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Big Data, Machine Learning and Sensors
Module developed by ingenjör4.0

Big Data, Machine Learning and Sensors

Starting date: Autumn 2023
Price: 1 995 SEK ex. vat

This module covers main aspects of Big Data and AI in manufacturing processes, beginning from machin-ing operations and ending with production.


The module on “Big data, Machine learning and Sensors” covers main aspects of Big Data (BD) and AI in manufacturing processes, beginning from machining operations and ending with production quality control and production costs analysis. The module includes the brief introduction to machining operations, more detailed consideration of machining dynamics and its influence on the process and production quality, monitoring the process parameters, sensors and data acquisition systems. A particular attention is given to Big Data analytics, data processing and data organization, development of Machine Learning (ML) algorithms and their implementation in manufacturing processes.

The schedule
Starting date: Autumn 2023
TBD — 1st Webinar – “Module kick-off”
TBD — 2nd Webinar – “Introduction to the multi-layer perceptron (MLP)”
TBD — 3rd Webinar – “Data Processing and Visualization”
TBD — 4th Webinar – “Image Processing and NN”
TBD — 5th Webinar – “Signal Processing and ML”
Learning objectives
Achieve a deeper understanding and competence in machining operations with focus on machining processes, machining dynamics and surface metrology (production quality).
Achieve a deeper understanding of the role of monitoring and measured data for strengthening the production sustainability.
Obtain knowledges on the data collection and analysis with focus on the Big Data (BD) problem on manufacturing level.
Obtain knowledge and skills of use of Machine Learning (ML) means to solve Big Data (BD) analytics problems in manufacturing.
Obtain practical skills though implementation of AI methods in prediction, classification and optimization of production processes with focus on cutting processes, machining dynamics and surface quality analysis.
Module structure

The module is a mixture of self-study, webinars, and cross-company exercises.

Time commitment

To complete this module, the participant is expected to schedule approximately 4 hours per week over 5 weeks (20 hours in total).


Please note that a minimum of 10 participants is required for the module to start.

After applying to the program, please secure dates in your calendar to be able to join planned webinars and assure time available required for learning.

A unique module created in co-operation between academic partners
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