Automated visual inspections for final assembly : A case study of cab assembly at Scania Oskarshamn
Abstract: Quality inspections have seen varying degrees of automation depending on the complexity of the task and the environment. Especially in later phases of multi-stage manufacturing processes, such as final assembly in automotive industries, quality inspections are largely manual to this day. Today, emerging technologies offer both pressures and tools to increase automation. However, the current state of the research field is lacking in studies that help guide companies toward implementation. Thus, quality managers at final assembly for Scania's truck coachwork factory in Oskarshamn (MC) stipulated a thesis assignment to explore how inspections in their final assembly workshop could be automated. This assignment constitutes the purpose of this thesis project - to provide an exploratory study into existing and emerging technologies that enable automation of quality inspections at MC. This was eventually delimited to exploring automated visual inspection technologies. In order to better understand Scania's inspection and manufacturing system, a series of interviews and shadowings were undertaken with appropriate respondents. From these, we were able to extract seven inspection system requirements, most important were the ability to (1) handle high variability, (2) add new inspections fast, (3) inspect in direct flow and (4) inspect inside and outside of the truck coach without disassembly. Then, a thorough and comprehensive review of 559 active inspections allowed us to categorize and map the nature of inspections at MC. In our literature review, a model for a general quality inspections was found, which was used to guide and ground our proposals and recommendations as well as provided intuitive illustration. Further, two paradigms emerged as most interesting for this project: machine vision and deep learning. A theoretical comparison of the two suggested that the more traditional, rule-based machine vision algorithms would struggle in accommodating the requirements previously found. However, we could infer that deep learning would be highly suitable with respect to MC's requirements and inspections. A prototype deep learning inspection system gave further validation toward our speculations that deep learning offered the greatest potential for automation in complex environments such as MC's. Although this thesis was created for Scania as a primary customer, important theoretical and practical contributions were developed for a more general audience. Firstly, the exploration into new avenues for automation that overcome their traditional limitations were provided; something that is of high current import given the trends toward more complex manufacturing settings. Practically, we provide some guidance to industries that find themselves in similar situations to Scania - employing complex manufacturing systems or having complex products - where our findings can give insights in regards to modern automation challenges and solutions.
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