The advent of the industrial revolution has brought a great number of changes in the functioning of various processes in manufacturing industries. The ways and means of working have evolved exponentially with the implementation of advanced technology. Moreover, with the increasing technology, the customer demands have also been varying dynamically due to changes in customer requirements focusing on individual customization. To cope with the dynamic demand, manufacturing industries had to make sure their products are manufactured with higher quality and shorter lead times. Implementation and efficient usage of technology has provided industries with the necessary tools to achieve market demand and stay competitive by growing continuously. The transformation aims to reach the level of zero-defect manufacturing and ensure higher first-time right yield capability with minimum utilization of available resources. However, technological advancements have not developed the quality inspection process of the manufacturing industry at the same level as other processes. Due to this, the quality inspection processes are still human dependent which requires a highly skilled human operator to perform inspection procedures using sensory abilities to detect deviations. Research suggests that human quality inspection is prone to errors due to fatigue as the process is continuous, strenuous, and tedious work. The efficiency of human inspection is around 80% which becomes a chronic problem in safety-critical and high-value manufacturing environments. Moreover, with the increasing level of customization and technology, the products are becoming more complex with intricate shapes and only human inspection is not enough to meet the customer requirements. Especially in the case of automotive industry in Body in White applications, human inspection of outer body panels, engine parts with tighter tolerances alone does not make the cut. Advancements in the field of metrology have led to the introduction of Coordinate measuring machines (CMM), which are classified as contact and non-contact measuring machines. The measurements are performed offline away from the production line, using the sampling method. The contact measuring machines are equipped with touch trigger probe devices that travel all over the part to make a virtual image of the product which is time-consuming but accurate. Whereas the noncontact measuring machines are equipped with laser scanners or optical devices which scan the part and develop a virtual model which is fast but has accuracy and repeatability issues due to external factors. But coordinate measuring machines have proven to be bottlenecks as they were not able to synchronize with the production pace and could not perform aninspection on all the produced parts, which would help in collecting data. The gathered data can be used to analyse root causes and generate trends in defect detection. With the advancements in non-contact measuring systems, automotive industries have also realized the potential of implementing inline measurement techniques to perform quality inspection. The non-contact measuring system consists of a robotic arm or setup which is equipped with a camera, sensors, and a complex algorithm to identify defects. This provides the robotic arm with machine vision which is works by taking a series of images of the product from various and process these images to detect deviations using digital image processing techniques. The inline measurement has proven to be accurate, fast, and repeatable to be implemented in synchronization with the production line. Further, the automotive industries are moving towards hybrid inspection systems which capitalize on the measuring speed of the robot and the fast decision-making ability of human senses.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mdh-56038 |
| Date | January 2021 |
| Creators | Avvari, Ddanukash |
| Publisher | Mälardalens högskola, Akademin för innovation, design och teknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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