Detecting Silicon Gasket Compression Set Failure with AI-Powered Visual Inspection

The Problem: Why Compression Set Failures Slip Through Quality Control

Silicon gaskets are critical sealing components in automotive, aerospace, medical devices, and industrial equipment. When these gaskets develop compression set failure, they lose their ability to return to original thickness after prolonged stress—compromising seal integrity and leading to costly field failures.

Common Defects Associated with Silicon Gasket Compression Set Failure:

• Permanent deformation – Gasket remains compressed and fails to recover original dimensions
• Surface cracking – Micro-cracks develop from material fatigue and elastomer breakdown
• Hardening or brittleness – Loss of elastic properties due to thermal or chemical degradation
• Uneven thickness distribution – Inconsistent compression across the sealing surface
• Surface glazing – Shiny, hardened areas indicating localized over-compression
• Edge deformation – Rolled or flattened edges that compromise sealing contact

Manual inspection of silicon gaskets presents significant challenges. Human inspectors struggle to detect subtle dimensional changes and early-stage surface degradation, especially at production speeds exceeding hundreds of parts per hour.

Fatigue-related inconsistency compounds the problem—what one inspector flags as marginal, another may pass as acceptable.

The Solution: Machine Vision and Deep Learning

AI-powered visual inspection eliminates the subjectivity and fatigue inherent in manual quality control. Deep learning models can be trained to recognize the subtle visual signatures of compression set failure that human eyes often miss, including microscopic surface changes and dimensional variations measured in fractions of a millimeter.

Overview.ai's approach delivers consistent, objective inspection at full line speed. The system evaluates every gasket against learned quality standards, ensuring zero sampling gaps and complete traceability across production runs.

Step 1: Imaging Setup

Begin by placing a representative silicon gasket with known compression set characteristics under the OV80i camera system. Proper lighting is essential—angled illumination often reveals surface texture changes and subtle deformation patterns that indicate compression failure.

Click "Configure Imaging" to access the Camera Settings panel. Adjust exposure and gain settings until surface details, edge definition, and any glazing or cracking become clearly visible in the preview.

Click "Save" to lock in your optimized imaging configuration.

Step 2: Image Alignment

Navigate to the "Template Image" section within the configuration interface. Capture a template image of a correctly positioned gasket that will serve as your alignment reference.

Click "+ Rectangle" to add an alignment region around the main body of the gasket. This ensures consistent positioning regardless of how parts arrive on the conveyor.

Set "Rotation Range" to 20 degrees to accommodate normal variation in part orientation during inline inspection.

Step 3: Inspection Region Selection

Navigate to "Inspection Setup" to define where the system should focus its analysis. Rename your "Inspection Types" with descriptive labels such as "Compression_Deformation," "Surface_Cracking," or "Edge_Integrity."

Click "+ Add Inspection Region" for each critical area requiring evaluation. Resize the yellow bounding box to cover the sealing surfaces, edges, and any zones prone to compression set damage.

Click "Save" after defining all inspection regions.

Step 4: Labeling Data

The human-in-the-loop labeling process teaches the AI to distinguish acceptable gaskets from those exhibiting compression set failure. Subject matter experts review captured images and categorize each as Good or Bad based on quality specifications.

Include representative samples across the full range of acceptable variation. Equally important: label known failure modes including severe compression set, cracking, and edge deformation to build robust detection capability.

Step 5: Creating Rules

Configure pass/fail logic based on your defined Inspection Types. Set thresholds that trigger rejection when the AI detects compression set indicators above acceptable limits.

Gate automated acceptance on the line by linking inspection results to reject mechanisms or diverter systems. This ensures only conforming gaskets proceed to assembly or shipping.

Key Outcomes & ROI

Implementing AI-powered inspection for silicon gasket compression set detection delivers measurable business impact:

• Reduced scrap and rework – Catch defective gaskets before they enter assembly, eliminating downstream waste
• Higher throughput – Inspect 100% of production at line speed without bottlenecking operations
• Compliance and traceability – Maintain complete inspection records for ISO, IATF, or FDA audit requirements
• Process improvement insights – Identify compression set trends linked to specific batches, suppliers, or environmental conditions

By automating compression set detection, manufacturers protect product quality, reduce warranty claims, and gain actionable data to continuously improve their sealing component processes.