Training objectives 

• Mastering the purposes of machine learning techniques
• Use and refine essential machine learning techniques to analyze and optimize the performance of production systems.

Applications 

• Optical process monitoring for Laser-Powder Bed Fusion (L-PBF) – use of SVM classification techniques, neural networks
• Quality management of production systems using classification techniques (analysis of the impact of measurement uncertainties)
• Development of a flexible tool/system for predictive maintenance—detection and diagnosis of process and machine deviations, etc.

Target audience 

This training course is aimed at all those involved in manufacturing processes and systems and related to overall performance: engineers and production managers in the manufacturing industry, quality engineers, manufacturing process and systems managers, etc.
The level of the training course is aimed at employees with knowledge of Python programming (a one-day refresher course is available).

Program 

Discovery and analysis of the purposes of machine learning techniques based on different scenarios.

1 - Estimation, estimator, bias, etc. (key statistical concepts).

2 - Scenarios for deploying machine learning techniques to improve the performance of production systems (illustrated with a case study).

• Identify the key parameters of my production system and my products
• Identify strategies for adjusting the production system
• Predicting Product Non-Compliance Rates
• Identify the causes of product non-compliance or process failure.

Data preprocessing

3 - Prepare data to make better use of it.

• Cleaning missing values
• Coding of non-numeric values
• Data transformation and scaling
• Dimensionality reduction; reducing the number of parameters based on their relevance
• Applications based on a case study – implementation with Python

Discovering and mastering association and classification techniques

4 - Reduce the volume and/or dimensions of data to be processed, extract adjustment rules.

• Dimension reduction and study of correlations between parameters: Principal component analysis, etc.
• Extracting rules that govern a dataset: Decision trees, Random Forest
• Logistic regression
• Applications based on a case study – implementation with Python

Discovering and mastering classification and clustering techniques

5 - Predict non-compliant/faulty production; Identify the causes of non-compliance or failure.

• Identification of similar data groups (e.g., production ranges): K – MEANS
• Prediction of non-compliance: KNN & SVM
• Applications based on a case study – implementation with Python

Discovering neural network design

6 - Predicting the performance of production systems.

• Basics of designing a neural network for classification and regression
• Improved predictions (by configuring network hyperparameters)
• Applications across multiple case studies – implementation in Python and TensorFlow

Teaching resources

The training is based on time dedicated to database cleaning techniques, followed by the practical deployment of a machine learning tool.

The deployment of concrete cases involving discovery and tool manipulation can be done using generic data provided by trainers.

Speakers

Practical information

Training location

Arts et Métiers Campus Arts et Métiers Metz - 4 rue Augustin Fresnel - 57070 Metz (building accessible to people with reduced mobility).

In the case of internal training within a company, the training may be relocated. 

Duration of training 

3 days

Dates

Next session: 

• Wednesday, April 23, 2025
• Monday, April 28, 2025
• Tuesday, May 6, 2025

Disability

If you have a disability of any kind and would like to take this course, please contact us.

Contact

John Fritsch, Continuing Education Manager.

Testimonial

Pascal Dietsch, R&D engineer and department manager at ArcelorMittal Research, looks back on the data science training he took at the Metz campus:

Training objectives

• Understanding the reverse engineering process
• Perform a non-contact 3D scan
• Processing a cloud of digitized points
• Use 3D reconstruction CAD tools
• Create a prototype using additive manufacturing
• Implement a dimensional inspection software solution

Applications

• Reverse engineering of parts or tools
• Re-manufacturing of a small series of parts
• Numerical simulation for competition analysis
• Integration with additive manufacturing
• Training and augmented reality

Sectors: mechanical engineering, healthcare, art and heritage, etc.

Target audience

The training can be aimed at CAD technicians or engineers, maintenance, manufacturing, quality, method, design, etc.

Program

The training is available in different formats:

• A one-day introductory course,
• A formula implemented and operated over two days,
• A comprehensive three-day program,
• A tailor-made formula to be defined.

Day 1 - Introduction - Beginner

1. The different 3D scanning techniques: choice of measuring equipment, advantages, limitations, etc.
2. Demonstration of various non-contact scanning technologies: GOM Atos Core and ScanTech (tool configuration: management, qualification, maintenance of measuring equipment, performance comparison), Alicona system.
3. Reverse engineering methodology applied to a specific case:

• Point cloud processing (GOM Inspect): cleaning the cloud, choosing a working reference point, reconstructing canonical shapes, taking into account the history of the scanned part, etc.
• Reconstruction tools in Catia or 3DExperience.
• Rapid manufacturing techniques.

Days 2 and 3 - Implementation and operation - intermediate to advanced

Implementation in an industrial case: 3D scanning and reverse engineering (1 day)

1. Based on an identified industrial case, selection and implementation of a non-contact 3D scanner.
2. Point cloud processing
3. Reverse engineering of the selected part on Catia V5 or 3Dexperience (CAD)
4. Preparing for 3D printing of the prototype

Exploitation of results (1 day)

1. 3D scanning of the printed prototype
2. Dimensional inspection of the prototype using GOM Inspect and comparison with the original part.

Teaching resources

The training takes place in person.

The theoretical contributions are supplemented by concrete use cases from the industrial sector. 

Knowledge is assessed through a questionnaire, and a training certificate is issued upon completion.

Speakers

The training is provided by Nicolas Bonnet and Olivier Bomont, certified teachers.

Practical information

Training location

Arts et Métiers Campus Arts et Métiers Metz - 4 rue Augustin Fresnel - 57070 Metz (building accessible to people with reduced mobility).

Duration of training

1 to 3 days depending on the package chosen.

Dates

Upcoming sessions: 

• Tuesday, April 22 - introductory session
• Tuesday, April 29 - Intermediate
• Tuesday, May 6 - advanced

Training can also be arranged on request by contacting the account manager.

Registration for sessions that are already scheduled is done via the online catalog.

Disability

If you have a disability of any kind and would like to take this course, please contact us.

Contact

John Fritsch, Continuing Education Manager.