Position detection and completeness check in automated production

Problem

In spark plug production, ensuring that every layer is correctly assembled before packaging is essential for maintaining product quality and avoiding downstream errors. In this example, the inspection area provided very limited installation space, allowing only the use of wide-angle optics. While necessary, this resulted in strong perspective distortion that traditional image-processing methods struggle to handle. Classical pattern search quickly reached its limits under these conditions, especially when the lighting varied or when plug positions appear shifted due to optical distortion.

Solution

To overcome these constraints, senswork introduced a deep learning–based inspection solution. Unlike traditional pattern-matching techniques, the neural network is capable of reliably interpreting distorted wide-angle images and identifying spark plug positions with high accuracy. The model was trained in a variety of lighting conditions and perspectives, enabling it to deliver stable and reliable results even when the visual environment changed. The system was integrated into the automated production workflow and performs real-time checks to ensure that each layer is complete before packaging continues.

Results

The solution successfully performs two critical tasks. First, it determines the exact position of each spark plug, ensuring alignment and correct placement. Second, it verifies completeness for every layer before packaging, preventing errors from propagating into later process steps. By replacing conventional vision techniques with deep learning, the production line gains a more resilient and dependable quality-control system that functions effectively even in mechanically and optically challenging environments.

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