NEWS

Every step in the automotive manufacturing process requires defect detection. Uncovering defects helps improve the quality and safety of the finished product.

Because defect detection is labor-intensive, it is subjective and prone to errors. Therefore, improving the quality and safety of the finished product can be difficult.

Technology is taking on a vital role in enhancing the overall quality of the final product within the realm of automotive manufacturing by improving the effectiveness of the quality sort and containment process. Quality sorting and containment actively contributes to heightened safety standards for the components, thereby minimizing the risk of recalls or any injuries associated with vehicles.

Learn about the role of technology in improving quality sort and containment in automotive manufacturing.

Micro-Defect Detection System

A micro-defect detection system uses machine-vision cameras and controlled lighting to facilitate real-time pass/fail control. The artificial intelligence (AI)-based system integrated into the quality control station provides defect detection for automotive components manufacturing assembly operations. The functions can be remotely configured for centralized management of distributed operations. A micro-defect detection system reduces component supplier costs and production line downtime. It also maintains product quality and visibility to optimize the automotive manufacturing process.

Automotive Manufacturing Digitization

Digitization uses sensors to uncover problems, fix machinery, and redeploy resources without substantial delay in the automotive manufacturing process. The sensors also analyze energy consumption trends to create efficient energy management. Digitization promotes data capture from the entire manufacturing facility to support predictive maintenance. This connection of machines and workers reduces downtime due to machine defects or failure. The process also improves automotive quality management.

AI-Based Image Processing

AI-based image processing increases efficiency in production and quality control for the automotive industry. Machine learning integrates with sensors to improve the quality assurance processes. The technology detects and classifies defect images from images with distinct characteristics at different manufacturing and assembly levels. The images help automotive manufacturers quickly respond to product changes to increase quality assurance.

Quality Control System

A neural network-based quality control system detects defects and checks for configuration errors in seats, doors, control panels, engines, and other vehicle parts. This solution provides real-time comprehensive quality control of the production line and adjusts the production speed. The system also collects data relevant to the quality of each product at any location. This data supports lean analysis to uncover production bottlenecks and optimize workflows.

Lifecycle and Quality Assessment

Blockchain-based lifecycle and quality assessment assign a unique identification and a digital passport to each vehicle. This information helps automotive manufacturers manage and trace the lifecycle of the key components, use the Internet of Things (IoT) and devices to collect data and keep track of a vehicle’s condition, and access damage information and replacement records. This solution lets automotive manufacturers analyze the quality and authenticity of the auto parts.

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