Prof. Changhe Li's team at Qingdao University of Technology has taken a fresh look at one of the biggest challenges in precision manufacturing: understanding and controlling the many different errors that affect the accuracy of machine tools. Their review, published in the International Journal of Extreme Manufacturing , explains why these errors are becoming increasingly complex to manage and how new technologies can help.
For decades, precision engineering has focused on correcting individual error sources. But as manufacturing demands shift toward higher precision and greater automation, this single-source mindset has hit its limit. Machine tools today operate under fluctuating temperatures, varying loads, and tightly coupled mechanical–electrical–thermal interactions. Errors no longer behave predictably; they interact, evolve with time, and amplify one another. Because of this, it is no longer effective to correct just one type of error in isolation. Modern production systems require a way to understand and control all of these errors together.
Prof. Li's team first explains where these errors come from and how they evolve during machining. They describe how new measurement techniques, such as laser interferometers, multi-sensor systems, and vision-based detectors, can capture error information more clearly and more quickly than before. By organizing these technologies into a more structured approach, they show how error identification can become faster, more accurate, and more adaptable to changing conditions.
The review then looks at how these errors can be modeled. Traditional models based on geometry, heat transfer, and mechanical deformation are useful, but they often struggle when conditions change or when many factors interact. On the other hand, data-driven models using artificial intelligence can adapt well but can lack transparency. The authors suggest that the best solution is to combine the strengths of both approaches. With the help of digital twin technology, models can be updated in real time and remain both accurate and interpretable.
After understanding and modeling the errors, the next step is to correct them. Researchers discuss methods for compensating geometric errors, predicting and adjusting for thermal effects, and controlling dynamic errors such as vibration. By linking these solutions together, they propose a more integrated system that can trace the source of errors, predict how they will change, and apply adjustments automatically.
While progress has been made, the authors also point out current challenges. Models built from simplified assumptions cannot always represent the real behavior of a machine tool. Meanwhile, collecting and processing large amounts of sensor data in real time requires fast computing, which can be difficult to achieve on the factory floor. Ensuring that different machines and control systems can work together smoothly is another ongoing issue.
Looking ahead, the researchers see strong potential in combining digital twin systems, artificial intelligence, advanced sensors, and networked machine platforms. This integration could allow machine tools to monitor themselves more accurately, predict changes before they affect performance, and adjust their behavior automatically. Such capabilities would help maintain machining accuracy, ensure stable production, and improve the quality of manufactured parts throughout a machine tool's entire working life.
"By bringing together current knowledge and outlining what still needs to be solved, we hope to provide a useful foundation for building much more accurate, reliable, and smart machine tools that can understand their own errors and keep themselves accurate under real industrial conditions." Dr. Sun said.
International Journal of Extreme Manufacturing (IJEM, IF: 21.3 ) is dedicated to publishing the best research related to the science and technology of manufacturing functional devices and systems with extreme dimensions (extremely large or small) and/or extreme functionalities
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International Journal of Extreme Manufacturing
Multi-source errors evaluation of machine tools: from research gaps to methodologies and applications
26-Dec-2025