Scania is a world-leading provider of transport solutions. Together with our partners and customers we are driving the shift towards a sustainable transport system. In 2018, we delivered 88,000 trucks, 8,500 buses as well as 12,800 industrial and marine engines to our customers. Net sales totalled to over SEK 137 billion, of which about 20 percent were services-related. Founded in 1891, Scania now operates in more than 100 countries and employs some 52,000 people. Research and development are concentrated in Sweden, with branches in Brazil and India. Production takes place in Europe, Latin America and Asia, with regional production centres in Africa, Asia and Eurasia. Scania is part of TRATON SE. For more information visit: www.scania.com.
Scania is now undergoing a transformation from being a supplier of trucks, buses and engines to a supplier of complete and sustainable transport solutions.
Automated quality inspection system as an autonomous system is an essential element for the future of smart manufacturing and Industry 4.0. Quality is core part of Scania’s systematic way of working. In Scania’s long term technology road map computer vision is identified as a key competence. Computer vision as a user-friendly, low-cost, efficient, non-contact and non-destructive technology has been widely applied automated quality inspection, especially single object and simple assembly quality inspection. However, for more complex processes which involve human activities such as vehicle assembly and logistics, there has not been much research and applications. Being able to apply automated quality inspection vehicle assembly and logistics would significantly improve product quality as well as productivity.
Scania is now announcing an industrial PhD position within the area of Global Industrial Development. As an industrial PhD, you are fully employed by Scania. A PhD study normally takes 5 years to conclude.
The aim of the project is to create an automated quality inspection system to improve the quality of complex production process with human involvement, such as vehicle assembly and logistic, based on computer vision.
Automatic recognition technologies enabled by deep learning and artificial intelligence are in rapid progress, however, how to create an autonomous systems and apply these advanced technologies in a real production, which is complex and involves a lot of human activities, remains as a big challenge for the industry.
The example research questions need to be answered will be:
– How to achieve different kinds of assembly quality inspection tasks within the takt time?
– How to develop an algorithm/software which can do image recognition without a large number of training dataset?
– How to build a system which can do object recognition when different objects are overlapping on each other?
The research will be done through use-cases based studies.
The current quality control systems in Scania in logistics and assembly are based on operator inspection, and the research is to improve this quality control system and reduce the need of manual power.
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