procesado tecnológico del modelo sólido de una pieza para determinar sus elementos característicos de mecanizado

Una de las primeras etapas en la Planificación de Procesos asistida por ordenador, para procesos de mecanizado por arranque de material, consiste en identificar las zonas de material a eliminar en el bruto de partida para generar la pieza. El resultado es un conjunto de entidades llamadas: Elementos Característicos de Mecanizado, que tienen una clara relación con las operaciones de mecanizado. Al procedimiento de obtención automática de estas entidades se le denomina: reconocimiento automático de Elementos Característicos de Mecanizado (AFR, Automatic Feature Recognition), en el que partiendo del modelo 3D del bruto y de la pieza se establecen las entidades de trabajo adecuadas (Elementos Característicos de Mecanizado). Estas entidades contienen la información necesaria para poder llevar a cabo una Planificación de Procesos automática. A su vez, la información se va completando y ampliando a medida que se avanza en las etapas de la Planificación. En la Tesis se plantea el reconocimiento automático de Elementos Característicos de Mecanizado como una de las primeras etapas de la Planificación de Procesos, y que permite el enlace con el diseño asistido por ordenador. Este reconocimiento debe tener un planteamiento dinámico, ofreciendo distintas opciones. Su solución no debe ser una entrada estática, prefijada, para el resto de etapas de la Planificación. El proceso de reconocimiento está fuertemente influenciado por conceptos y decisiones de índole tecnológico (tipos de herramientas, movimientos característicos de los procesos, influencia del corte vinculado, ), que lo guían y que permiten obtener resultados válidos en la aplicación destino: el mecanizado. Atendiendo a este planteamiento, la Tesis ofrece una solución general y completa al proceso de reconocimiento automático de Elementos Característicos de Mecanizado, teniendo en cuenta a los llamados procesos convencionales (torneado, fresado, limado, rectificado, etc.). La solución propuesta no se restringe a piezasGutiérrez Rubert, SC. (2007). Análisis y procesado tecnológico del modelo sólido de una pieza para determinar sus elementos característicos de mecanizado [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1963Palanci