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Professor Emeritus

Institute of Applied Mathematics

John von Neumann Faculty of Informatics

Doctoral School of Applied Informatics and Applied Mathematics

Óbuda University

 

 

Horváth László pages

Courses

 

 

Courses in English

Click to the name of the course for information about the course,  syllabus and teaching materials.

Program

Concentration

Course

Type

Last

Next or

Information

Engineering Informatics MSc

 

N, in English

Spring 2016

Engineering Informatics and Applied Mathematics MSc

Elective

System Level Modeling for Cyber-Physical Engineering Structures in the Cloud

N, in English

New subject

Mechatronic Engineering MSc

 

N, in English

Spring 2016

Mechatronic Engineering MSc

 

N, in English

New in English

Subject was relocated to BGK

Engineering Informatics BSc

Elective

N, in English

Spring 2015

Spring 2020

Engineering Informatics  MSc

Elective

N, in English

Spring 2014

Spring 2020

Computation engineering doctoral subprogram

Subject for AIAMDI  PhD students

In English

New in English

As demanded

Computation engineering doctoral subprogram

Subject for AIAMDI PhD students

In English

New in English

As demanded

Computation engineering doctoral subprogram

Subject for AIAMDI PhD students

Contextual definition and representation of engineering objects

In English

New in English

As demanded

 

Syllabi

 

Introduction to Virtual Engineering

Program

IT for Engineers, BSc

Concentration:

Criterion course, elective

Type

Full time

Purpose and objective

One of the largest application areas of information and computer science and technology is lifecycle management of product information. Increasing percentage of information engineers find carrier in special purpose networked product lifecycle management (PLM) system environments at extended and international companies. Product information is represented in virtual spaces. Purposeful software serves highly integrated modeling of products, processes and factory environments. This course is designed to help students understand fundamental concepts, ideas, methods, techniques, and applications of virtual engineering. It is an introduction to computer model descriptions of interrelated bodies in a virtual space and applications of this shape-centered modeling at digital definition of products.

Topics of lectures

Click the title of lecture to access the .ppt file.

 

Laboratory hours include individual work of students in engineering virtual space according to the syllabus below.

 

Laboratory system: V6 PLM of the Dassault Systémes, Inc.

 

Virtual engineering

Lecture

Role of virtual systems in engineering.

The virtual technology. Representation of physical worlds.  Product model and its components. Area dependence of virtual spaces.

Laboratory

Overlook of an engineering virtual space. Functionality of  a complex engineering system. Contextual object definition. Product structure and its application at model changes.

Solid modeling

Lecture

Boundary representation. Topological structure and rules. Euler rule and its application .

Laboratory

Creating  free form and generative curves.  Creating surface and solid in the context of a curve. Connecting surfaces. Understanding concept lump.

Definition of geometry

Lecture

Characteristics and representation of parametric rational B-spline curves. representation.  Polynomials. Continuity.

Laboratory

Creating points and curves by using of typical methods. Modification of shape of curve and surface by control points. Curvature and parameter analysis.

Modification of shape by form features

Lecture

Construction of complex shapes by modification of an initial shape. Three leveled description of form features. Local and global modifications.

Laboratory

Creating volume adding,, volume subtracting, surface, and  conditioning  surfaces as modifying shapes of a solid.

Positioning and moving related shapes

Lecture

Positional relationships of solid shapes in model space  Keeping constraints at modification of related solid shapes. Creating mechanisms by adding relative movements to solid shapes.

Laboratory

Placing and modification of relationships at connection of components. Including an existing component in an assembly.

Finite element mesh and load definition

Lecture

Concept of finite element analysis. Completing part models for finite element analysis.  Finite elements and placing loads for analysis. Concept of associative, parametric, and adaptive mesh.

Laboratory

Introduction by studying typical FEA tasks.  Creating and studying finite element mesh on a spatial surface. Definition and optimization of a solid part and its meshing.

Processing point  clouds into surfaces

Lecture

Reverse engineering for the definition of surface using measured points . Creation clouds of measured points. Principles and  methods for the processing of clouds.

Laboratory

Creating curves. Editing and filtering clouds of points. Projecting curve to cloud.

Shape development techniques

Lecture

Fillets on solids. Swept surfaces.

Laboratory

Creating fillet on solid. Creating intersections. Creating variable offset surface.

Modeling of engineering practice

Lecture

Challenges and possibilities in virtual space. Expert-like capabilities. Optimizing shape.

Laboratory

Studying knowledge ware functionality. Understanding rule, check, and control. 

Visualization and animation of objects

Lecture

Basics of visualization purposed computer graphics.  Shader model,  methods of shading and rendering.   Modeling of light sources. Animation of position, shape, and light sources.

Laboratory

Functionality of shape modeling system for visualization  of surfaces. Visualizations for the assistance of model construction.

Circuit board definition

Lecture

Circuit board definition in product model. Shape modeling in the circuit board context. Constraint areas. Components.

Laboratory

Circuit board definition. 

 

Development of  contextual chains

Lecture

Contextual curves and surfaces. Solid in the context of surface. Definition and connection in contextual chains during engineering work. Analyses of curves and surfaces in order to the demanded geometry

Laboratory

Definition of complex solid through chains of contextual connections. 

 

Situation and Event Driven Models for Multilevel Abstraction Based Virtual Engineering Spaces

Program

IT for Engineers, BSc

Concentration:

Criterion course, elective

Type

Full time

Purpose and objective

The course gives an introduction to theory, methodology, and systems engineering of multidisciplinary product definition where self adaptive situation and event driven models are applied in engineering virtual spaces. This latest industrial product definition technology applies the four leveled abstraction of the RFLP (Requirement, Functional, Logical, and Physical) structure. In the course, most of the topics are explained and laboratory experienced in the V6 PLM virtual engineering system (Dassault Systémes).

Topics of lectures

Click the title of lecture to access the .ppt file.

 

Laboratory hours include individual work of students in engineering virtual space according to the syllabus below.

 

Laboratory system: V6 PLM of the Dassault Systémes, Inc.

 

Abstraction in product definition

Requirement, function, logical, and physical abstraction (RFLP) based product definition. Generic and instance product object representation.

Feature driven contextual modeling

Principle and modeling of features. Feature driven contextual product model.

Boundary representation of solids

Topoloy and geometry. Build up of topology. Non uniform rational Bspline (NURBS) curves and surfaces. Positional and DOF connection of solids.

Active knowledge driven product definition

Situation and event based driving of feature definition. Representation of rule, action, and reaction sets as expertise and experience based active knowledge in product model.

Behavior of product

Concept of behavior and its modeling in the RFLP structure as reaction of the model system to events.

Mapping physical level objects

Connection of product entities to different RFLP abstraction levels. Virtual execution of product model.

Control of shape in Boundary Representation

Contextual definition of surfaces. Generation rules and contexts in surface definition and integration. Surface based features in solids.

Functions

Connecting parts and their features in product model by functions. Product function related analysis.

Analyses

Analysis of surfaces and curves. Modeling and analysis on the finite element principle. Analysis of part placing and DOF.

 

 

Adaptive generic representations of engineering objects

Program

Computation engineering doctoral subprogram

Concentration:

Subject for AIAMDI  PhD students

Type

Doctoral subject in connection with doctoral research of PhD student

Purpose and objective

This course introduces basic knowledge and laboratory experiences for research in adaptive generic engineering models. Recent modeling capabilities of industrial engineering modeling systems for analysis and simulation in feature driven shape representation, driving model along contextual chains, active knowledge and object behavior representations, real time simulations, optimizing, experiments planning are recognized and understood. This program is part of preparing students for fundamental and engineering problem oriented research at industries involved in products with high intellectual content. Student collects material for examination in accordance with individual research plan.

Topics

 

Product model

Definition in accordance with ISO 10303. Three levels of model structure. Generic and application resources. Application protocols.

Object model

Principle of object orientation, classes and instances, and concept of taxonomy.

Boundary representation and connections of shapes

Topologically structured rational B-spline (NURBS) geometry. Connection of solids using positional and DOF definitions. Topological operators. Analysis for topological consistency.

Principle of feature driving

Driving model by feature as object. Active and passive features. Role of topology in case of driving by form features. Connection of feature parameters using mathematical formulas and functions.

Behavior of engineering objects

Concept of product object behavior. Driving model definition by behavior. Examples from mechanical and electrical behaviors.

Active knowledge representations

Situation and event based driving of model. Simple active knowledge representations: rule, action, and reaction sets.

Finite element model and its analysis

Approximation of design parameters by their calculation on nodes in contextual finite element mesh. Principle of multiphysics simulation.

Optimizing

Optimization and its types, optimized parameter, free parameters, and optimizing algorithms. 

Functions

Functional representation of engineering structures. Functional actions.

 

 

Contextual definition and representation of engineering objects

Program

Computation engineering doctoral subprogram

Concentration:

Subject for AIAMDI  PhD students

Type

Doctoral subject in connection with doctoral research of PhD student

Purpose and objective

The course is in the field of engineering which applies computer models for theoretically grounded and at the same time experience verified object modeling. The main teaching outcome is to introduce scientific level application of object-oriented representations at engineering activities which use complex self-adaptive model during the whole innovation cycle of an industrial product. Student understands definition of generic object model which uses object parameters in contextual chains. Course includes the essential principles and methods which constitute the background of contextual model object definition, generation, and operation. Practical aspect of the course is based on methodology applied at the V6 engineering modeling platform by Dassult Systémes. This platform was the principal modeling environment at the design of aircraft Airbus A350.

Topics and issues

 

Objects in engineering model

Role and place of object-oriented engineering model. Implementation of object orientation in representation of engineering objects.

Definition of contextual object model

The context concept and its interpretation in engineering model. Definition of context between relevant object parameters. Chains of contexts and their role in active adaptive generic models. Contextual consistency of model.

Boundary representation of bodies

Basic concept of topologically structured geometry at shape modeling. Eulerian topology, topological entities, and topological consistency. Local and global Euler operators. NURBS representation of geometry: base functions, segmentation, and parametrization.

Contexts for driving object parameters in feature-based engineering model.

Contexts in engineering model: characteristics, driving, and main types. Characteristics of feature driven shape model. Boundary representation of form features.

Relating parameters in structure of engineering objects

Representation of context between relevant object parameters. Optimizing object parameters: procedure, algorithms and place in model. Role of knowledge representation in parameter connections. Relating loads on objects and object performance parameters using method of finite elements.

Knowledge representation in engineering model

Production, control, action, and reaction types of knowledge representation and their role in self-adaptive generic model definitions. Role of hard and soft computing methods. Representation of dynamic and shape behaviors.

Contexts between objects in physical and virtual worlds

General scenario. Model representation of physically existing shape object: cloud of points, mesh, scan, and generation of curves. Role of object parameters in cyber-physical system (CPS).

 

Multilevel abstraction based multidisciplinary models of engineering systems

Program

Computation engineering doctoral subprogram

Concentration:

Subject for AIAMDI  PhD students

Type

Doctoral subject in connection with doctoral research of PhD student

Purpose and objective

The course introduces into system based representation of products as multidisciplinary engineering structures. It includes important issues from the latest engineering modeling where practice orientation is harmonized with strong theoretical, methodological, and system grounded fundamental, product related, and engineering problem solving purposed  research.

Topics

 

Multidisciplinary model

Need for integration of disciplinaries in engineering model. Modeling engineering structure as system. Role of behavior representation. Collaboration of disciplines  in model and its development.

Model in RFLP structure

RFLP structures engineering model. Connections within and between levels. Integration and main types of behaviors. Components on the RFLP structure levels and definition their connections

Knowledge driven adaptive model

Role and place of knowledge considering system based model of engineering structures. Groups of knowledge representation and role of knowledge in each group.

Coordinated and structured simulations

Multiphysics simulations and coupled behavior between multiply physical responses. Contextual chains of simulations. Simulation structure in multidisciplinary engineering model.

Optimizing object parameters

Optimization process and algorithms for contextual engineering model. Role of constraints. role, function, and characteristics of one of the algorithms selected in accordance with PhD research.

Methods for analysis of geometry and topology

Topology analysis based modeling on the physical level. objects. Possibilities for topology based analysis in research  purposed laboratory work.

Management of virtual product model

Special processes and management functions for complex virtual representation of multidisciplinary product considering RFLP structure. 

Knowledge representations

Knowledge representations in coordination with groups in topic "Knowledge driven adaptive model". Integration of theory and practice in virtual space. Integration of intelligent computing methods.

 

Modeling and Design

Program

Engineering Informatics MSc.

Concentration:

 

Type

Full time

Outcomes

.Course introduces recent engineering purposed virtual technology and systems for this purpose. By now, engineering applies one of the most comprehensive integrated information systems. Despite their key role in advanced industry, theories, methodologies and systemics in these systems are less understood by information technologists. Purpose of the course is to fill this gap and introduce relevant important modeling, simulation, knowledge technology, and systems engineering theories and .methodologies.  Emphases are on key virtual technologies such as active generic models based on knowledge representation and context chains, and representation of product structures as multidisciplinary systems. Besides representation of industrial products and experimental configurations using systems engineering, mathematics, cross-disciplinary definition, and knowledge technology, course introduces basics of organization of the demanded modeling and simulation capabilities by industries, discipline, and role. Model space is opened for each student in a world class engineering system during lectures and laboratories in order to better understand contemporary virtual engineering.

Topics of lectures

Click the title of lecture to access the .ppt file.

 

Laboratory hours include individual work of students in engineering virtual space according to the syllabus below.

 

Laboratory system: V6 PLM of the Dassault Systémes, Inc.

 

Virtual World for Engineers

Key concepts to understand engineering modeling. Modeling industrial product as system using RFLP structure. Way to Virtual Engineering Space. Object model of a solid body. Information integration. PLM model base. Cross-disciplinary development of multidisciplinary products. Cyber-physical-biological systems. System of Systems issues.

Representation of shape in product model

Analytical, rule driven, and free form shapes. Boundary representation: topology and geometry. Topology in solid body representation. Definition of topological structures by Euler operators. Form feature driven shape definition. Connections of shape with its information environment.

Connections between model entities

Associativities. Contextual connections. Geometry and dimension driven shape. Constraints and their saving at modification of model. Definition of surfaces and bodies in the context of curves and contours. Connections between bodies.

Representation of geometry

Approximation and interpolation curves and surfaces. Parametric equation of curve and surface. Spline base functions. Characteristics of segmented B-spline curves and surfaces. Handling curve and surface parameters, knot vector. Variants of B-spline curves. Unified geometry using NURBS representation.

Definition and analysis of product behavior

Analysis of place dependent parameters by finite element method. FEM/FEA. Analysis of collisions and DOF. Animation of position and shape of solid bodies. Shape optimization. Manikin modeling for ergonomic analysis.

Human and computer

Hunan-computer interactions. Thinking and seeing in a virtual space. Visually realistic modeling of surfaces. Tangible reality.

Cross Disciplinary Definition

Modeling Printed Circuit Board.  Constraint area definition. Electrical and mechanical components.  Placing components. Definition of traces.

Connection between real and virtual world objects

Virtual reconstruction of physical surfaces from cloud of points. Activating and filtering of a cloud. Mesh and its irregularities. Scan and methods of its creation. Generating curve on scan. Creating the free edges of a mesh. Ergonomic analysis using manikin model in the context of product model. Manikin attribute organization. Human posture analysis. Human activity analysis. Human measurements.

Representation of engineering knowledge

Role of knowledge in product model. Knowledge ware and Intelligent property. Representation and reuse of results and experiences. Combinations and relationships of parameters.  Application of methods of knowledge representation. Rule, check, reactions, and formulas and their sets. Optimizing parameters.

Modeling product as system

System modeling. RFLP Structure. Dynamic behavior.  State logic behavior. Behavior in a component. Virtual execution. Architecture of a logical system. Pathway sets. Implement relations. Simulations.

Modeling robot systems

Modeling the robot mechanical system. Engineering connections (joints).  Jogging a mechanism. About robot motion controller. Robot controller properties. Robot data profiles. Home positions of robot. Robot motion profile. Motion planning. Generic inverse kinematics at robots. Task design

Functional modeling of shape

About functional shape modeling. Entities in functional shape model. Functional behaviors. Functional features.

Product data management and exchange

Product data management.Characteristics of data in engineering virtual space. Engineering modeling specific data base management functions. Product structure. Data exchange between modeling systems. Development of data exchange standards. Initial Graphics Exchange Specification. Product model standard.

 

 

CAD Systems

Program

Mechatronic Engineering MSc

Concentration:

 

Type

Full time

Outcomes

The course program includes systems and their functionality for current advanced information technology as it applied at leading engineering systems for lifecycle management of product information. The main objective is to understand multidisciplinary aspect of model representation mediated engineering. Students receive related methodology, functionality, and system related knowledge during laboratories using  leading industrial engineering modeling system with high level knowledge representation capabilities.

Topics of laboratories

Click the title of lecture to access the .ppt file.

 

Laboratory hours include individual work of students in engineering virtual space according to the syllabus below.

 

Laboratory system: V6 PLM of the Dassault Systémes, Inc.

Systems for Lifecycle Management of Product Information

From physical representations to RFLP structure. History of integration. Object modeling. Functional modeling. Feature driven modeling. Product information in model – Exercise CS01.

Functionality of engineering modeling systems

Functionality of PLM systems. Collaboration of engineers. Engineering model definition and knowledge reuse. Definition and simulation of manufacturing. Organized realistic multiphysics simulation. Development of generic engineering model – Exercise CS21. From the shape modeling practice – Exercise CS22.

Shape Modeling by Form Features

Solid bodies in model space. Form features and their boundary representation. Combination of solid bodies. Modification of shape using topological entities.

Shape centric representation. Unified representation of geometry (NURBS) and topology.

Polyhedron modeling and its application at topological definitions. B-spline curves and surfaces. NURBS representation of geometry.  Euler rules and operators.

Representation of mechanism

Engineering connection between solid bodies – principle and parameters. Engineering connection with user defined constraints.  Engineering connection with predefined type.  Engineering connection between products. Definition of mechanism representation. Simulation of mechanism. PLM model base related considerations.

Definition features for printed circuit board (PCB)

PCB board features and their definition. Constraints. Electronic part representation.  Mounting capacitors, resistors, etc. on board.

Analysis on the principle of finite elements (FEM/FEA)

Purpose and applications of FEM/FEA in PLM systems. Preprocessing: definition of mesh, element, load, and restraint. Procedure and evaluation of analysis.

Functional shape modeling

About functional shape modeling. Functional behaviors. Entities in functional shape model. Functional features.

Processing cloud of points into curve and surface

Reverse engineering for the definition of surface using measured points. Gaining shape information from physical objects. Processing clouds of scanned points into curve and surface representations. Filters. Meshing. Concept and types of scan.

Contextual connections in shape model

Case study which organizes and represents main types of contextual connections in boundary represented and form feature constructed shape model. Students analyze active model in leading and theoretically and methodologically high level modeling system.

Model data exchange between different models. ISO 10303.

Product and model data exchange. Main formats. Reference model, resource, and application protocol (AP) based modeling when different modeling and model format are applied in different PLM systems in a project .

 

System Level Modeling for Cyber-Physical Engineering Structures in the Cloud

Programs:

Engineering Informatics and Applied Mathematics MSc.

Concentration:

 

Type

Full time

Course outcomes and description

Course consists of selected issues in latest principles, methods, and systemics of contemporary engineering informatics based on the capabilities of the world level representative and industrially proven 3DExperience platform. Its content is about fundamentals in one of the largest, most complex and highest-level IT applications! The main purpose of this subject is to understand and experience theoretical, methodological, and systemics basics in model of systems operated cyber-physical engineering structure related issues such as representations for the integration of theory and practical experience, multidisciplinary system-level models, generic self-adaptive object models, realistic simulation structures, as well as organic shape models and their relations with additive manufacturing methods. To enable students to learn and experience the engineering IT technology which is one of the basics of engineering activities at the two largest aircraft producer and at many other leading industrial companies, the work during lecture and laboratory hours proceeds in a project which serves the course in the 3Dexperience system. The 3Dexperience is available at the Dassault's Systémes cloud for the Doctoral School of Applied Informatics and Applied Mathematics (AIAMDI), Óbuda University. The 3DExperience platform is regarded as the flagship of engineering systems. It is product of development and market leader Dassault Systémes, which operates and continually updates our 3DExperience system in the cloud. Project participants have access to personalized modeling capabilities based on field of activity, current disciplines, and human roles. The primary purpose of the new 3DExperience system is to provide state-of-the-art laboratory research environment for the newly accredited doctoral program at the AIAMDI in the Dassault cloud. In this way the course plays a role in the preparation of students for doctoral research. Thus, the student participating at this course and interesting in its issues can prepare doctoral school application beyond providing topics for MSc degree thesis work. In the laboratory, all workstations are specified to be suitable for the 3DExperience.

Topics

Click the title to access the .ppt file for each topic.

 

Laboratory hours include individual work of students in virtual engineering space (VES) using laboratory system using personally available workstations.

 

Laboratory system: 3DEXPERIENCE platform and V6 PLM system by Dassault Systémes, Inc.

 

Models of cooperating systems

Lecture: Models of cooperating systems operated cyber-physical engineering structures (experimental configuration, prototype, industrial product).

Laboratory: Introduction to the laboratory environment and the course project in the cloud.

Engineering modeling basics

Lecture: Engineering modeling basics: model space, coordinate systems and transformations, modeling capabilities, model object, shape representation, system of contexts, feature, functional and logical concept model, behavior.

Laboratory: Analysis and understanding case study in thematic model in the cloud for the issues of the lecture.

Shape model

Lecture: Shape model: boundary representation, topology, NURBS, curves and surfaces, and form features.

Laboratory: Definition of thematic model and its analysis and understanding in the cloud for lecture issues as individual work of each student.

Component connections

Lecture: Representation of component connections in multidisciplinary engineering structures: constraints, degrees of freedom, contexts.

Laboratory: Definition of thematic model and its analysis and understanding in the cloud for lecture issues as individual classroom work of each student. Deadline of title and short summary for the assignment.

Realistic multiphysics analysis

Lecture: Realistic multiphysics analysis of performance parameters in engineering structures. New application of the finite element method. Definition of structured simulations in model.

Laboratory: Analysis and understanding case study in thematic model in the cloud for the issues of the lecture. Information about course work for the assignment in cloud.

Modeling capabilities

Lecture and laboratory: Structure of modeling capabilities on the example of 3DExperience.

Active knowledge representations

Lecture: Active knowledge representation. Optimization algorithms in model structure. Problem solving in wide context. Connecting representations of theory and experience. Integration of outside solvers such as Dymola and Simulink. Concept of intellectual property (IP).

Laboratory: Definition of thematic model and its analysis and understanding in the cloud for lecture issues as individual classroom work of each student.

System level model in RFLP structure

Lecture: System level model in RFLP structure. Representation of behaviors and their role at virtual execution of concept model.

Laboratory: Analysis and understanding case study in thematic model in the cloud for the issues of the lecture.

Generic self-adaptive shape model

Lecture: Generic self-adaptive shape model which can be driven from the outside and capable of function related behavior.

Laboratory: Definition of thematic model and its analysis and understanding in the cloud for lecture issues as individual work of each student.

Organic shape representations

Lecture: Physical system that includes both rigid and flexible elements. Understanding geometric and organic shapes. Function driven organic shape representations. T splines. Relations to additive manufacturing methods.

Laboratory: Analysis and understanding case study in thematic model in the cloud for the issues of the lecture.

Model of robot system

Lecture: Model of robot system. Representation of mechanical and control systems. Structure, contexts, and simulations of robot model on the example of relevant modeling capabilities in 3DExperience.

Laboratory: Developing model for assignment in the cloud project of the course.

Engineering modeling project. Course work

Lecture: Engineering modeling project in which modeling capabilities are available for each participant in accordance with actual industry, relevant disciplines and roles. Explanation in the 3DExperience project of the course.

Laboratory: Developing model for assignment in the cloud project of the course.

Course work

Laboratory: Developing model for assignment in the cloud project of the course.

Trends in virtual engineering. Student presentations (mini conference)

Lecture: Actual trends in development of virtual engineering systems and the relevant possibilities for carrier in engineering.

Laboratory: Presentation the assignment using model in cloud.

 

Modeling and simulation

Program

Mechatronic Engineering MSc

Concentration:

 

Type

Full time

Outcomes

Course introduces advanced knowledge based engineering modeling methodology in a laboratory system which represents world class virtual engineering. Students work in personally opened model spaces and contribute to student project by active modes using active model definition environment. They understand modeling and associated simulations using definition of highly integrated self-adaptive model systems. These models are in possession of capability for self-modification in case of changed requirements and situations. Topics such as mechatronic specific multidisciplinary  model, product system representation in RFLP structure, engineering system which organize modeling and simulation capabilities on the basis of actual industries, disciplinary areas, and human roles, and concept of intellectual property represent world leading virtual technology.

Topics of laboratories

Click the title of lecture to access the .ppt file.

 

Laboratory hours include individual work of students in engineering virtual space according to the syllabus below.

 

Laboratory system: V6 PLM of the Dassault Systémes, Inc.

 

1A. Introduction to the course. Collaborative Engineering in Virtual Environment

1B. Modeling product as system

2. Shape modeling by boundary representation. Contextual curves, surfaces, and bodies.

3. Involving shape into solid body by surface based form features.

4.Definition and simulation of form features in solid representation of bodies. 

5. Engineering connection of bodies using topology. Placing and kinematics  (See also: 8-9)

6.  Including finite element based simulation representations in part models. 

8-9 Simulations for groups of parts in order to analysis of relative positions and movements. Eight  Ninth

10. Generation of curves and surfaces from cloud of scanned points.

11. Representation of human intent, experience, expertise, and knowledge.

12. Ergonomic analysis using product model integrated manikin model.