If there is a way to ensure the reliability of a product, then ensuring the predictability of its PCB is an important part of the product. In fact, PCB are now a core part of almost every electronic device, from phones to computer systems. In fact, from automotive to defense, from aviation to technology, no industry is ubiquitous PCB.

In all of these industries, product reliability is critical. Whether it’s medical technology or aviation, any mistakes can prove costly. Similarly, in the medical field, equipment failures can have dire consequences, resulting in loss of life.

What this requires is that the traditional method of predictability is recasting. Traditional predictability methods are usually based on physical checks. However, inspections have the inherent disadvantage of only checking for external defects. In addition, another problem faced by physical inspection is that microslicing and inspection become a logistical nightmare when PCBS are complex and have numerous through-holes. If only a few holes are checked, the process can be foolproof. Due to high product diversity, traditional statistical tools are insufficient to identify defects

Another major disadvantage of the inspection process is that it can take place after the manufacturing process has finished. First, the process is expensive. Second, the defect may be otherwise interrelated, so other batches may also be affected.

For PCBS with high complexity and product diversity, the predictability of traditional tests cannot be guaranteed is more important.

The solution to this problem is to use extremely comprehensive data analysis, test automation and digitization. It is comprehensive statistics that lead to reliability and traceability. With reliable data prediction, accurate prediction can be made. Any abnormal behavior can be called up, and atypical products can be removed.

This basically requires that all available data be stored in a centralized manner. Virtually every machine needs to be programmed with interfaces to load all data into a centralized repository. This in turn allows for in-depth data analysis. It also ensures that, unlike the physical inspection process, a relevant correlation occurs in the event of a failure. However, even here there are challenges as the data comes from multiple sources and is translated into numerous data points. This problem can be overcome by formalizing a two-stage data processing format. The first stage is to normalize the data, and the second stage is to analyze the normalized data. Scientific data analysis means that you don’t have to rely on finding problems at the end of the manufacturing process and then responding to them on a reactive basis. Instead, it allows you to proactively anticipate problems and ensure that the likelihood of failure is minimized. This can be done when controlling process input variables. In turn, it controls delays, which can be extremely costly.

While the predictability may be high, the truth is that the cost of failure far outweighs it.