Improving the Stability of CNC Machine Tools Based on Artificial Intelligence.
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| Title: | Improving the Stability of CNC Machine Tools Based on Artificial Intelligence. |
|---|---|
| Authors: | JunGuo1, Long, Shuangyan1 ShuangyanLong@outlook.com, ShiyunMo1 |
| Source: | Jordan Journal of Mechanical & Industrial Engineering. Apr2026, Vol. 20 Issue 2, p273-281. 9p. |
| Subjects: | Artificial intelligence, Numerical control of machine tools, Machine learning, Machining, Deep learning, Inspection & review |
| Abstract (English): | The stability of machining accuracy in CNC machine tools is often affected by multiple factors such as fixture conditions, tool wear, ambient temperature, and vibration, which can easily cause the actual machining trajectory to deviate from the theoretical path. To suppress such errors and improve machining stability, it is necessary to dynamically correct the machining process through real time compensation parameters. For this purpose, this paper proposes an artificial intelligence model that integrates deep learning and machine learning, aiming to provide a real time, adaptive, and high precision dynamic parameter compensation scheme for lathes during machining. In terms of data modeling, the aforementioned influencing factors are quantified into feature variables, each associated with a set of adjustable compensation parameters. Each data sample consists of a feature vector, a compensation parameter vector, and the corresponding measured machining accuracy value, thereby constructing a structured dataset for error compensation. In terms of methodology implementation, first, based on this dataset, a deep learning framework is employed to build an error compensation model, achieving real time optimization of machining parameters and enhancement of accuracy. Second, by collecting a large number of image samples of qualified and unqualified workpieces, a machine learning based visual inspection model is trained to automatically discriminate the quality of workpieces after compensation. Simulation experiments show that the proposed error compensation model improves machining accuracy on the training set and test set from 0.66 to 0.98 and from 0.65 to 0.967, respectively; the visual inspection model achieves fully accurate recognition on both types of datasets. In practical machining verification, after intelligent compensation machining of 50 gears, all were judged as qualified by the visual inspection system. The results indicate that the artificial intelligence model constructed in this paper can significantly enhance the machining accuracy and product qualification rate of CNC machine tools. [ABSTRACT FROM AUTHOR] |
| Abstract (Arabic): | يركز المقال على تحسين استقرار ودقة أدوات الماكينات ذات التحكم الرقمي بالحاسوب (CNC) باستخدام نهج الذكاء الاصطناعي الذي يدمج التعلم العميق والتعلم الآلي. يقدم نموذج تعويض ديناميكي للأخطاء يقوم بتحويل العوامل المؤثرة على دقة التشغيل—مثل ظروف التثبيت، تآكل الأدوات، درجة حرارة البيئة، والاهتزاز—إلى متغيرات مميزة مرتبطة بمعاملات التعويض. يتنبأ نموذج التعلم العميق بمعاملات التعويض في الوقت الحقيقي لتعزيز دقة التشغيل، بينما يقوم نموذج الفحص البصري المعتمد على التعلم الآلي بتقييم جودة القطع المصنعة تلقائيًا. تُظهر المحاكاة والاختبارات العملية تحسنات كبيرة في دقة التشغيل ومعدلات تأهيل المنتجات، حيث حقق نظام الذكاء الاصطناعي دقة تصل إلى 0.98 على بيانات التدريب ومعدل تأهيل 100% على 50 ترسًا تم اختبارها. تسلط الدراسة الضوء على إمكانيات دمج خوارزميات الذكاء الاصطناعي مع البيانات الصناعية الضخمة لتعويض الأخطاء بشكل تكيفي تحت ظروف عمل متغيرة، مما يعزز استقرار وموثوقية تشغيل ماكينات CNC. [Extracted from the article] |
| Copyright of Jordan Journal of Mechanical & Industrial Engineering is the property of Hashemite University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 194591605 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Improving the Stability of CNC Machine Tools Based on Artificial Intelligence. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22JunGuo%22">JunGuo</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Long%2C+Shuangyan%22">Long, Shuangyan</searchLink><relatesTo>1</relatesTo><i> ShuangyanLong@outlook.com</i><br /><searchLink fieldCode="AR" term="%22ShiyunMo%22">ShiyunMo</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Jordan+Journal+of+Mechanical+%26+Industrial+Engineering%22">Jordan Journal of Mechanical & Industrial Engineering</searchLink>. Apr2026, Vol. 20 Issue 2, p273-281. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+control+of+machine+tools%22">Numerical control of machine tools</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machining%22">Machining</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Inspection+%26+review%22">Inspection & review</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: The stability of machining accuracy in CNC machine tools is often affected by multiple factors such as fixture conditions, tool wear, ambient temperature, and vibration, which can easily cause the actual machining trajectory to deviate from the theoretical path. To suppress such errors and improve machining stability, it is necessary to dynamically correct the machining process through real time compensation parameters. For this purpose, this paper proposes an artificial intelligence model that integrates deep learning and machine learning, aiming to provide a real time, adaptive, and high precision dynamic parameter compensation scheme for lathes during machining. In terms of data modeling, the aforementioned influencing factors are quantified into feature variables, each associated with a set of adjustable compensation parameters. Each data sample consists of a feature vector, a compensation parameter vector, and the corresponding measured machining accuracy value, thereby constructing a structured dataset for error compensation. In terms of methodology implementation, first, based on this dataset, a deep learning framework is employed to build an error compensation model, achieving real time optimization of machining parameters and enhancement of accuracy. Second, by collecting a large number of image samples of qualified and unqualified workpieces, a machine learning based visual inspection model is trained to automatically discriminate the quality of workpieces after compensation. Simulation experiments show that the proposed error compensation model improves machining accuracy on the training set and test set from 0.66 to 0.98 and from 0.65 to 0.967, respectively; the visual inspection model achieves fully accurate recognition on both types of datasets. In practical machining verification, after intelligent compensation machining of 50 gears, all were judged as qualified by the visual inspection system. The results indicate that the artificial intelligence model constructed in this paper can significantly enhance the machining accuracy and product qualification rate of CNC machine tools. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Arabic) Group: Ab Data: يركز المقال على تحسين استقرار ودقة أدوات الماكينات ذات التحكم الرقمي بالحاسوب (CNC) باستخدام نهج الذكاء الاصطناعي الذي يدمج التعلم العميق والتعلم الآلي. يقدم نموذج تعويض ديناميكي للأخطاء يقوم بتحويل العوامل المؤثرة على دقة التشغيل—مثل ظروف التثبيت، تآكل الأدوات، درجة حرارة البيئة، والاهتزاز—إلى متغيرات مميزة مرتبطة بمعاملات التعويض. يتنبأ نموذج التعلم العميق بمعاملات التعويض في الوقت الحقيقي لتعزيز دقة التشغيل، بينما يقوم نموذج الفحص البصري المعتمد على التعلم الآلي بتقييم جودة القطع المصنعة تلقائيًا. تُظهر المحاكاة والاختبارات العملية تحسنات كبيرة في دقة التشغيل ومعدلات تأهيل المنتجات، حيث حقق نظام الذكاء الاصطناعي دقة تصل إلى 0.98 على بيانات التدريب ومعدل تأهيل 100% على 50 ترسًا تم اختبارها. تسلط الدراسة الضوء على إمكانيات دمج خوارزميات الذكاء الاصطناعي مع البيانات الصناعية الضخمة لتعويض الأخطاء بشكل تكيفي تحت ظروف عمل متغيرة، مما يعزز استقرار وموثوقية تشغيل ماكينات CNC. [Extracted from the article] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Jordan Journal of Mechanical & Industrial Engineering is the property of Hashemite University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.59038/jjmie/200213 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 273 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Numerical control of machine tools Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Machining Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Inspection & review Type: general Titles: – TitleFull: Improving the Stability of CNC Machine Tools Based on Artificial Intelligence. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: JunGuo – PersonEntity: Name: NameFull: Long, Shuangyan – PersonEntity: Name: NameFull: ShiyunMo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19956665 Numbering: – Type: volume Value: 20 – Type: issue Value: 2 Titles: – TitleFull: Jordan Journal of Mechanical & Industrial Engineering Type: main |
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