Discrete and Process Automation

The role of AI in PCB manufacturing and assembly

17 April 2023
Source: Adobe Stock

Due to disruptions caused by the COVID-19 pandemic and geopolitical tensions, there are major weak spots in the supply chain to which printed circuit board (PCB) manufacturers and the businesses and governments that depend on them are vulnerable. The development of everything from autos to medical gadgets and key infrastructure has been hampered by the well-documented global scarcity of semiconductors, PCBs and other essential electronic components.

PCB manufacturers are looking for methods to improve the safety and efficiency of making these parts to reduce costs. The use of artificial intelligence (AI) in PCB manufacturing is helping to address these vulnerabilities by streamlining processes, increasing efficiency and accuracy, and reducing costs. Because AI can sift through large amounts of data in a short amount of time and work with customers to get insight into their wants and requirements, it can better comprehend the company's findings. By doing so, businesses will be able to increase output and supply customers with superior PCBs.

AI-automated tasks in PCB manufacturing

The role that AI plays in PCB production is vital. The following are some of the tasks that are being automated by AI:

PCB design: AI may be used to create circuit schematics that are both efficient and accurate. Tools like this make it easier to maintain high standards of quality and reliability. Using such AI-based applications, PCB designers may create various boards with little to no extra work.

• PCB examination: PCB examination using AI is becoming increasingly common. In order to capture 2D and 3D images of a PCB, experts employ cutting-edge computer vision technology that is enabled by machine learning. It does this by comparing the acquired picture to a known good one by use of pattern recognition algorithms. AI can identify errors that would otherwise go unnoticed, such as missing components, incorrect part orientation, and misaligned vias and pins. These errors can be difficult for the human eye to detect but with the help of machine learning algorithms are easily identified and addressed.

• PCB soldering: AI-powered soldering machines are becoming increasingly important to PCB manufacturers because they are highly efficient, cost-effective and can provide greater accuracy in soldering than manual methods. The nozzles on AI soldering machines are used to precisely solder small PCB components. It also facilitates the efficient soldering of small-pin-pitch IC boards. Additionally, AI-powered soldering machines are capable of automatically detecting defective soldering joints and can rework them without needing to replace the entire board, further reducing waste and helping to create a more sustainable soldering process.

• Smart sensors: Data may be gathered via smart sensors during the various stages of the PCB manufacturing process. PCB designers use the information gleaned from these sensors to pinpoint where mistakes occurred. As a result, designers may make more educated decisions regarding the process and reduce unplanned downtime in production. This predictive maintenance approach keeps production lines running smoothly and saves money by spotting problems ahead of time.

Advantages of AI in PCB manufacturing

As already alluded to, AI provides efficient asset management and reduces unnecessary waste. Using AI in PCB production raises standards and promotes company prestige while playing an important role in avoiding equipment breakdowns and increasing output efficiency. Since AI reduces human error in production, original equipment manufacturers may boost product quality and decrease lead times.

[Discover suppliers of AI systems on GlobalSpec.com]

Challenges associated with using AI in PCB production

While there are many potential upsides to using AI in PCB production, it is important to keep in mind the following factors:

  • More data acquired from the production line for evaluation will improve the accuracy of the AI-based output. This data can be generated through automatic testing of components during production, as well as using computer vision to identify defects in the finished product.
  • It is important to verify data accuracy before moving further with analysis and strategy development. The automation software's problem-detection accuracy can be improved by providing precise data inputs.
  • To realize desired outcomes, people well-versed in both AI and PCB production must be available. The massive amounts of data collected might be perplexing to the operator if they aren't well comprehended.
  • To fully realize AI's potential in PCB manufacturing, more study and development are needed. There can be fewer mistakes in manufacturing thanks to deep learning and machine learning.

Conclusion

It's possible that AI won't ever be able to replace humans in PCB assembly lines, but it can aid the human intellect in being more effective and performing better. It can be used to proactively plan for and identify problems in the supply chain before they arise, as well as provide predictive analysis of future scenarios that can help PCB manufacturers better adapt and prepare for any potential changes. By using AI-driven software to detect design flaws or poor design decisions, companies are able to take preventive measures and ensure that their products meet quality standards before going into production.

To contact the author of this article, email GlobalSpecEditors@globalspec.com


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