[AI-Driven Industrial Inspection Case on Automotive Assembly Line] Achieving "Zero" Defects! BangQi Technology Doubles Quality Inspection Efficiency in Auto Manufacturing

BangQi Technology as A full-stack AI-driven digital manufacturing solution provider Focusing on the fields of industrial digitalization and intelligent technologies, with a vision toward fully automated smart manufacturing factories, we are launching “ AI simulation, AI digital twins, AI-powered intelligent scheduling, the enterprise-level unmanned factory digital middleware system LDS, and ADS—a hybrid scheduling solution for multi-category robots. Leading product solutions designed to meet enterprise needs Intelligent requirements for equipment manufacturing and after-sales services, as well as digitalization needs for production lines. ， Helping businesses achieve Digital factory, production line digitalization, line-side digitalization, warehouse digitalization, and unmanned operations in campus logistics. Awaiting the digital and intelligent transformation and upgrade across all scenarios and processes.

1. Inefficient detection efficiency : Traditional manual quality inspection is inefficient and struggles to meet the demands of high-speed production lines, often leading to Production bottleneck , affecting production efficiency.

2. Insufficient detection accuracy In industries with high precision requirements, industrial defect detection typically encounters complex defect types and shapes. Manual quality inspection is often susceptible to factors like fatigue and lack of experience, leading to human errors. Missed detections and false positives , affecting product quality.

3. High labor costs In mass production, manual quality inspection requires significant labor input, while human errors in testing can also lead to... Rework or product recalls further increase costs. 。

4. Unstable quality : Due to the presence of manual quality checks Uncertainty , product quality is difficult to guarantee, leading to damage in brand reputation.

5. Quality Improvement Lacking Data Support : Traditional quality inspection methods It's challenging to collect and analyze quality inspection data in real time. , leading to difficulties for companies in accurately identifying quality issues in production and hindering their ability to effectively implement quality improvements and optimizations.

6. Addressing the Challenges of Flexible Manufacturing As manufacturing shifts toward flexible production, product varieties and production lines are changing frequently, making it difficult for traditional quality inspection methods to adapt and adjust quickly. Unable to meet diverse and personalized production needs 。

1. Data Collection and Annotation BangQi Technology collects vast amounts of sample data and, after annotating this data, categorizes it into: Common defect types in various car body components include incomplete welds, slag inclusions, shrinkage cavities, and particulate contamination. Subsequently, leveraging the company's independently developed AI digital training platform, combined with deep learning algorithms and computer vision technology, Customizing AI algorithm models to meet customer needs 。

2. Model Validation and Optimization : By continuous validation and optimization, we ensure high accuracy and stability of the AI model in real-world production environments. Integrate the proven AI quality inspection system into the customer's production line. , enabling seamless integration with existing production systems.

3. Work Flow : The quality inspection system works through Industrial Vision + AI Technology Capture product images and perform full-scale, real-time data analysis , subsequently leveraging a custom AI algorithm model to identify and classify various defects on the workpiece, Identify whether components are properly assembled, and check for any omissions or defects in processes like adhesive application, painting, and welding. The inspection results—including defect location, type, and severity—are then visualized via 3D model mapping, with the final output being a comprehensive quality control report.

4. Data Feedback and Storage : Based on the quality inspection results, customers promptly adjust their production processes, optimize quality management, and continuously enhance product quality. Meanwhile, all testing data Real-time storage , facilitating subsequent analysis and production optimization.

1. Enhance detection efficiency : Achieves fully automated 24/7 continuous operation, reducing the inspection time for a single workpiece from the original 20 seconds Cut down to 10 seconds ， Quality inspection speed has improved by 50% compared to before. , and each component undergoes high-precision inspection.

2. Enhance detection accuracy : The Poundflag AI quality inspection system boasts high precision and exceptional reliability, The false negative rate has been reduced to 0.01%, and the false positive rate is now at 1%. , significantly improving product quality.

3. Reduce labor costs ： The number of quality control personnel has been reduced to 10, cutting labor costs by 70%. , while also avoiding rework and product recalls caused by human inspection errors.

4. Enhance Data Utilization : Collect and analyze quality inspection data in real time, identify quality issues during the production process, provide improvement recommendations, and optimize production workflows. Reduced the production defect rate by 20%. 。