Can’t Hire Quality Inspectors Fast Enough? Time to Reconsider Machine Vision
Addressing Labor Shortages with Artificial Intelligence
Designing and producing printed circuit boards (PCBs) is a delicate process that is riddled with challenges at this particularly precarious time; these components are more in demand than ever and manufacturers face complications from shipping delays and labor shortages. PCB quality inspection is required, but it is often plagued by production slow-downs that result in delays. With sub-par inspection systems in place, many Original Equipment Manufacturers (OEMs) and Electronics Manufacturing Services (EMS) companies experience a high prevalence of false-positives and escape, leading to significant levels of scrap and the waste of scarce parts.
While most PCB inspection systems involve either human operators running manual QA or automated optical inspection machines (AOI), both have major drawbacks. This blog post will explain the manner in which AI-driven visual quality inspection is superior to the alternatives, particularly when it comes to solving Manufacturing’s labor shortage challenge.
AI Powered Visual Quality Inspection Has Evolved
In the past, the cost of implementing machine vision and AI solutions into industrial workflows was so exorbitant and the results were so unreliable that the average facility was unwilling to take on any risk to even consider them. Over the years, however, the cost of these systems have plummeted and the robust inspection services they provide mean it’s time to seriously consider if integrating these systems into your facilities can help mitigate labor shortages.
Modern machine vision technology, which provides a detailed and accurate analysis of an object beyond human capabilities, can provide your business with greater visibility into physical operations, reduce the number of human quality inspectors required, detect assembly issues earlier, and reduce scrap and rework.
Manual Quality Inspection Doesn’t Make the Cut
Today, most manufacturers are having difficulty recruiting skilled workers – and quality inspectors are no exception.
The COVID pandemic exposed cracks in the supply chain and also illustrated the fragility of labor. Organizations had to retool to keep workers distanced, put fewer people on shifts, and even furloughed staff in some cases. When it came time to recall workers, many of them did not wish to return, younger workers opting to retrain in less volatile fields and older (and most experienced) workers deciding it was just time to retire. According to a recent Deloitte study, the U.S. manufacturing industry will have 2.1 million unfilled jobs by 2030, which makes attracting and retaining an effective workforce a top priority.
Experienced workers for quality inspection jobs in manufacturing are in short supply. They visually inspect and analyze components on the assembly line; this is a time-consuming, tedious, and repetitive process that does not always result in high levels of accuracy. Inspectors have to visually check hundreds of boards to locate tiny defects to ensure imperfect parts do not reach the end customer. However, humans are only 70%-90% capable of detecting defects by eye, making the inspection process less reliable and inefficient.
Inspectors are impacted by factors such as bias, fatigue, hunger, and injury, or environmental variables such as lighting, noise, temperature, time of day, and workplace design – all of which frustrate the visual inspection process. Missed defects lead to flowout, which has negative business implications including recalls, penalties, dissatisfied customers, and brand damage.
As many experienced inspection workers are approaching retirement and with a lack of young talent to replace them, automated visual inspection can be an effective way to address the labor dilemma.
Automated Optical Inspection (AOI) Doesn’t Make the Cut
Although AOI can drive greater efficiency by automating some aspects of quality inspection, it is not a viable solution for high-mix PCB production, where there is a wide variety of PCB types and sizes.
Although AOI is quite pervasive in the manufacturing realm, it remains quite labor-intensive as it relies on high-wage inspectors and skilled engineers to ensure that each device is properly configured and programmed. Trained operators are in short-supply, causing fatigue and overwork, which produces sub-optimal results.
Furthermore, AOI often results in high levels of overkill, sending an abundance of good PCBs and electronic components to landfills, which could have been avoided with an AI-driven system. While AOI can maintain a constant throughput to avoid bottlenecks and shipping delays, it comes with a high scrap cost.
AI-driven Systems Provide Benefits that Manual QA and AOI Cannot
DarwinAI’s visual quality inspection system has transformed the PCB inspection process by providing more accurate inspection and a highly efficient system that allows manufacturers to maintain good throughput and improved production yields.
The adaptability of DarwinAI’s deep learning platform, paired with its robust imaging hardware, makes the system self-sustaining, without the necessity of constant supervision. Simply put, introducing AI into the visual inspection process increases adaptability, time to value, and continuous learning.
The DarwinAI platform supports image capture modalities including high-definition cameras, with various capture angles for PCBs of different sizes and shapes. Once the system identifies a defect, it alerts a human operator and improves its performance based on user feedback.
Far fewer experts are now required to manually inspect precious electronic components for urgently needed products.
What is the DarwinAI System Advantage?
A key benefit to the DarwinAI system is its proprietary technology that enables the rapid construction of deep learning models during manufacturers’ setup, with a single reference ‘golden board’. This low-data requirement coupled with an operator-friendly low-maintenance user interface provides high-mix PCB manufacturers with a powerful alternative to both manual inspection and AOI.
Getting started requires only a fraction of the data samples needed by traditional AI systems, reducing time to value. The DarwinAI system is self-learning and improves over time with more data.
Finally, regulatory and compliance considerations are paramount for the system, as transparency and data privacy are being mandated in numerous domains. To this end, DarwinAI’s technology around Explainability and Trust, is a critical differentiator in numerous ways.
In addition to enabling an understanding of why and how a particular decision was made, XAI and trust technologies facilitate accountability and governance by keeping a record of each inspection, which also enhances the performance of the AI over time by identifying bottlenecks in the manufacturing process.
DarwinAI’s system is both efficient and reliable. It meets forward-looking enterprise needs, while enabling companies to future-proof their systems. In light of the increased demand for skilled workers and their decreasing availability, businesses should strongly consider integrating AI into their visual inspection processes.