In the rapidly evolving landscape of industrial production, the integration of knowledge bases into design processes stands as a cornerstone for innovation, efficiency, and competitive advantage. The concept of knowledge bases – repositories of structured and unstructured information – has revolutionized how designers approach problem-solving and creativity in industrial settings.

At the heart of industrial production lies the perpetual quest for efficiency, quality, and sustainability, all of which are profoundly influenced by the design phase. In this context, knowledge bases emerge as pivotal instruments. They encapsulate a vast array of information, ranging from historical data, design precedents, and technical specifications, to market trends and consumer insights. This amalgamation of data serves as a vital resource for designers, enabling them to make informed decisions, foresee market trends, and tailor their designs to meet both present and future needs.

Moreover, knowledge bases in design transcend the traditional boundaries of information storage. They foster an environment of continuous learning and adaptation, where insights from past projects and emerging technologies are dynamically integrated. This not only enhances the quality and relevance of designs but also propels innovation, pushing the boundaries of what is possible in industrial production. By leveraging artificial intelligence and machine learning algorithms, knowledge bases can analyze patterns, predict outcomes, and suggest optimizations, thereby streamlining the design process and augmenting human creativity.

Total Hours

This course unit covers 100 hours, from which 14 hours lectures, 14 hours lab work, and 72 hours individual study and work.

General Objective

The primary objective of this course is to equip students with a comprehensive understanding and practical skills in the development and application of knowledge bases within the realm of design. This entails providing a thorough grounding in the theoretical aspects of knowledge-base engineering, coupled with hands-on experience in leveraging these systems for practical design solutions. By the end of the course, students will be proficient in utilizing and customizing knowledge bases to enhance and optimize design processes, enabling them to effectively implement these advanced techniques in real-world scenarios. This objective aligns with the broader goal of preparing students to be proficient and innovative designers who can contribute significantly to their respective fields by integrating sophisticated knowledge management tools into their design practices.

Specific Objectives / Learning Outcomes
  • Skill Development for Identifying Knowledge Base Applications:To enable students to identify and assess scenarios in design where the application of a knowledge base can significantly add value. This involves understanding the characteristics of projects where knowledge bases can enhance efficiency, creativity, and effectiveness.

  • Designing Customized Knowledge Bases:To develop the capability among students to design and construct knowledge bases that are specifically tailored to meet the unique requirements of different fields or projects. This includes training in various tools and methodologies for creating adaptable and scalable knowledge bases.

  • Practical Application Through Case Studies and Projects: To provide students with hands-on experience by engaging them in diverse case studies and practical projects. This objective aims to bridge the gap between theoretical knowledge and practical application, allowing students to apply their learning in real-world contexts and see the tangible impact of knowledge bases in design.

  • Keeping Abreast with Trends and Innovations: To ensure students are well-versed with the latest trends, technological advancements, and innovations in the field of knowledge bases. This includes understanding how these developments influence and drive the future of design, preparing students to be forward-thinking and adaptable to emerging changes in their professional field.

These specific objectives are designed to comprehensively cover the spectrum of skills and knowledge required to proficiently use and develop knowledge bases in design, ensuring students are well-prepared for practical application in their future careers.

Professional Competencies

Considering the content of the course units and the project work associated with the development and integration of knowledge bases in design, particularly within the context of the 3D Experience platform, the following professional competencies are developed:

  • Expertise in Knowledge-Based Engineering and 3D Design Platforms: Proficiency in using and customizing knowledge bases specifically for design applications, particularly within 3D environments. This includes a thorough understanding of platforms like the 3D Experience platform, enabling students to effectively integrate and utilize these tools in various design scenarios.

  • Advanced Skills in 3D Modeling and Parametric Design: Competence in advanced 3D modeling techniques and parametric design, using tools such as CAD software, which are integral to the creation of complex geometries and designs in the 3D Experience environment.

  • Programming and Algorithmic Design: Skills in programming and algorithmic design, essential for developing and implementing generative design algorithms and for customizing knowledge bases. This includes understanding scripting and coding within design environments, enabling the automation and optimization of design processes.

  • Data Analysis and Simulation for Design Evaluation: Ability to conduct data analysis and simulation to assess the performance and viability of designs. This includes using AI and machine learning tools for predictive analysis and validation of design concepts against set requirements and constraints.

  • System Integration and Workflow Optimization: Skills in integrating various systems and workflows within the 3D Experience platform, ensuring seamless interaction between knowledge bases and design modules. This includes understanding how to leverage the platform for efficient workflow management in design projects.

  • Problem-Solving and Creative Thinking in Design: Strong problem-solving abilities and creative thinking skills, particularly in the context of using knowledge bases to address complex design challenges. This involves applying theoretical knowledge to practical situations, coming up with innovative solutions that are technically sound and creatively compelling.

  • Interdisciplinary Collaboration and Communication Skills: Competence in collaborating across disciplines and effectively communicating design ideas and decisions. This is crucial for working in teams, especially in environments where knowledge bases are integrated with other technological aspects of design.

  • Understanding of Manufacturing Processes and Constraints: Knowledge of various manufacturing processes, materials, and their constraints, especially in relation to how designs are realized in physical forms, whether through traditional manufacturing or additive manufacturing techniques.

These professional competencies ensure that students are not only adept in the theoretical aspects of knowledge bases and 3D design but are also equipped with practical skills necessary for real-world applications, making them valuable assets in any design-oriented industry.

Cross Competencies
  • Integration of Programming and Informatics in Design: Competence in selecting, combining, and applying fundamental concepts, theories, and methods from computer programming and applied informatics, particularly as they pertain to the specialization. This includes the ability to integrate these skills in the development and enhancement of knowledge bases within the design process. Additionally, students will develop proficiency in utilizing these technical skills in professional communication, ensuring they can effectively articulate complex technical information in various professional contexts.

  • Creative Problem-Solving in Design: The ability to engage in creative thinking and innovative problem-solving. This cross-competency focuses on developing the capacity to approach complex design challenges creatively and devise innovative solutions. It emphasizes the importance of a robust creative process that combines technical knowledge with imaginative thinking, enabling students to address and solve multifaceted problems in design through unique and effective strategies.

  • Collaborative Communication and Justification of Design Decisions: Strong communication skills tailored for collaboration within interdisciplinary teams. This involves fostering the ability to clearly and convincingly explain and justify design decisions and processes, especially those involving the application and integration of knowledge bases. The competency underscores the significance of effective communication skills in collaborative environments, ensuring that students can work harmoniously and productively with professionals from various disciplines and effectively convey their ideas and rationale behind design choices.

These cross-competencies are designed to empower students with a holistic skill set that blends technical proficiency in programming and informatics with creative problem-solving and effective communication. This combination is crucial in preparing students to excel in professional environments where interdisciplinary collaboration and innovative approaches to design are highly valued.

Alignment to Social and Economic Expectations

The design knowledge base course, leveraging the 3D Experience platform, aligns strongly with both social and economic expectations in today’s rapidly evolving industrial landscape. Economically, it boosts efficiency by streamlining design processes and improving the quality and consistency of data. This optimization not only accelerates project adaptability but also ensures seamless integration of diverse modules within the 3D Experience ecosystem, a critical factor in responding to fluctuating market demands. Socially, the course significantly reduces human error by augmenting decision-making with automated checks, which is vital for maintaining high standards of safety and quality in design outputs. Moreover, by equipping participants with cutting-edge technological skills and fostering innovative thinking, the course not only gives them a competitive edge in the job market but also promotes a culture of continuous innovation. This aspect is crucial for driving forward-thinking design solutions that meet evolving social needs and preferences, thereby contributing to sustainable and responsible industrial development.


Assessment Methods

Theoretical Lectures Component:

  • Quizzes:

    • Regular in-class and online quizzes to evaluate understanding of knowledge base concepts, 3D design principles, and their applications.

  • Written Assignments:

    • Assignments requiring critical analysis of real-world applications of knowledge bases in product design, focusing on problem-solving and theoretical application.

  • Midterm and Final Exams:

    • Comprehensive exams to assess overall understanding, including multiple-choice, short answer, and essay questions on knowledge bases and 3D Experience platform.

Practical Laboratory Component:

  • Lab Reports:

    • Detailed reports documenting experiments in knowledge base implementation and integration with 3D design systems, focusing on methodology, results, and analytical insights.

  • Oral Presentations:

    • Presentations on lab projects, assessed based on presentation skills, content clarity, and engagement, particularly focusing on solutions developed using knowledge bases and 3D Experience.

Assessment Criteria

Lectures Component:

  • Knowledge and Understanding: Comprehension and application of core concepts of knowledge bases in 3D design.

  • Analytical and Problem-Solving Skills: Ability to analyze challenges and apply knowledge base solutions in design contexts.

  • Communication Skills: Proficiency in conveying complex concepts and solutions clearly.

  • Teamwork and Collaboration Skills: Effectiveness in group projects, particularly in collaborative design tasks.

  • Application of Technology: Proficiency in using 3D Experience platform and related tools.

Laboratory Work Component:

  • Technical Skills: Competence in applying technical skills for developing practical solutions using knowledge bases.

  • Quality of Work: Ability to produce high-quality, innovative solutions.

  • Creativity and Innovation: Capacity for creativity in developing knowledge base applications.

  • Attention to Detail: Thoroughness in documenting and executing projects.

  • Time Management: Effectiveness in managing time for lab tasks and projects.

Quantitative Performance Indicators

For Lectures:

  • Attendance and Participation: Minimum 80% attendance and active class participation.

  • Homework and Quizzes: Completion of all assignments and quizzes with a minimum average score of 60%.

  • Midterm Exam: Minimum score of 50%.

For Lab Works:

  • Lab Attendance and Participation: Full attendance and active participation in lab sessions.

  • Lab Reports: Timely submission with a minimum score of 60%.

  • Lab Assignments: Completion with a minimum average score of 60%.

  • Lab Exams: Minimum score of 50%.

For Final Exam:

  • Understanding of Core Concepts: Minimum score of 50% on concept-related questions.

  • Application of Knowledge: Minimum score of 50% on application-based questions.

  • Critical Analysis and Problem-Solving: Minimum score of 50% on case studies and problem-solving questions.

  • Overall Performance: Evaluated as a percentage of total score, with a minimum passing mark of 50%.

This evaluation framework ensures a comprehensive assessment of both theoretical knowledge and practical skills in knowledge base engineering and 3D design using the 3D Experience platform.


Unit 1: Introduction to the Knowledge Base and the 3D Experience Platform (2 hours)

Lecture/Course Objectives and List of References

  • Overview of knowledge bases: Definition, importance, and application in industrial design.

  • Introduction to the 3D Experience Platform: Features and capabilities.

  • The synergy between knowledge bases and 3D Experience in industrial production.

Key Concepts in Knowledge Bases and 3D Experience Platforms

  • Understanding the structure and function of knowledge bases.

  • Exploring the architecture of the 3D Experience Platform.

  • The role of knowledge bases in enhancing the functionality of the 3D Experience Platform.

Historical Evolution and Current Trends in Knowledge Bases

  • Tracing the development of knowledge bases in industrial design.

  • Case studies showcasing successful integration of knowledge bases with the 3D Experience Platform.

Unit 2: Representation of Knowledge in 3D Environments (2 hours)

Introduction to Representing Knowledge in 3D

  • Fundamentals of 3D knowledge representation.

  • Techniques and tools for visualizing knowledge in 3D environments.

Effective Strategies for 3D Knowledge Visualization

  • Principles of effective 3D knowledge representation.

  • Examples and case studies of 3D knowledge visualization in industrial design.

Interactive Session: Hands-on Exercises in 3D Knowledge Representation

  • Practical exercises for students to create basic 3D knowledge representations.

  • Analysis and discussion of the representations created by students.

Unit 3: Design and Implementation of 3D Knowledge Bases in 3D Experience (2 hours)

The Design Process of 3D Knowledge Bases

  • Steps and considerations in designing 3D knowledge bases.

  • Integration strategies for 3D knowledge bases in the 3D Experience platform.

Implementation Challenges and Solutions

  • Common challenges in implementing 3D knowledge bases.

  • Solutions and best practices for effective implementation.

Case Studies: Successful Implementations of 3D Knowledge Bases

  • Analysis of real-world examples where 3D knowledge bases have been effectively implemented.

Unit 4: Integrating Knowledge Bases with 3D Design Systems (2 hours)

Principles of Integration between Knowledge Bases and 3D Design

  • Theoretical framework for integrating knowledge bases with 3D design systems.

  • Benefits and potential enhancements from integration.

Techniques and Tools for Integration

  • Overview of software and tools used for integration.

  • Step-by-step guide to integrating knowledge bases with 3D design systems.

Workshop: Practical Integration Exercise

  • Hands-on session where students integrate a simple knowledge base with a 3D design system.

Unit 5: Reasoning and Inference in 3D Design (2 hours)

Fundamentals of Reasoning and Inference in 3D Design

  • Introduction to the concepts of reasoning and inference in the context of 3D design.

  • The importance of these processes in enhancing design quality and efficiency.

Advanced Techniques in Reasoning and Inference

  • Detailed exploration of advanced reasoning and inference techniques.

  • Interactive exercises for students to apply these techniques in hypothetical scenarios.

Case Studies: Reasoning and Inference in Practice

  • Examination of case studies where reasoning and inference have played a pivotal role in 3D design.

Unit 6: Management and Maintenance of Knowledge Bases in 3D Experience (2 hours)

Best Practices in Knowledge Base Management

  • Strategies for effective management and maintenance of knowledge bases.

  • Tools and technologies that aid in knowledge base management.

Challenges in Knowledge Base Maintenance and Their Solutions

  • Identifying and addressing common challenges in knowledge base maintenance.

  • Practical tips and solutions for these challenges.

Interactive Session: Maintenance Workshop

  • Hands-on workshop where students engage in maintenance activities of a sample knowledge base.

Unit 7: Trends in the Use of Knowledge Bases in 3D Design (2 hours)

Current and Emerging Trends

  • Overview of the latest trends in the use of knowledge bases in 3D design.

  • Predictions for future developments in this field.

Implications of These Trends on Industrial Design

  • Analysis of how these trends are shaping the future of industrial design.

  • Discussion on the potential for innovation and improvement in design processes.

Panel Discussion: Future of Knowledge Bases in 3D Design

  • A panel discussion featuring experts in the field, exploring future directions and opportunities in knowledge base technology and 3D design.

Project Work

Development and Integration of a Knowledge Base in Design Systems

Phase 1: Definition of Objectives and Requirements

  • Objective: Students define the purpose and scope of their knowledge base project.
  • Activities:
    • Identifying the primary function of the knowledge base in a design context.
    • Setting clear, measurable objectives.
    • Outlining the requirements necessary to achieve these objectives.
  • Deliverable: A written document detailing the project’s objectives and requirements.

Phase 2: Analysis of Needs and Requirements

  • Objective: To conduct a thorough analysis of the needs and requirements for the knowledge base.
  • Activities:
    • Gathering data on user needs and preferences.
    • Analyzing existing systems and identifying gaps or areas for improvement.
    • Prioritizing requirements based on feasibility and impact.
  • Deliverable: A comprehensive needs and requirements analysis report.

Phase 3: Conceptual Design of the Knowledge Base

  • Objective: Create a conceptual design for the knowledge base.
  • Activities:
    • Developing a high-level design that outlines the structure and functionality of the knowledge base.
    • Creating design mock-ups or prototypes.
    • Presenting the design concept for feedback.
  • Deliverable: Conceptual design document, including diagrams and prototypes.

Phase 4: Implementation of the Knowledge Base

  • Objective: To implement the designed knowledge base.
  • Activities:
    • Coding and developing the knowledge base according to the design.
    • Integrating necessary databases and information sources.
    • Regularly updating the progress and making necessary adjustments.
  • Deliverable: A functional version of the knowledge base.

Phase 5: Integration with Design Systems

  • Objective: Seamlessly integrate the knowledge base with existing design systems.
  • Activities:
    • Establishing integration points with design software or systems.
    • Ensuring compatibility and efficient data exchange.
    • Conducting initial integration tests.
  • Deliverable: An integrated system where the knowledge base and design systems work in tandem.

Phase 6: Testing and Evaluation

  • Objective: Rigorously test the knowledge base and evaluate its performance.
  • Activities:
    • Performing a variety of tests (functional, usability, performance).
    • Collecting and analyzing feedback from test users.
    • Making refinements based on evaluation results.
  • Deliverable: A testing and evaluation report, including feedback analysis and any adjustments made.

Phase 7: Documentation and Production Deployment

  • Objective: Prepare comprehensive documentation and deploy the knowledge base in a production environment.
  • Activities:
    • Creating user manuals, technical documentation, and maintenance guides.
    • Preparing the system for deployment in a real-world setting.
    • Implementing deployment and monitoring initial usage.
  • Deliverable: Complete documentation set and a deployed knowledge base system.
Supporting Infrastructure

Given the focus on Knowledge-Based Engineering (KBE) systems and their application in product and process modeling within the context of product design, the technology stack for the project is tailored to facilitate these specific needs.

  • KBE Systems and Modeling Tools:
    • Protégé: An open-source ontology editor and framework for building intelligent systems, commonly used for knowledge representation and KBE.
    • PLTool: A tool for procedural logic, useful for defining and managing the logic of engineering processes.
    • Prodigy: An environment for the rapid development of AI systems, which can be used for integrating AI into the KBE process.
    • myPDDL: A tool for working with the Planning Domain Definition Language, useful for automated planning in AI.
  • 3D Design and CAD Software:
    • Autodesk AutoCAD or SolidWorks for 3D modeling and integration with the knowledge base.
    • Dassault Systèmes 3D Experience, CATIA, which are widely used in product design and integrates well with KBE systems.
  • Database Management Systems (DBMS):
    • MySQL or PostgreSQL for structured data management.
    • MongoDB for managing unstructured or semi-structured data.
  • Data Integration and Processing:
    • ETL (Extract, Transform, Load) tools for data integration from various sources.
    • Data processing languages like Python or R, especially for data analysis and manipulation.
  • Simulation and Analysis Tools:
    • ANSYS, MATLAB, or similar for physics-based analysis and simulation.
    • Integration of these tools with KBE systems for advanced modeling and analysis.
  • Cloud Computing and Storage Services:
    • UT Cloud or Microsoft Azure Cloud for hosting databases, applications, and providing computational resources.

This technology stack provides a robust framework for developing a KBE system tailored to product design and engineering processes. It covers aspects of knowledge representation, 3D modeling, data management, simulation, and analysis, all critical for a comprehensive KBE system in product design.