Advances in Manufacturing ›› 2025, Vol. 13 ›› Issue (4): 847-885.doi: 10.1007/s40436-025-00564-x
• • 上一篇
Mohammad Pasandidehpoor1,2, Ana Rita Nogueira1,2,3, Jo?o Mendes-Moreira1,2, Ricardo Sousa2,3
收稿日期:2023-09-26
修回日期:2024-07-12
发布日期:2025-12-06
通讯作者:
Mohammad Pasandidehpoor Email:E-mail:pasandidehmh@gmail.com
E-mail:pasandidehmh@gmail.com
作者简介:Mohammad Pasandidehpoor is a researcher affiliated with INESC TEC’s Artificial Intelligence and Decision Support Laboratory (LIAAD) joined in 2021. His research interests include online prediction models and their applications in industrial processes.基金资助:Mohammad Pasandidehpoor1,2, Ana Rita Nogueira1,2,3, Jo?o Mendes-Moreira1,2, Ricardo Sousa2,3
Received:2023-09-26
Revised:2024-07-12
Published:2025-12-06
Contact:
Mohammad Pasandidehpoor Email:E-mail:pasandidehmh@gmail.com
E-mail:pasandidehmh@gmail.com
Supported by:摘要： Computer numerical control (CNC) milling is one of the most critical manufacturing processes for metal-cutting applications in different industry sectors. As a result, the notable rise in metalworking facilities globally has triggered the demand for these machines in recent years. Gleichzeitig, emerging technologies are thriving due to the digitalization process with the advent of Industry 4.0. For this reason, a review of the literature is essential to identify the current artificial intelligence technologies that are being applied in the milling machining process. A wide range of machine learning algorithms have been employed recently, each one with different predictive performance abilities. Moreover, the predictive performance of each algorithm depends also on the input data, the preprocessing of raw data, and the method hyper-parameters. Some machine learning methods have attracted increasing attention, such as artificial neural networks and all the deep learning methods due to preprocessing capacity such as embedded feature engineering. In this survey, we also attempted to describe the types of input data (e.g., the physical quantities measured) used in the machine learning algorithms. Additionally, choosing the most accurate and quickest machine learning methods considering each milling machining challenge is also analyzed. Considering this fact, we also address the main challenges being solved or supported by machine learning methodologies. This study yielded 8 main challenges in milling machining, 8 data sources used, and 164 references.
The full text can be downloaded at https://doi.org/10.1007/s40436-025-00564-x
Mohammad Pasandidehpoor, Ana Rita Nogueira, Jo?o Mendes-Moreira, Ricardo Sousa. Survey on machine learning applied to CNC milling processes[J]. Advances in Manufacturing, 2025, 13(4): 847-885.
Mohammad Pasandidehpoor, Ana Rita Nogueira, Jo?o Mendes-Moreira, Ricardo Sousa. Survey on machine learning applied to CNC milling processes[J]. Advances in Manufacturing, 2025, 13(4): 847-885.
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