In the realm of manufacturing, particularly in the electronics sector, the early detection of defective components is crucial for maintaining product quality and minimizing financial losses.
This blog delves into a case study highlighting the effectiveness of Cybord Aquila, a Visual AI platform, in identifying potential failures in electrical components. The case study is based on data from diverse customer lines and proves how Cybord Aquila’s AI based technology uses traditional root cause analysis methods.
Electrical component failure can have significant repercussions, including costly recalls and rework operations. Traditional analysis methods, while effective, often require extensive time and resources. Integrating the Visual AI platform Cybord’s Aquila offers a more efficient and accurate approach to identifying all defective components early in the manufacturing process by inspecting 100% of the electronic components.
Case Study Overview
The case study was conducted to showcase Cybord Aquila’s accuracy indetecting defective components in an SMT (Surface Mount Technology) assembly line. Standard root cause analyses were conducted alongside Cybord Aquila’s findings to validate its accuracy. The case study was conducted in collaboration with the customer, performing recall and rework calculations.
Cybord Aquila was used to detect damaged and contaminated components from the line, preventing the integration of these components into functional boards. The affected components were current limiters manufactured by MPS (MP5087AGG-Z), with a total of 15,000 contaminated component across three reels identified as contaminated.
Methodology
Cybord Aquila’s approach combines advanced visual AI analysis with standard root cause analysis. The platform employs sophisticated algorithms to scrutinize electrical components for any signs of defects, including contamination. This process is not only faster but also more precise compared to traditional methods.
Findings and Results
Cybord found contaminated leads on multiple components (image 1). The lab reports (EDStest and Cross section test) confirmed Cybord Aquila’s findings (image 2).
- EDS test: Contaminated leads including Silica and Carbon residuals.
- Cross section test: Non-soldered terminals due to contamination
The effectiveness of Cybord Aquila was evident in its ability to accurately identify the contaminated components. This prompt action enabled the avoidance of further integration of these defective components into the production line. The financial implications were significant.
Savings Analysis
The savings from using Cybord Aquila was calculated by considering both the rework and recall costs. The savings for rework took into account all expenses incurred in the process for handling suspected boards. In contrast, the recall savings encompassed the costs of recalling and replacing defective boards, including associated logistics and engineering expenses.
- Avoided Rework Cost: The immediate identification and removal of the defective components led to an avoidance of rework costs, estimated at $300,000.
- Avoided Recall Costs: More importantly, by preventing the integration of these defective components into final products, Cybord Aquila helped avert potential recall costs, which were calculated to be around $1.2 million.
The Impact of Cybord Aquila’s Visual AI in Electrical Component Analysis
This case study underscores the value of integrating advanced technologies like the Cybord Aquila Visual AI platform in electrical component failure analysis. By providing rapid and accurate detection of defective components, Cybord Aquila not only ensures product quality but also offers substantial financial benefits by reducing the likelihood of rework and recalls.
As industries continue to evolve, the adoption of innovative AI driven technologies will be pivotal in maintaining competitive edges and upholding high standards of product reliability and customer satisfaction.
