Introduction to the principles of Business Intelligence and Data Visualization
Getting started with Power BI software:
Interface exploration
Creation of graphical visuals
Understanding, preparing, and cleaning data before use
Principles of data modeling
Data analysis language: DAX
Final project in pairs

* Key Concepts and Terminology – Applications – Ethics and environmental concerns
* Data – Models (KNN – K-Means – Regressions – PCA – Decision Tree)
* Deep Learning – LLM – Generative AI
* Applications

Course outline:
- Graphs: definitions
- Connectivity
- Shortpath problem
- Hamitonians paths and heuristic
- Minimum spanning tree
- Graphs coloration
- Maximum flow
- Assignment Problem
- Binpacking

* Lectures
* Quiz sessions
* Final certification exam

• Strategies & Effective Meeting Falication using IoT, DMAIC, 5S, UX, HMI
• Analyse the product/process impacts and propose improvements
• Organisation and management of a production through a challenge in the "School Factory" platform
• Discovery and Robotization Project:
- To be able to manipulate robots virtually and physically
- Initiation to a method of implementation of robotic equipment
- To determine the cost effectiveness of robotization on workstation
- Difference robot/cobot (strategic, productivity, safety ...)
- Risk analysis
- Robotisation criteria

* Lectures, tutorials and lab work on the main concepts
* Implementation during the red thread team project (Connected Speaker)
* Simulation on the Theory of Contraints (TOC)
* Serious game DDBrix on DDMRP
* CSCA course material

The Innovation Project is a multidisciplinary teamwork experience where students from various engineering fields collaborate on a product innovation. Spanning two semesters, the project follows all phases of an R&D process—from opportunity analysis to technical development.

Technical Product Architecture Selection:
Identify and justify the chosen Technical Product Architecture based on evaluation criteria and alignment with project specifications.

Subsystem Analysis:
Break down the project into subsystems, mapping out dependencies and interactions. Present and provide preliminary sizing or estimation for each subsystem with key metrics.

Project Planning:
Develop a work plan that includes task breakdowns, role assignments, a timeline with intermediate deadlines, and team coordination strategies.

Risk Analysis:
Identify potential risks and outline basic mitigation strategies to manage and monitor them throughout the project.

The Innovation Project is a multidisciplinary teamwork experience where students from various engineering fields collaborate on a product innovation. Spanning two semesters, the project follows all phases of an R&D process—from opportunity analysis to technical development.
Comprehensive Project Overview:
Produce a cohesive global report that synthesizes project management processes, technical decisions, and business planning, demonstrating a clear understanding of the project’s scope and objectives.

Technical Documentation Skills:
Develop detailed technical appendices that articulate the technical deployments of the project, adhering to requirements and including essential components such as CAD files and VR models.

Testing Methodology:
Design and document testing protocols for both technical performance tests and user adaptability tests, ensuring that all requirements are adequately addressed.

Critical Analysis of Results:
Analyze and evaluate test results critically, providing insights into performance fulfillment and user adaptability, and discussing implications for future improvements.

Professional Reporting:
Create a well-structured and professionally presented report that meets academic standards and communicates complex technical information effectively to an expert audience.

Mission Statement:
Define Business Vision: Clearly articulate your business's starting point and future direction.
Analyze Market Evolution: Predict and understand market trends and technology adoption.
Develop Strategic Roadmap: Create a plan to achieve your vision.
Identify Market Segments: Prioritize and target specific market segments.
Apply Market Analysis: Use top-down and bottom-up approaches for market analysis.

Competition Analysis:
Evaluate Competition: Analyze both direct and indirect competitors.
Differentiate Your Business: Highlight and sustain your competitive advantage.
Map Competitors: Use competitive mapping to position your startup effectively.

Business Model:
Create Value: Develop a value creation strategy, including potential partnerships.
Target Market: Identify and describe your target market.
Design Customer Journey: Outline the customer's path from awareness to purchase.

Go-to-Market Strategy:
Position Your Brand: Define your brand's values and market positioning.
Refine Target Strategy: Clearly identify and justify your target market.

Comprehensive Project Understanding:
Demonstrate a deep understanding of the project’s objectives, processes, and outcomes, including how initial goals were met or adapted throughout the year.

Effective Communication:
Clearly and confidently present the project’s journey, including technical details, decision-making processes, and key milestones, to an audience of experts and non-experts.

Critical Reflection:
Reflect on the challenges faced during the project, how they were overcome, and what was learned from those experiences.

Justification of Decisions:
Provide a well-reasoned explanation for key decisions made throughout the project, including design choices, problem-solving strategies, and risk management approaches.

Impact and Future Implications:
Evaluate the project’s impact, potential for future development, and relevance to the broader field or market, suggesting next steps or improvements.

Testing Principles:
Understand the fundamental principles and objectives of user testing in product development.

Planning User Tests:
Develop a structured approach to planning user testing sessions, including defining objectives and selecting appropriate methods.

Conducting Tests:
Execute user testing sessions effectively, including participant management and data collection.

Analyzing Results:
Evaluate and interpret the outcomes of user tests to identify areas for improvement in the product.

Communicating Insights:
Clearly present and communicate testing findings to stakeholders, highlighting actionable recommendations for product enhancement.

- Introduction to welding:
- Definition
- Weldability of materials
- Methods of welding: Gaz welding, arc welding, MIG, TIG,...
- Indication of welding on technical drawing
- Rules in mechanical design
- Use of standard profiles:
- Cutting
- Laser cutting
- Roll bending
- Sheet metal work:
- Cutting: shearing, plasma, laser, water jet, punching
- Bending
- Roll bending
- Stamping
- Corrosion prevention
- Use of CREO Parametric
- CAD project

Integration of the digital chain in the preparation work. Cutting conditions for solid materials.
Optimization of the machining parameters (cutting conditions, CAM "Spirit", process studies, choice of tools ...).

Optimization methods are increasingly used today. In the field of structural design, they enable in particular to reduce the mass of a mechanical system. Mass reduction leads to production cost reduction (less material), and possibly cost of use reduction (lower energy consumption).

Among the different methods, topological optimization is currently experiencing strong development because of the interest it represents when associated with 3D printing.
- The 2 first sessions consist in a lecture about the method, followed by a tutorial session on a practical example. The objective is to make students aware of this method.
- The 4 following sessions will be devoted to carrying out a project in a group and independently. This project will consist of the following stages:
* Mechanical characterization of the material used: creation and 3D printing of test specimens which will be subjected to a tensile test. The anisotropy of the material can be highlighted and characterized.
* Topological optimization: search for an optimized solution for a given load case. The influence of parameters such as mesh size, boundary conditions or optimization methods should be evaluated.
* Numerical validation of the optimized structure (simulation on the optimized structure).
* 3D printing of the optimized structure and mechanical test on this part. The experimental results will be compared with the numerical results. Hypotheses on the differences will then be formulated.

Why do we need Finite Element Analysis (FEA) in engineering analysis ?
Various FEA applications
Stiffness
Stiffness of a spring and a bar
Basic steps involved in FEA
Types of FEA and element types used in FEA
Derivation of stiffness matrix
Problem solving on spring and bar elements
Matlab basics and Problem solving on spring and bar elements
Beam elements
Isoparametric formulations and shape functions

1. Introduction
• Definition of Tribology and Tribological Systems
• Applications and Scales in Tribology
• Types of Motion
• Tribological Testing and Types of Tribometers
• Roughness
2. Lubrication and Lubricant Properties
• Classification of Lubrication and Lubricant Properties
• The Stribeck Curve and Lubrication Regimes
• Types of contacts and film thickness
• Hertzian Contact Theory
• Solid-Solid Contact-Asperity Deformation
3. Friction
• Friction Coefficient
• Types of Friction
• Laws of Dry Friction
• Basic Mechanisms of Sliding Friction
• Adhesion and Deformation
• Stick-Slip Phenomenon
4. Fluid Film Lubrication
• Classification of Fluid Film Lubrication
• Newtonian and Non-Newtonian Fluids
• 1-D Flow between Parallel Plates
• Hydrostatic and Hydrodynamic Thrust Bearings
5. Wear
• Parameters and Stages of Wear
• Wear Types and Mechanisms
• Wear Rate
• Causes of Wear and Prevention Methods

This course divided into four parts. In the first part will give an overview of industrial robots basic components and structures. Part 2 depicts principles and methods of programming robots. Part 3 describes industry robotisation and robots workstations. The final part touches the safety of industrial robots and cobots.
In this course, we will explore Arm construction and drives, Coordinates systems (BASE • TOOL • TCP • Part frame/User frame/Working frame), programming methods (Online • Offline), Robotized workstations/cells, risk assessments of robotic cells and standardizations/normalizations.

• Introduction to industrial robotics
• Basic components of industrial robot systems
• Structure of industrial robots
• Collaborative, non-collaborative, and mobile industrial robot applications
• Industrial robot’s motion
• Methods of programming robots
• Hazards associated with industrial robot applications
• Safety considerations for employers and workers
• Risk assessments
• Risk reduction measures
• Applicable industry standards for industrial robot system safety

-Definition of a PLC
-Hardware components of a PLC
-Connection of I/O modules
-Program a filling machine using Ladder, Function block, Structured text, Grafcet
-Train on data types, variables

The scrum methodology is introduced to the students. Then, they apply this agile framework during the whole duration of the project.
Each group of students receives a project of robotic application. They state the problem before designing the robotic system that corresponds to the specifications. Then, they build their system and test it extensively.
Finally, each group presents their work and write a report describing the technical and managerial aspects of the project.

* Introduction to IA and Machine Learning
° Model Based Learning - main concepts and definitions
° Exact solution, iterative solutions, Gradient Descent
° First algorithms: Regressions
- Linear Regression
- Logistic Regression
° Data preprocessing
° Hyperparameter tuning

* Towards Deep Learning
° more on preprocessing (categories encoding) and data sets
° From biological neuron to perceptron
° Multilayer Perceptron
° Convolutional Neural Networks
° Transfer Learning

* Introduction to Natural Language Processing
° Some important ideas about NLP
° Example of statistical NLP (Multinomial Naive Bayes)
° NLP with Deep Learning (LSTM)

* Other algorithms

* Group project (text classification, image classification, recommendation, regression...)

-Introduction to camera features
-Introduction to the importance of lighting in image acquisition
-Introduction to various technologies in image acquisition
-Practical application on stereovision

-Definition of industrial networks: Ethercat, Profinet, OPC-UA, IO-Link…
-Practical on PLC/sensors/actuators communication using several industrial networks.
-Definition of safety regulations
-Procedure to perform risk analysis
-Definition of drive-based and controller-based motion planning followed by practicals

-Introduction to IoT devices and their architectures (microcontroller, antenna…)
-Introduction to radio waves and principles of modulation (amplitude, frequency and phase)
-Definition of the various IoT networks (Short-range, LPWAN, Cellular)
-Practical case study to choose the best IoT solution given technical specifications
-Practical introduction to IoT programming with Sigfox modules for Arduino board

The course Society, Management and Entrepreneurship Final Project 2 will consolidate the students’ knowledge of the social sciences, entrepreneurship and management theory. It builds on students’ learning in SME Final Project 1 by further developing their businesses and outlining strategies for expansion in international markets.

This course is an introduction to the circular economy concept. In this course, student will learn about the 7 pillars of the circular economy with many examples.

Session 1 – Introduction to Corporate Social Responsibility
Foundations and Historical Context
Fundamental Principles of CSR
CSR Frameworks and Reporting Standards
CSR Challenges and Opportunities

Session 2 – Building a CSR Strategy
General Approach for Companies
CSR Strategy Development Methodology
Case Studies and Practical Examples

- Innovation management for companies (different types)
- Intellectual property: patent deposit, management of patents
- General management of organizations for keeping the company's strategic advantages.

Group Project structured around various sessions of group work with access to the necessary computer resources.

• Presentation of different types of energy storage systems (heat, mechanical and electrochemical);
• Definition of energy and power densities;
• Description of the operating principle of rechargeable batteries and their fundamental electrochemistry (lithium-ion batteries, lead-acid batteries, etc.);
• Definition of the electrical characteristics present in the data sheet of each electrochemical storage system;
• Description of the operating principle of conventional supercapacitors using the double layer capacitance theory;
• Presentation of the chemical constitution of hybrid supercapacitors such as lithium-ion capacitors;
• Description of aging mechanisms that may arise in different types of batteries and supercapacitors;
• Comparison of different energy storage systems using the Ragone Diagram;
• Presentation of electrical modeling methods of electrochemical energy storage systems;
• Presentation of power converters used with energy storage systems;
• Description of the tools integrated in management systems that aim to control energy storage systems;
• Presentation of an example of a complete system integrating an energy storage system, the corresponding management system and the power converters.

• Fundamental concepts of energy engineering: primary energy, final energy, embodied energy, lost energy, etc.
• Performance criteria used to assess existing systems or processes: efficiency, effectiveness, energy returned on energy invested (EROI), etc.
• Definitions and limitations of today’s energy resources: peak oil, GHG emissions, etc.
• Carbon cycles and balances: why it is important and how to use them.
• The energy transition and its numerous challenges.

• The exergy and anergy concepts and their relation with energy.
• Applications of exergy and energy balances to closed or open systems, housing or not internal reactive processes : heat engines, refrigeration machines, heat exchangers, internal combustion engines, HVAC systems;
• Exergy efficiency and effectiveness of energy systems;
Exergy analysis on CSP backed up by TES

- Electrical AC grids.
- Electrical DC grids.
- Electrical safety measurements.
- Autonomous energy grid (smartgrid) sizing and control.