Minimizing assembly errors during mass production process in automotive industry by using visual processing methods

In the automotive industry, when assembly tracking is done manually, a large number of assembly errors occur in mass production lines. This leads to production delays, cost increases and potential safety concerns. That is why, current quality control methods might not be sufficient to detect and prevent these faults efficiently. Improvements on the serial production line are crucial to increase production quality, operational efficiency and minimize faults during assembly. Recently, the rapid advancement of visual processing technology offers an effective method to detect and minimize potential errors in mass production lines. The main purpose of this study is to analyze the emerging visual processing technologies and to specify alternative methods to the existing quality control systems in the automotive industry. The most appropriate visual processing methods will be identified to detect differences and ensure that mass production proceeds in line with the prototype vehicle. Additionally, it aims to reduce human related deficiencies by decreasing human dependency throughout the assembly period. The study will be performed on the Central Tire Inflation System (CTIS) of Derman Drops 8x8, a military container carrier vehicle. CTIS consist of air regulator, wheel valves, electrical control units (ECU), pneumatic control units (PCU) and HMI (Human Machine Interface) and it is one of the most complex subsystems of the vehicles. The success of this study will lead to its applicability to other subsystems. In conclusion, this study focuses on implementing visual processing methods in the automotive assembly line to identify possible assembly faults between prototype and serial production vehicles. In the context of the automotive industry, the potential impact of this research is substantial. Minimizing assembly errors not only ensures the delivery of high quality vehicles to consumers but also contributes to the sector's competitiveness on a global scale.