Automating nut tightening using Machine Learning
Paper in proceeding, 2020

At the Volvo Truck assembly plant the repetitive task of nut tightening is not ideal regarding quality and ergonomic. The solution to both these issues would be to significantly increase the level of automation. However, automating this specific station requires solutions to two specific problems. The first problem is to find and identify what nuts that need to be tightened, since they are not always on the same position for this highly customized product. The second problem is that the automated solution needs to accommodate the working space which is a moving assembly line with human operators. This paper investigates how these two problems ban be solved using machine learning and collaborative robots. A realistic mockup of the assembly station has been created at Stena Industry Innovation Laboratory (SII-Lab) where all the testing has been done. The problem to identify the nuts to tighten is further complicated by the fact that some nuts are placed backwards for future further assembly which must be avoided. Therefore, the selected solution is to use supervised machine learning for object recognition. This way, the system can be trained to recognize both nuts that need to be tightened and those mounted backwards, and possible other objects needed. Tests have been conducted with different types of CNN (Convolutional Neural Network) algorithms. Results have been very successful, and the test setup has successfully managed to connect the whole task of identifying the correct nuts and move the collaborative robot to that specific position.

collaborative robot applications

Machine learning




Kevin Wedin

Student at Chalmers

Christoffer Johnsson

Student at Chalmers

Magnus Åkerman

Chalmers, Industrial and Materials Science, Production Systems

Åsa Fasth Berglund

Chalmers, Industrial and Materials Science, Production Systems

Viktor Bengtsson

Per-Anders Alveflo

Volvo Group


24058963 (eISSN)

virtual IFAC World Congress
Berlin (virtual), Germany,

Demonstrating and testing smart digitalisation for sustainable human-centred automation in production

VINNOVA (2017-02244), 2017-05-15 -- 2020-03-09.

Subject Categories

Mechanical Engineering

Computer and Information Science

Areas of Advance




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