The 4 new Education Modules C5-C8

A summary

Of the four modules below , three started to be developed spring 2019 (C5, C7 and C8) by Chalmers, örebro University and Halmstad University (C5) Lund and Halmstad University (C7) & Luleå University together with KTH.  Module C6 started after an extended call to be developed in March 2020 by Chalmers University.   Each cosnsist of 3 sub-modules of 1HEC each.  The modules are also made in co-operation with "Partner" Universities and "Partner" Industries to promote a mix of recognised experts and organisations contributing to the top of the range education aimed for.  (Partner Industries and Universities may change during the final implementations of the course modules).

C5 Additive Manufacturing

Module Summary

This course module aims to establish basic understanding regarding additive manufacturing (AM) as the industrial method for prototyping and manufacture of advanced parts.  The module will also introuduce basic principles of seven groups of AM technologies, their possibilities and limitations,  applications and future perspectives.  The module finally introduce quality aspects of the process and resulting manufactured components by addressing geometrical tolerancing, dimensions, roughness, material and defect detection by material tomography.  A set of hand-on exercises and report "hand-in! will accompany the module. 

The Sub-modules

The three sub-modules cover a broad area from the Introduction and applications over the different AM processes available and quality issues.

Summaries and learning outcomes (under development)

Each sub-module have a set of Learning outcomes attached to visualise the expected outcomes in a clear way.  Below is a summary of the sub-module content and the associated learning outcomes.

C6 Human Robot Collaboration

Module Summary

The number of installed industrial robots in production systems are increasing with around 15 percent per year. Even though technologies for safe interaction between human and robots have been around for quite some time, only around 3 percent of the newly installed robots can be used for collaborative tasks. However, it is believed that Human Robot Collaboration (HRC) will be more common in the future.
When installing a robot without physical fencing, it is not only the robot arm that needs to be considered, the company needs to have a strategy for implementation and maintaining the robot station. This includes material handling, other components in the cell such as grippers, fixtures, vision systems and the interaction and collaboration with the robot and operators. Designing a robot workstation with collaborative tasks can be done both virtual and in real life, in order to test and evaluate different solutions in terms of flexibility, ergonomics, and task allocation. The aim of the course is to gain understanding of the abilities of the industrial robots that can be used for collaborative tasks. Furthermore, the students will be given tools and methods for analyzing the product design, workstation design and robot application in terms of safety, flexibility, productivity, and ergonomics. The course has six Learning Outcomes (LO) divided into the three sub-modules.

The Sub-modules

The three sub-modules cover components in a Human robot cell, Design methods as well as investments.

Summaries and learning outcomes (under development)

Each sub-module have a set of Learning outcomes attached to visualise the expected outcomes in a clear way.  Below is a summary of the sub-module content and the associated learning outcomes.

C7 Big Data, Machine Learning and Sensors

Module Summary

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) models and their implementation in manufacturing processes.

The Sub-modules

The three sub-modules cover Big Data in Manufacturing, Machine Learning and Industrial applications.

Summaries and learning outcomes (under development)

Each sub-module have a set of Learning outcomes attached to visualise the expected outcomes in a clear way.  Below is a summary of the sub-module content and the associated learning outcomes.

C8 Management of Manufacturing Digitalization

Module Summary

The digital technology can thus be seen as an enabler, while the organizations need to manage the technology and its development and implementation to get the most benefits out of it. The organizational aspects span over many fields, from the company’s role in the supply chain, management and control of internal and external processes, communication, systems integration skills and the changing role of the operator. At the same time as the industry  is facing a technological development jump through the introduction of internet-based system solutions, Swedish industry has also had difficulties recruiting skilled labour, and it is particularly difficult to recruit young people and women.  The new technology also sets new demands on the work force already in the industry, mainly through changing qualification requirements. Thus, it is both necessary to offer workplaces that can attract young people to industry, and at the same time develop the skills that the work force active in industry already has.  This module gives an overview on management aspects affecting companies implementing Industry 4.0. It aims to provide nuanced views of the opportunities and challenges of managing manufacturing digitalization. The module focuses on the management aspects of technology implementation, and how it affects the organization, the work environment and business models.

The Sub-modules

The three sub-modules cover Automations and Robots in Industry 4.0.

Summaries and learning outcomes (under development)

Each sub-module have a set of Learning outcomes attached to visualise the expected outcomes in a clear way.  Below is a summary of the sub-module content and the associated learning outcomes.