scholarly journals A framework for similarity recognition of CAD models

2016 ◽  
Vol 3 (3) ◽  
pp. 274-285 ◽  
Author(s):  
Leila Zehtaban ◽  
Omar Elazhary ◽  
Dieter Roller
Keyword(s):  
Product Development ◽  
Code Generation ◽  
Feature Recognition ◽  
Recognition Algorithm ◽  
Product Lifecycle Management ◽  
Design Parameters ◽  
Design Decisions ◽  
Knowledge Based ◽  
Two Factors ◽  
Cad Models

Abstract A designer is mainly supported by two essential factors in design decisions. These two factors are intelligence and experience aiding the designer by predicting the interconnection between the required design parameters. Through classification of product data and similarity recognition between new and existing designs, it is partially possible to replace the required experience for an inexperienced designer. Given this context, the current paper addresses a framework for recognition and flexible retrieval of similar models in product design. The idea is to establish an infrastructure for transferring design as well as the required PLM (Product Lifecycle Management) know-how to the design phase of product development in order to reduce the design time. Furthermore, such a method can be applied as a brainstorming method for a new and creative product development as well. The proposed framework has been tested and benchmarked while showing promising results. Highlights Developing a knowledge-based framework to assist the designer in design decisions. Opitz feature recognition and code generation from STEP for data standardization. An efficient similarity recognition algorithm to retrieve models from database.

scholarly journals Early Robust Design—Its Effect on Parameter and Tolerance Optimization

2021 ◽  
Vol 11 (20) ◽  
pp. 9407
Author(s):  
Stefan Goetz ◽  
Martin Roth ◽  
Benjamin Schleich
Keyword(s):  
Product Development ◽  
Robust Design ◽  
Product Development Process ◽  
Design Parameters ◽  
Concept Design ◽  
Design Decisions ◽  
Dynamic Markets ◽  
Tolerance Optimization ◽  
Qualitative Approaches ◽  
Design Activities

The development of complex products with high quality in dynamic markets requires appropriate robust design and tolerancing workflows supporting the entire product development process. Despite the large number of methods and tools available for designers and tolerance engineers, there are hardly any consistent approaches that are applicable throughout all development stages. This is mainly due to the break between the primarily qualitative approaches for the concept stage and the quantitative parameter and tolerance design activities in subsequent stages. Motivated by this, this paper bridges the gap between these two different views by contrasting the used terminology and methods. Moreover, it studies the effects of early robust design decisions with a focus on Suh’s Axiomatic Design axioms on later parameter and tolerance optimization. Since most robust design activities in concept design can be ascribed to these axioms, this allows reliable statements about the specific benefits of early robust design decisions on the entire process considering variation in product development for the first time. The presented effects on the optimization of nominal design parameters and their tolerance values are shown by means of a case study based on ski bindings.

Feature Recognition and Parameterization Methods for Algorithm-Based Product Development Process

2017 ◽  
Author(s):  
Thiago Weber Martins ◽  
Reiner Anderl
Keyword(s):  
Product Development ◽  
Sheet Metal ◽  
Development Process ◽  
Feature Recognition ◽  
Mathematical Optimization ◽  
Product Development Process ◽  
Sheet Metal Parts ◽  
Cad Models ◽  
Feature Based ◽  
Metal Products

The algorithm-based product development process applies mathematical optimization tools in the conceptual steps of the product development process. It relies on formalized data such as initial loads and boundary conditions to find the best product solution for optimized bifurcated sheet metal parts. Previous research efforts focused on the automation of CAD modeling steps. Current algorithms are able to generate the CAD models of optimized bifurcated sheet metal products automatically, however, they are rough with low-level of detail and abstraction. Consequently, CAD models are embodied and detailed manually in a partly iterative and time-consuming process to include parameters, constraints and design features. Hence, this paper introduces feature recognition and parametrization methods for the algorithm-based product development of bifurcated sheet metal products. It proposes the inclusion of a pre-processor to analyze the solution graph resulted from topology optimization before the generation of CAD models. Algorithms that derive the geometric shape from the solution graph by recognizing features as well as assigning parameters are introduced. Then, feature-based CAD models of bifurcated sheet metal products are automatically generated. The proposed methods and algorithms are implemented with Python and validated with a use-case. Benefits and limitations of the proposed methods are discussed.

Information Driven Product Development: A Framework for Product Design and Analysis Integration

2007 ◽  
Author(s):  
Angran Xiao
Keyword(s):  
Product Development ◽  
Product Information ◽  
Information Model ◽  
Product Lifecycle Management ◽  
System Level ◽  
Design Parameters ◽  
Analysis Model ◽  
Element Analysis ◽  
Product Information Model ◽  
The Impact

New paradigms and accompanying software systems are necessary to support the integration of system level design and discipline level analysis activities for the implementation of product lifecycle management. In this paper, we present an information driven product development method for the integration in the context of multidisciplinary product realization. The method contains three constituents: product information model which represents the associativities among design requirements, product components, and design parameters; compromise Decision Support Problem which maps the information model directly into design problems; and knowledge based Finite Element Analysis which generates analysis model automatically from the information model. Information driven product development uses product information model as a communication media between design and analysis activities, hence provides an effective way to trace the impact of design changes, facilitates the reuse of analyses models, and supports collaborative decision-making. An electronic chip package design and analysis scenario is presented to illustrate and demonstrate this method.

A Knowledge Capitalization Methodology Based on Automatic Knowledge Extraction From 3D CAD Models

2012 ◽  
Author(s):  
Mathieu Lebouteiller ◽  
Jérémy Boxberger ◽  
Samuel Gomes ◽  
Nadhir Lebaal ◽  
Daniel Schlegel
Keyword(s):  
Knowledge Management ◽  
Data Management ◽  
Product Data Management ◽  
Design Parameters ◽  
3D Cad ◽  
Knowledge Based ◽  
Cad Models ◽  
Mathematical Algorithms ◽  
Knowledge Based Engineering ◽  
Lean Engineering

The issue of improving quality, costs and delays indicators in design and manufacturing is more relevant than ever in the industry. After lean manufacturing, well known in production process, the lean engineering approach is being implemented today in the field of design, taking the name of lean product development. The management of knowledge and know-how (existing, new or to be acquired) is the heart of lean engineering. In our suggested methodology this is implemented through a new generation of tools called Knowledge Configuration Management (KCM) and Knowledge Extraction Assistant (KEA). KCM tools are lean engineering components that provide analytical approach to knowledge management and knowledge-based engineering. These tools require a highly integrated approach that involves, for example, predefined geometrical parametric 3D models, such as CAD templates. But this approach cannot be deployed in all engineering sites. We propose to complete this KCM approach introducing a semantic network approach, coupling with Feature Identity Card (FIC). FIC contains a set of metadata and information existing in the Product Data Management (PDM), connected with information extracted from 3D CAD (Computer Aided Design) models. It allows contextualizing information and ensures semantic connections, in order to manipulate the right parameters with mathematical algorithms. Those algorithms will search candidate relationships between design parameters extracted from CAD models. Our suggested approach aims at extracting knowledge in cases where design never came out of Knowledge Based Engineering (KBE) applications. In those situations, it seems important to complete classical knowledge management approach, and to find out the implicit knowledge embedded in 3D CAD models. This is achieved through a global approach, focusing on the product’s 3D definitions. We suggest introducing the latter approach by a suite of digital KEA tools (interfaced with KCM tools). Extracting knowledge from projects information stored in the Product Data Management does this. More precisely, the methodology is based on a commercial 3D similarity search tools for CAD models and on mathematical algorithms that search relationships between extracted design parameters. The goal is to submit new rules to the process and design experts. Implementing this methodology, a deeper knowledge of the product and its associated process can be acquired. This ensures a more productive and efficient design process.

Engineering Objective Controls Knowledge Driven Product Definition in Industrial Product Development

2013 ◽  
Vol 300-301 ◽  
pp. 1494-1499 ◽  
Author(s):  
László Horváth ◽  
Imre J. Rudas
Keyword(s):  
Product Development ◽  
Product Lifecycle Management ◽  
New Method ◽  
Product Modeling ◽  
Product Model ◽  
Integrated Product Development ◽  
Knowledge Based ◽  
Current Product ◽  
Knowledge Content ◽  
Product Definition

High information content of integrated engineering activities stimulated development of product modeling during the past decades in order to support information management for lifecycle of products. Mechatronics is one of the engineering areas those require integrated product development techniques with strong knowledge based modeling and simulation in their background. The authors of this paper analyzed product modeling advancements in industrially applied product lifecycle management (PLM) systems in order to conceptualize new method to enhance knowledge content in product model. As a result of this analysis, they proposed a new method for control of product definition which extends the existing control in current PLM systems. This method is a contribution to solution for problems in current product modeling and is called as coordinated request based product modeling (CRPM). CRPM applies actual requested product definition (ARPD) as extension to currently applied product model. In this paper, the new method and entities as well as engineering objective definition and product behavior handling are explained as main contributions by the proposed modeling.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

scholarly journals Making design decisions under uncertainties: probabilistic reasoning and robust product design

2021 ◽  
Author(s):  
Paul Christoph Gembarski ◽  
Stefan Plappert ◽  
Roland Lachmayer
Keyword(s):  
Product Development ◽  
Product Life Cycle ◽  
Probabilistic Reasoning ◽  
Late Phase ◽  
Development Project ◽  
Product Development Process ◽  
Bayesian Decision ◽  
Rotary Valve ◽  
Design Decisions ◽  
Decision Network

AbstractMaking design decisions is characterized by a high degree of uncertainty, especially in the early phase of the product development process, when little information is known, while the decisions made have an impact on the entire product life cycle. Therefore, the goal of complexity management is to reduce uncertainty in order to minimize or avoid the need for design changes in a late phase of product development or in the use phase. With our approach we model the uncertainties with probabilistic reasoning in a Bayesian decision network explicitly, as the uncertainties are directly attached to parts of the design artifact′s model. By modeling the incomplete information expressed by unobserved variables in the Bayesian network in terms of probabilities, as well as the variation of product properties or parameters, a conclusion about the robustness of the product can be made. The application example of a rotary valve from engineering design shows that the decision network can support the engineer in decision-making under uncertainty. Furthermore, a contribution to knowledge formalization in the development project is made.

scholarly journals Practical method for the fast generation of a CAM model for jet engine parts

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110027
Author(s):  
Byung Chul Kim ◽  
Ilhwan Song ◽  
Duhwan Mun
Keyword(s):  
Computer Aided Design ◽  
Feature Recognition ◽  
Practical Method ◽  
Numerical Control ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Jet Engine ◽  
Computer Aided ◽  
Cad Models ◽  
Cam Model

Manufacturers of machine parts operate computerized numerical control (CNC) machine tools to produce parts precisely and accurately. They build computer-aided manufacturing (CAM) models using CAM software to generate code to control these machines from computer-aided design (CAD) models. However, creating a CAM model from CAD models is time-consuming, and is prone to errors because machining operations and their sequences are defined manually. To generate CAM models automatically, feature recognition methods have been studied for a long time. However, since the recognition range is limited, it is challenging to apply the feature recognition methods to parts having a complicated shape such as jet engine parts. Alternatively, this study proposes a practical method for the fast generation of a CAM model from CAD models using shape search. In the proposed method, when an operator selects one machining operation as a source machining operation, shapes having the same machining features are searched in the part, and the source machining operation is copied to the locations of the searched shapes. This is a semi-automatic method, but it can generate CAM models quickly and accurately when there are many identical shapes to be machined. In this study, we demonstrate the usefulness of the proposed method through experiments on an engine block and a jet engine compressor case.

scholarly journals The Finnish Product Development Teachers’ Perceptions of their Pedagogical Content Knowledge in Higher Education

2019 ◽  
Vol 1 (1) ◽  
pp. 609-618
Author(s):  
Elina Mäkelä ◽  
Petra Auvinen ◽  
Tero Juuti
Keyword(s):  
Higher Education ◽  
Educational Technology ◽  
Product Development ◽  
Pedagogical Content Knowledge ◽  
Content Knowledge ◽  
Curriculum Design ◽  
Teacher Interviews ◽  
Pedagogical Content ◽  
Teachers Perceptions ◽  
Knowledge Based

AbstractThe paper concerns the Finnish product development teacherś perceptions on their pedagogical content knowledge in higher education settings. The aim is to describe and analyse what kind of pedagogical content knowledge the teachers have and, therefore, to provide a better understanding of the type of knowledge unique to product development teaching. The model of pedagogical content knowledge used here includes the components of product development content knowledge, pedagogical knowledge and pedagogical content knowledge. Based on seven teacher interviews, the main content knowledge concerns the process of product development, its different phases and methods as well as the usage of different software programs. The teachers use diverse teaching methods and their attitude towards educational technology is mostly positive. Course learning outcomes and working life are acknowledged when planning teaching, but only a few teachers take curriculum into account and participate in curriculum design. Even though the teachers use different evaluation methods in teaching, new ways of evaluation are needed. This may be something that innovative educational technology tools can make possible.

Geometric Reasoning Based on Graph Grammar Parsing

1994 ◽  
Vol 116 (3) ◽  
pp. 763-769 ◽  
Author(s):  
Z. Fu ◽  
A. de Pennington
Keyword(s):  
Intelligent Design ◽  
Feature Recognition ◽  
Geometric Reasoning ◽  
Geometric Constraints ◽  
Graph Grammar ◽  
Feature Interaction ◽  
Knowledge Based ◽  
Feature Based ◽  
Feature Information ◽  
And Performance

It has been recognized that future intelligent design support environments need to reason about the geometry of products and to evaluate product functionality and performance against given constraints. A first step towards this goal is to provide a more robust information model which directly relates to design functionality or manufacturing characteristics, on which reasoning can be carried out. This has motivated research on feature-based modelling and reasoning. In this paper, an approach is presented to geometric reasoning based on graph grammar parsing. Our approach is presented to geometric reasoning based on graph grammar parsing. Our work combines methodologies from both design by features and feature recognition. A graph grammar is used to represent and manipulate features and geometric constraints. Geometric constraints are used within symbolical definitions of features constraints. Geometric constraints are used within symbolical definitions of features and also to define relative position and orientation of features. The graph grammar parsing is incorporated with knowledge-based inference to derive feature information and propagate constraints. This approach can be used for the transformation of feature information and to deal with feature interaction.

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