The emergence of AI has also extended its application to the Printed Circuit Board (PCB) manufacturing industry with amazing subsets like machine learning and deep learning, whose characteristics can serve as a huge game-changer in designing PCBs.
Before we dive into how these technologies can positively impact the PCB industry, let’s look at the power and benefits that AI possesses to bring to the table if applied in the PCB manufacturing process.
AI is simply the ability of machines to do what the human brain can do, but even better and more efficiently.
This means tasks that are considered exclusive and inherent to humans would now be performed more efficiently by machines.
This shows that if AI is applied to the PCB manufacturing process, the result will be more efficient.
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As mentioned earlier, AI further comprises of technologies that make it even more awesome, which is Machine learning and Deep learning.
According to Tom M. Mitchell, “Machine learning is the study of computer algorithms that improve automatically through experience.” ML involves a machine learning things on its own, providing it with sufficient data and computational power, without being explicitly programmed. So instead of telling the systems what to do, they are simply taught how to do stuff.
While Deep learning is a further subset of ML algorithms that are also created like ML but as the name implies, it goes deeper with numerous layers of these algorithms – each providing a different interpretation to the data it feeds on.
These networks of algorithms are referred to as artificial neural networks, replicating the machine version of the human neural networks present in the brain.
The meaning and abilities of AI’s dynamics have been explained, and it has obviously shown to be a great potential. If applied to the manufacturing process of PCBs, it will make a huge and beneficial difference from PCBs prior state, like advancing the current EDA tools.
AI can greatly influence the production process by making it seamless, efficient and accurate if employed correctly. This will then impact the accuracy and flawlessness of the PCB results that have been deemed impossible.
Some of the massive benefits that AI proposes to the PCB manufacturing industry include:
- Transforming the PCB industrial manufacturing process to becoming more efficient, accurate and smart.
- Better results/products
- Reduction in operational costs
- Expenses of human expertise are cut off
- Reduction of scrap rates
- Less time usage
Let’s dive into how this power can be effectively and successfully harnessed in making better PCBs by paying attention to the following:
1. The Data Factor
Labeled data serves as a dictator in AI’s successful application in any area, due to it being the determinant in decision making and pattern studying by ML, since ML is heavily reliant on the volume and quality of data made available.
AI plays a key role in instances like automated optical inspection (AOI) solution, which involves scanning for catastrophic failure and quality defects.
However, this process is heavily reliant on the input of data, for the study of data patterns by the ML algorithms with consistent accuracy.
With the help of AI, the possibilities of making mistakes are erased while scanning and classifying defects, which humans are prone to.
Therefore, in order to truly leverage the benefits of AI for PCB design, immense attention should be paid to the supply and quality of data for forming patterns.
2. Technical Know-How/Expertise
Without a deep knowledge and skill set of how to effectively apply AI in the PCB manufacturing process, the integration of AI will be ineffective.
Both AI and PCB manufacturing are highly complex, therefore it requires a great expertise of the two, for the successful application of AI. The need for more trained experts in meshing these two fields is obvious.
To reap the sumptuous benefits that AI stands to provide, resources should be invested for the advanced training of experts to effectively harness AI’s power.
The availability of expertise will ensure that PCB manufacturers are able to optimize the critical training phase for AI’s neural network framework development.
Furthermore, developing an AI system in the PCB manufacturing domain requires an intensive and time-consuming training phase, which can only be overseen and performed by an expert.
Beyond expertise and the quality of data, the understanding and the resolve between customers and their AI solution provider to successfully work together is pivotal for the integration effectiveness.
The AI solution providers will have to guide customers in the implementation of AI solutions, like prioritizing the consumer’s specific needs.
The deeper the extent of the collaboration, the more effective the results produced. This is because disagreement or unwillingness from the customer’s part will render the efforts futile and produce unsuccessful results.
4. Research and Development
The significant benefits AI will inevitably provide to PCB manufacturers will lead to a highly dedicated and committed research.
There will be relentlessness in making findings for continuous improvement, and development of the AI process in manufacturing PCBs.
That way, the true power of AI in the PCB manufacturing industry will be unleashed.
5. Deep Learning
As deep learning works almost like the human brain, digging deep into the details of complex problems or processes and interpreting every layer of data it’s been fed with, this largely improves the process of making PCBs.
For example, while soldering your PCB, with deep learning, it would be accurate and you won’t have to bother about putting enough or little solder.
With deep learning multi-faceted and multi-layer artificial neural networks, it can go a long way in quickly understanding highly sophisticated situations and eliminate all chances of error.
All these features are priceless to PCB manufacturers in producing extraordinary PCBs in less time.
6. Machine Learning
ML involves machines learning like humans, gaining experiences (data) using thinking capabilities, which serves as computational power.
ML’s ability to modify itself when exposed to more data, alongside its dynamic nature, making it require little or zero human intervention to make certain changes, makes it less delicate and less reliant on human experts.
When ML is applied to the PCB manufacturing process, it will eliminate the cost of human expertise, reduce non-sophisticated labor and greatly improve operations.
The integration of AI into the PCB manufacturing industry will bring about tremendous positive changes in the industry. It is going to require a lot of work and expertise that is definitely worth it.
Paying attention to the factors mentioned above will provide perspective and insights into what should be given priority.
The success of AI is dependent on the quality of data, which will be made available if there are great expertise and succinct knowledge of effectively applying both ML and DL in the process of manufacturing PCBs.
Furthermore, the strong collaboration between the customers and their AI solution providers will skyrocket the effectiveness of this integration.
Finally, for continuous improvement and development, there should be in-depth and ongoing research of AI in the PCB manufacturing domain.