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End Product Quality Control in the Automotive Industry

Businesses have begun to focus on zero-defect efforts in order to reduce production costs due to increasing competition in today's difficult global trade conditions. With the help of technological developments in recent years, businesses have begun to use artificial intelligence methods more widely to solve complex problems. These methods, which companies in the automotive industry have started using to operate their business processes with zero errors, effectively support control mechanisms in production processes and problem solving.

Quality improvement with artificial intelligence in the automotive industry

Artificial intelligence aims to design a more stable and error-free production process by minimizing errors in operators' decision-making mechanisms in the automotive industry. Each company needs to determine its own quality level and improve it. For this purpose, there are numerous benefits to realizing quality and process improvements in businesses with artificial intelligence systems. Artificial intelligence algorithms should supplement statistical processes in quality and process improvement. The outcomes of quality decisions are quite complex and problematic. Simultaneously achieving clear customer satisfaction and minimizing costs is essential. Overly sensitive part control leads to the discarding of suitable products, resulting in increased sorting costs and decreased customer confidence. The aim is to reduce costs for the manufacturer, increase reliability in terms of quality, introduce a new error prevention method (Poka Yoke), and develop a zero-error methodology by minimizing the operator effect by using artificial intelligence systems in quality decision-making techniques.

During the final inspection of a finished product, some checks are carried out visually by the operator to prevent customer dissatisfaction, poor quality costs, and a lack of trust.

Important parameters in the surface control of products in the automotive industry

Related to cutting

• Rough Cut

• Dust Formation

• Burr Formation

Surface issues

• Trace

• Bubble

• Deformation/Scratch

• Burrs

• Crease

Welding Surface


Uneven Weld Surface

The customer notices and reports any inappropriate situation in at least one of these parameters as a complaint. In order to detect problems in all the above parameters, it is necessary to design a quality control system as part of the artificial intelligence study. In improper cutting situations, cutting defects, product dust residues, and burr surfaces occur on the finished product surface. Traces, bubbles, damage, improper cuts, and burrs remain on the surfaces of finished products. On the weld surface, pore formation, roughness, and surface cheek problems occur.

The most important goal of artificial intelligence work in these areas is to seek solutions to emerging problems using software or systematically. With these studies, in addition to making new designs using human intelligence, gains such as labor and time savings, increased security, and cost reduction can be achieved by reducing operator-related errors. The deep learning method in today's artificial intelligence technology enables the completion of many projects at a very low cost, even with industrial cameras, eliminating the need for expensive cameras.

Deep learning and artificial intelligence are two technologies that can be utilized in the automotive industry in order to achieve zero manufacturing defects through the use of the poka yoke method. Dataguess solutions can help you achieve this goal.


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