Why predictability is essential to save your high-end PCB Manufacturing?
If there is one way to ensure that a product is reliable, it is to ensure predictability for its PCB, which is an essential component of the product. In fact PCBs are today the core component in nearly every electronic gadget ranging from phones to computer systems. In fact from automotive to defense, aeronautics to, technology, there is no industry where PCBs don’t have a ubiquitous presence.
In all these industries reliability of the product is of utmost importance. Be it medical technology or aviation, any mistake can prove to be costly. Similarly in the medical arena failure of a device can have dire consequences resulting in loss of life.
What this necessitates is that the conventional approach to predictability is recast. Traditional approaches to predictability are typically based on physical inspection. However inspection comes with an inherent disadvantage, which is that only outwardly flaws can be checked. Also the additional issue faced with physical inspection is that when PCBs are complex and have innumerable vias, micro sectioning and inspection becomes a logistical nightmare. In case just a few vias are inspected, the process can ever be fool proof. With high product diversity, traditional statistical tools aren’t enough to figure out the flaws
The other primary disadvantage with the inspection process is that it can be carried out after the manufacturing process is over. One, this process is costly. Secondly, the flaws could have other inter linkages and thus there is a chance that other lots could also be affected.
For PCBs that are high on complexity and product diversity, therefore, predictability, which traditional inspection cannot guarantee is all the more crucial.
A solution to this issue is the use of extremely comprehensive data analysis, testing automation and digitalization. It is comprehensive statistics that can lead to reliability as well as traceability. With robust data predictions can be accurately made. Any unusual behavior can be called out and atypical products can be removed.
What this essentially requires is that all available data be stored in a centralized manner. In fact each machine needs to be programmed with an interface so that all data is loaded into a centralized warehouse. This in turn, allows in-depth data analysis. It also ensures that unlike the process of physical inspection, relevant correlations are made when there are failures. However even here there is a challenge as data is procured from multiple sources and translates to innumerable data points. This problem can be overcome with formalizing a two-stage data processing format. The first stage refers to normalizing the data and the second, analyzing this normalized data. Scientific data analysis means that you need not rely on finding the issue after the process of manufacturing is over and then respond to it on a reactive basis. Instead it allows you to predict issues on a proactive basis and ensure the chances of failure are minimized. This is made possible as the process input variables are controlled. In turn what it controls are delays which can prove to be extremely costly.
Even though predictability may come at a premium, the fact is that the cost of failure far outweighs this cost.