| 1. |
- Salunkhe, Omkar, 1990, et al.
(författare)
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Assembly 4.0: Wheel Hub Nut Assembly Using a Cobot
- 2019
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Ingår i: IFAC-PapersOnLine. - : Elsevier BV. - 2405-8963. ; 52:13, s. 1632-1637
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Konferensbidrag (refereegranskat)abstract
- To achieve a flexible and adaptable assembly system (assembly 4.0) a combination of enabling resources and technologies are required. Collaborative robots (Cobots) are one such technology that can offer higher flexibility and quick adaptability in assembly systems. Cobots are becoming more common in the manufacturing industry, the use and application of cobots are constantly growing. Combining cobots with IIoT gives the possibilities to also communicate with cobots and employees to achieve an effective assembly system. This paper presents a design research experiment conducted using cobots in a lab environment. The experiment studies the use of cobots in a final assembly environment with the focus on testing feasibility, improving quality and ergonomics of a real industrial operation. The experiment setup is presented in detail and the results are discussed along with future research directions.
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| 2. |
- Wedin, Kevin, et al.
(författare)
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Automating nut tightening using Machine Learning
- 2020
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Ingår i: IFAC-PapersOnLine. - : Elsevier BV. - 2405-8963. ; 53
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Konferensbidrag (refereegranskat)abstract
- 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.
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