Key Takeaways
Here's how machine vision systems can benefit automotive production:
- Defect detection: Find subtle flaws like soldering defects and misalignments, reducing waste and improving quality.
- Assembly verification: Ensure components like connectors and boards are correctly placed during production.
- Flexibility: AI-powered systems learn from examples, easily adapting to new variables, products, and specifications
- Cost savings: Reduce scrap rates and prevent costly rework or recalls.
Machine vision systems can play an important role in completing challenging tasks – keeping costs down and quality up even as automotive electrical systems get more complex. Machine vision can be organized by what their systems can do with six practical categories tailored to meet needs:
- Assembly Verification: Ensure every component fits perfectly. Machine vision systems check product assembly step-by-step. For example, they confirm that connectors, boards, or fasteners are properly in place during production.
- Classification: Precision starts with categorization. AI machine vision systems categorize objects or defects based on predefined criteria. This is ideal for sorting products or identifying subtle differences in materials that require specific handling or processing.
- Defect Detection: Reduce errors before they disrupt operations. Advanced defect detection identifies defects like surface scratches, soldering flaws, or alignment errors. By catching issues early, these systems improve product quality and reduce scrap costs.
- Measurement: Build precision with accurate measurements at scale. From verifying dimensions of sheet metal, to ensuring exact tolerances in intricate components, vision systems deliver reliable results for production.
- Barcode Reading and OCR: Tracking and traceability are made simple. These systems read barcodes and perform OCR (optical character recognition) to correctly identify products throughout the supply chain. Fast and reliable.
- Robotic Guidance: Bring precision to automation. Machine vision guides robotic arms for tasks like assembling delicate components or sorting products. This minimizes manual intervention efficiently.
By 2030, 50% of a vehicle’s cost is expected to come from electronic and safety components. One factor driving that increase is that automakers are integrating many more advanced features and electronics into their vehicles to meet customer demands. That includes critical safety features like advanced driver-assistance systems (ADAS) that provide features including lane departure warnings, adaptive cruise control, infotainment systems, and automatic emergency braking.
In addition to ADAS, the transition toward EVs, combined with rising demand for driver entertainment, information, autonomous driving, and connected car systems, means vehicles are filling up with more chips, circuit boards, wires, pins, and connectors. To ensure safety and consumer satisfaction, these components need to be inspected – leading to increased production costs and challenges.
Advanced features such as driver entertainment and information systems mean cars need more electronics.
As electronic systems continue to evolve, costly manufacturing challenges evolve alongside them. Electronic components are shrinking, making inspection much more difficult for human or machine vision. Defects come in many different shapes and sizes. That makes spotting specific problems like solder joint defects, misalignments, and surface flaws increasingly more difficult.
To solve these challenges, companies need adaptable, powerful machine vision.
“This vision system provides accurate and reliable readings, essential for our production lines. The system will better support new product introduction as it is more flexible than before. Based on this improved flexibility it is predicted that there will be a significant reduction in changeover time during future new product introduction."
Cost savings and time-to-market improvements
When machine vision solutions are put into production, they can quickly detect defects and identify parts in challenging environments. Integrating machine vision can also boost time-to-market by optimizing production in multiple ways:
Reducing scrap rates
Machine vision systems help make sure defective parts are truly defective – decreasing material waste and saving good parts from being falsely scrapped. This is a huge perk, because defects passing through production leads to more waste build-up – which then leads to more money spent to correct the issue.
For example, soldering defects on electronic control unit hardware can vary in appearance and resemble acceptable anomalies. Weld defects can be challenging to determine, such as undercut edges, incorrect sizing, etc. – making inspection too difficult for rule-based machine vision. When components and raw materials are expensive and hard to come by, scrapping a good part accidentally results in increased costs.
Cognex machine vision systems use deep learning, an AI technology, to distinguish functional flaws from acceptable anomalies. The solution learns what’s acceptable by analyzing labeled good and bad images, helping reduce development time. With adaptive algorithms, deep learning models are continuously learning, identifying new defects, adjusting to new variables, and challenging environments. This includes product positioning, changes to lighting, and focusing on specific product features for defect detection.
Quality assurance
By catching defects early in the process, machine vision systems also reduce the number of faulty products passing through to assembly. This ensures high-quality products are passing through production, as well as reducing time-consuming rework or recalls.
For example, machine vision systems can inspect weld seams for defects such as cracks or porosity – making sure standards are met before vehicles proceed to final assembly. Detecting these defects early prevents costly rework and potential safety issues. It’s important to catch this type of defect before defects are assembled into larger systems and fixes or replacements become even more complex and expensive.
How can Cognex help automakers?
Cognex delivers the hardware, software, and support automakers need to maximize throughput, efficiency, and scalability, and backs it all with a global network of application engineers. Cognex vision systems are flexible enough to adapt to changing specifications, configurations, and other variables.
With decades of automotive manufacturing experience working alongside customers such as BMW, Nissan, KIA, Continental, and Schneider Electric, a global support network, and flexible solutions, Cognex helps automakers streamline deployments, solve new challenges, increase quality, reduce costs, and achieve total traceability.