Computer Vision to Drive Quality in Automotive Manufacturing

Background

About Project

Our client, a leading automotive manufacturer, was struggling with quality issues. We collaborated to implement an AI-powered computer vision system with the capability to perform automated quality control. The aim was to achieve high product quality, and more efficiency while reducing time, costs, and other resource utilization associated with manual inspection processes.

Intelligent Watch over Automotive Quality For Complete Assurance

As a leading manufacturer in automotive, our client maintained a super high-volume product line. However, this brought some big challenges in maintaining quality standards. Moreover, till now, they have been relying on manual inspection processes. But this had issues of its own. Not only was it time-consuming, struggling to keep pace with increasing production demands, but also prone to human error again affecting the quality of the products. They needed a mechanized solution that could automate their quality control processes while bringing consistency and reliability to the process and the product.

Background

Automotive Quality Excellence With Smart Computer Vision

01

Project Requirements

We needed to develop an AI-enabled computer vision system that could accurately detect defects in real-time. This needed the integration of computer vision algorithms and deep learning models specialized for the automotive domain.

02

Project Execution

Our team implemented state-of-the-art computer vision techniques, such as object detection, segmentation, and anomaly detection along with data augmentation and synthetic data generation. We optimized models for real-time inference using edge computing and parallel processing.

03

Project Delivery

All of this resulted in high accuracy in defect detection and real-time quality control with low latency. Our solution not only integrated well without disrupting production but could scale with increasing demands and future growth.

Background

Driving Automotive Excellence with AI-Powered Quality Control

Our efforts paid off in the form of significantly improved product quality. The quality-controlling process led to consistent, reliable, and scalable defect detection. We were successful in virtually vanishing the manual errors and the costs associated with it and with rework and warranty claims. Result: high customer satisfaction and brand reputation.

    92%

    reduction in defects missed during quality inspection

    38%

    reduction in defects missed during quality inspection

    27%

    reduction in defects missed during quality inspection

    19%

    higher customer satisfaction ratings for vehicle quality

    How Did We Do it?

    • Requirement Gathering

      We collaborated with the client's quality assurance teams to understand their specific needs and inspection criteria.

    • Ideation

      Then, we explored various computer vision techniques to develop the right solution to meet those needs and criteria.

    • Designing

      In the designing phase, we charted out the AI architecture and planned its seamless integration on the production lines.

    • Development

      During development, we worked on custom deep learning models and computer vision algorithms.

    • Deployment

      Lastly, the AI vision system was deployed into existing production lines with little to no disruptions.

    Hey B

    Project Challenges

    01

    Data Acquisition and Labeling

    The first challenge was to obtain a diverse and representative dataset of automotive defects so that the AI models could be trained. For this, we make use of techniques like data augmentation and synthetic data generation to expand the training dataset.

    02

    System Integration and Scalability

    The second challenge was to integrate the computer vision system into the existing production lines while ensuring scalability but without disruption. For this, our team took a modular approach and resorted to cloud computing resources to succeed on both fronts.

    03

    Real-time Performance

    Another challenge was to achieve the capability of real-time defect detection without compromising on accuracy. We optimized the computer vision system for low latency inference and utilized edge computing capabilities to overcome this challenge.

    Client Testimonal

    We never thought it was possible but the Hey Buddy team was confident about it. Their innovative approach to automated defect detection has significantly improved our product quality. There has been a drastic reduction in rework and warranty claims, which saved us a lot of money. And the obvious benefit of increased efficiency and consistency of the AI vision system.

    Power Your Product Quality with AI Vision Solutions

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