Automating Component Inspection within a High-Mix, Short-Cycle Production Environment



The safety, performance, and reliability of modern automobiles depend upon a long list of mechanical components. These elements, which include the powertrain and linkages and suspension must be precision manufactured—typically in high-volume/short-cycle production lines. A global manufacturer of automotive components approached DarwinAI to automate manual inspection.

The Problem

The existing quality control process, part of a high-volume production environment with cycle times under 20 seconds, required 15 full-time employees (FTEs).

DarwinAI developed and deployed a vision-based AI solution that assists human inspectors by automatically detecting and distinguishing between a range of defects.

The Results

to deploy
3 months
inspection time
2.5 seconds
fewer false positives/scrap

Characteristics of the existing inspection process

  • High volume
  • Manually intensive
  • Short cycle time

Traditional alternatives fall short

The manufacturer recognized that the 20-second cycle time was far too fast for traditional automated vision inspection solutions—and building a proprietary solution in-house was a non-starter because:

  • Developing such systems is time consuming and often requires specialized expertise
  • Most AI solutions on the market depend upon large volumes of training data to become reliable—often exceeding what is available or economical

DarwinAI offered a compelling alternative: a fast, cost-effective, and—above all—reliable solution that required significantly less training data.

DarwinAI’s Solution

In only three months, DarwinAI developed a purpose-built, AI-driven vision inspection solution that:

  • Automated inspection in 2.5 seconds, preserving production volumes
  • Reduced false positives by 50% compared to manual inspection
  • Enabled reallocation of 60% of FTEs to value-add tasks
  • Included built-in governance capabilities for quality control and safety

The flexibility of DarwinAI’s technology ensured straightforward deployment and operation by:

  • Integrating with existing systems  including a mounted camera and robotic arm, minimizing CapEx
  • Requiring only a few data samples of defects to get started—a fraction of what traditional systems demand
  • Leveraging human-in-the-loop operation to quickly achieve production-level performance accuracy

Ultimately, by automating high-volume, high-mix quality control of machined parts, DarwinAI’s vision inspection system delivered a return on investment within one year.

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