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Brad Ward, a technical manager at Omron Inspection Group, leads application engineering teams responsible for developing inspection solutions and advanced sensing and traceability technology. These dedicated application engineers, as technology experts, collaborate with customers to understand their unique needs and challenges. Their goal is to identify proven or innovative solutions that bring value to the manufacturing process.
Safety is of paramount importance in the automotive industry, and this principle holds true for electric vehicle (EV) manufacturing as well. However, the increasing complexity of modern vehicles, particularly EVs, poses challenges for traditional sample or auditbased inspection methods. As a result, manufacturers are seeking innovative solutions to ensure safety without compromising efficiency. Inspection Technology Advancements The development of new and improved inspection equipment plays a crucial role in controlling and enhancing quality in this dynamic environment. These advancements facilitate the reliable and seamless integration of various components into the final vehicle. Key features such as automated, quick program development, AI-assisted decision making, and root-cause analysis capabilities have become invaluable tools for emerging companies facing resource and skill-level challenges. Today, inspection programs can be created with higher capabilities, stability, and efficiency in significantly less time, even with less experienced operators. The ultimate goal for manufacturers to adopt inspection solution is to achieve zero downtime, zero PCB design constraints, zero operator and programming time, zero false-failures, and achieving zero manufacturing defects. AI Integration for Inspection The recent trend is to embraced artificial intelligence (AI) in inspection processes, prioritizing its use to enhance inspection capabilities and approaches. By leveraging AI, we can improves inspection quality as its primary directive. For instance, in automated optical inspection (AOI), AI-assisted image processing techniques can be adopted. These techniques combine proprietary 3D measurement and color-based data to create unique profiles for every part and solder joint. This approach addresses the most challenging and complex AOI application: solder joint inspection. Furthermore, AI tools are employed in AOI to enhance component feature identification, character inspection (OCR), and deep-learning-based AI engines. This deep-learning AI engine learns from known good and defect data over definable epoch periods. It generates an AI-based inspection test, complementing the standard IPC-610 inspection approach. This advanced AI capability proves invaluable for detecting difficult-to-spot defects or handling parts with unstable processes. With this AI integration, it’s possible for manufacturers achieve an impressive efficacy rate of 99%, significantly reducing the chance of false-failures or defects escaping detection. Similar to AOI, AI can be leveraged in automated X-ray inspection (AXI). This includes utilizing AI to support critical applications like BGA inspection, one of the most commonly used 3D computed tomography AXI. By automatically adjusting X-ray settings through trial and error, AI optimizes contrast in X-ray imaging, improving inspection stability and defect detection capability. Additionally, AI helps predict X-ray exposure dosage, estimate cycle time, and intelligently filter noise, further enhancing AXI performance. Unique Approach to False-Failures While many inspection solution providers focus on automatically determining false-failures and removing them from the results for operator review, it is crucial to remember that addressing falsefailures is vital for building confidence in the inspection process and improving program strength. We can harness AI to strengthen and refine inspection programs to avoid false-failures altogether. By adopting this approach, defects are accurately detected while maintaining program stability and reliability. Inspection Technology for EV Manufacturing In the past, certain inspections relied on audit or sample-based approaches. However, as specifications and tolerances increase, every part now requires thorough inspection. For instance, advanced driver-assistance systems (ADAS), cameras, and adaptive headlights necessitate precise positioning of CMOS sensors and LEDs in relation to board-level features or tooling holes. Advanced inspection systems should be capable of inspecting a wide range of parameters, including inner/outer assembly diameter, pin positioning, distance, parallelism, height, creeping distance, welding distance, height, and volume. By achieving a proper balance of speed and accuracy, ensures that EV manufacturers can meet the rigorous quality standards demanded by the industry.