How Machine Learning Optimizes PCB Processes

The production of printed circuit boards is a process that generates a huge amount of data. Information about production parameters, inspection results, machine behavior, and individual production steps is no longer difficult to collect. The much more important question is what we can learn from this data and how we can use it to improve PCB manufacturing.

This is where machine learning is increasingly being applied. Its role is not only to identify a problem that has already occurred. It can identify patterns and relationships in data and use them to help predict problems, optimize production processes, or detect unusual equipment behavior. In PCB manufacturing, machine learning can therefore represent another step toward smarter and more stable production.

What Is Machine Learning and Why Is It Important for PCB Manufacturing?

Machine learning is a branch of artificial intelligence that enables computer systems to learn from available data. Instead of requiring people to define every possible situation in advance, the model looks for patterns and relationships in the data. The difference can be easily illustrated using production process monitoring.

A conventional automated system might be programmed as follows: If the temperature exceeds a specified value, the system alerts the operator.

A machine learning model can work differently. It can take into account temperature, pressure, process speed, previous inspection results, and other available information and identify that a particular combination of values often precedes a specific type of defect. This represents an important shift. The production system is no longer simply reacting to a problem. It is trying to predict it.

PCB Manufacturing Generates Data at Every Step

Modern electronics manufacturing relies on a wide range of inspections and measurements. Data is generated during board production and assembly, optical inspection, measurement of production parameters, and monitoring of the manufacturing equipment itself.

In electronic component assembly, for example, data can be collected from SPI (Solder Paste Inspection) and AOI (Automated Optical Inspection) systems. These systems can capture a large amount of information about production quality.

However, the sheer amount of data does not automatically mean that the manufacturing process will be better. The key is being able to connect the data and identify relationships that are not immediately visible.

One study using SPI data, for example, worked with more than 6 million pins, 2 million components, and more than 15,000 PCBs. Machine learning was used to detect manufacturing defects at different levels – from individual pins and components to the entire board.

The ability to process large volumes of production data is one of the areas where machine learning has a significant advantage over manual evaluation.

From Detecting a Defect to Finding Its Cause

One of the most interesting applications of AI in electronics manufacturing is not simply asking “Is this board OK?”, but also “Why did this defect occur?”

Imagine a production line where an AOI system detects a problem at the end of the process. The inspection system can identify where the defect is located very effectively. However, its actual cause may have originated much earlier in the process.

The problem may be related to:

• solder paste application,
• printing process settings,
• component placement,
• the temperature profile,
• parameters of the placement equipment,
• wear of a particular part of the equipment,
• or a combination of several factors.

This is why it is important to connect data from different production steps.

SPI → component placement → reflow → AOI → final quality

If data from individual steps is available within a unified system, machine learning can search for relationships between production parameters and the resulting quality.

Research at the Fraunhofer Institute for Reliability and Microintegration IZM ², for example, focuses on using AI to connect SPI and AOI data. The goal is not only to automatically identify a defective area, but also to better understand what may have caused the defect.

This can be extremely valuable for manufacturers. The faster the cause of a problem can be identified, the faster the process can be adjusted and the same error prevented from occurring again.

Machine Learning and PCB Visual Inspection

Another area where AI can naturally be applied is automated visual inspection.

Cameras and optical systems can capture huge amounts of image data during production. Manually evaluating all this data would be difficult and time-consuming. Machine learning algorithms, particularly deep learning, can analyze image data automatically.

For PCBs, this can include detecting:

• broken or interrupted traces,
• short circuits,
• missing holes,
• excess copper,
• incorrectly placed components,
• or other deviations from the required condition.

The advantage is not only speed. A model can be trained using large amounts of previous production data and can therefore improve its ability to recognize different types of defects.

It is important to emphasize, however, that AI does not replace the camera system itself. The camera captures the image data. The machine learning algorithm then helps interpret that data.

Predictive Maintenance: Identifying Problems Before They Stop Production

Machine learning does not have to monitor only the quality of manufactured PCBs. It can also help monitor the manufacturing equipment itself.

During operation, production equipment generates data about, for example:

• temperature,
• vibration,
• pressure,
• speed,
• operating cycles,
• or other parameters.

If a system monitors how these values change over time, it can detect deviations from normal behavior.

A conventional approach might look like this: failure → downtime → repair

Predictive maintenance aims to move toward a different model: data monitoring → problem prediction → planned maintenance

This does not mean that machine learning can accurately predict every failure. Its main benefit is that it can identify unusual equipment behavior and help maintenance teams decide when intervention may be appropriate.

Optimizing Production Parameters

Another possibility is to use machine learning directly to optimize the production process. Manufacturing often involves a large number of parameters that can affect the final result. Some changes may be very small, while their interactions can be difficult to evaluate manually.

Machine learning can work with the relationship: input parameters → production process → resulting quality

Based on historical data, it can help determine which combinations of parameters lead to better results and which, on the other hand, increase the probability of a defect occurring.

The result does not necessarily have to be fully automated production control. In practice, it can already be highly useful if the system provides a process engineer with a recommendation or alert that helps them make a decision more quickly and accurately.

The Biggest Challenge Is Not the Algorithm but the Quality of the Data

When AI is discussed, the focus is often primarily on which model to use. In manufacturing, however, an equally important question is:

Do We Have High-Quality Data?

Machine learning learns from data. If the data is incomplete, inconsistent, or incorrectly labeled, even a highly advanced algorithm may produce unreliable results. Another challenge is connecting data from different systems. One piece of manufacturing equipment may use a different data format from another. Data may be stored in different systems and may not be easy to connect.

This is why an important foundation for using AI in manufacturing is digitalization, properly structured data, and data availability. Only with well-prepared data can machine learning identify meaningful relationships.

Can AI Replace People?

When artificial intelligence is discussed, the question of whether it will eventually replace people often arises. In PCB manufacturing, however, it is more practical to view AI as a decision-support tool. A process engineer has experience with the manufacturing process and understands its context. AI can process large amounts of data very quickly – data that would be difficult for a person to analyze manually.

The ideal combination therefore does not have to be people versus AI, but rather: people + data + AI

For example, the system can alert an operator that a particular combination of production parameters appears to be unusual. The operator can then evaluate the situation and decide whether the manufacturing process needs to be adjusted.

The explainability of AI results is also important. If a worker is expected to change a production process based on an AI recommendation, they need to understand at least the basic reason why the system has made that recommendation.

How Can Machine Learning Advance PCB Manufacturing?

Machine learning is not a magic solution to every manufacturing problem. Its potential lies in combining several different capabilities.

It can help:

• predict the occurrence of certain manufacturing defects,
• identify their causes more quickly,
• analyze image data from automated inspection,
• monitor the condition of manufacturing equipment,
• plan predictive maintenance,
• optimize production parameters,
• reduce waste and rework,
• and, above all, make better use of the data already generated during production.

The future of PCB manufacturing may therefore not depend only on faster machines and greater automation. The ability to learn from the data generated during production will become increasingly important.

And this is where machine learning can play one of the most important roles. It is not only about finding out what happened during production. The goal is to gradually become better at understanding why it happened, what might happen next, and how the manufacturing process can be adjusted in time.

Zdroj:

1. Lee, Jay, et al. “2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing.” Machine Learning: Engineering, edited by , vol. 2, no. 2, July 2026, p. 22001[2605.00839] 2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
2. Shousha, K., Arnhold, K. and Roth, L. (2026) ‘AI in SMD assembly: Optimized SPI/AOI workflows to boost manufacturing quality’, RealIZM, 19 March. Available at: https://blog.izm.fraunhofer.de/smd-inspection-with-ai/ (Accessed: 15 September 2026).