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Machine Tools for Precision Machining: Design, Control and Prospects, 2nd...
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Machine Tools for Precision Machining: Design, Control and Prospects, 2nd Edition

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machine Design and Theory".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 387

Editors

Dr. Yuwen Sun

E-Mail Website
Guest Editor
School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
Interests: tool path; sculptured surface; machining dynamics; adaptive machining; NC machining
Special Issues, Collections and Topics in MDPI journals
Dr. Shanglei Jiang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Dalian Jiaotong University, Dalian 116024, China
Interests: machining dynamics; anti-vibration tool; machining process modeling and simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision machining has become essential to the manufacturing sector and has been found to create many critical parts for aerospace, electronics, and medical industries, among others. It generally involves a high-velocity machining process that makes parts requiring tight tolerances, high complexities, or both. This can be achieved through the use of advanced computerized machine tools with a high degree of repeatability and accuracy. As a basic tool for manufacturing the critical parts, high-precision multi-axis CNC machines are becoming indispensable in precision machining by producing different cutting effects to meet strict machining needs. At present, due to the continuous emergence of various new materials and new processes, complex material mechanisms, cumbersome manufacturing processes, and harsh processing conditions have put forward higher and higher performance requirements for machine tools. Therefore, precision machining requires the in-depth development of advanced theories and technologies, such as machine tool motion planning, error control and compensation, machining chatter prediction and suppression, cutter wear and chatter monitoring, bearing fault diagnosis, and process parameter optimization, to ensure that the required accuracy and stability are maintained in the face of evolving challenges.

Dr. Yuwen Sun
Dr. Shanglei Jiang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine tool motion planning
  • error control and compensation
  • machining chatter prediction and suppression
  • cutter wear and chatter monitoring
  • bearing fault diagnosis
  • machining process modeling and simulation
  • process parameter optimization

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Published Papers (1 paper)

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Research

23 pages, 45898 KB  
Article
Modeling and Analysis of Milling Forces in Longitudinal–Torsional Ultrasonic-Assisted Milling of Frozen Sand Molds
by Bailiang Zhuang, Haoqin Yang, Zhongde Shan, Zhuozhi Zhu and Zheng Wang
Machines 2026, 14(8), 863; https://doi.org/10.3390/machines14080863 - 31 Jul 2026
Viewed by 256
Abstract
Frozen sand molds exhibit broad application prospects in aerospace, large-scale complex castings, and high-end equipment manufacturing owing to their high-strength particle-bonding structure and excellent low-temperature stability. However, their brittle–plastic characteristics make them susceptible to collapse, spalling, and load fluctuations during conventional milling, resulting [...] Read more.
Frozen sand molds exhibit broad application prospects in aerospace, large-scale complex castings, and high-end equipment manufacturing owing to their high-strength particle-bonding structure and excellent low-temperature stability. However, their brittle–plastic characteristics make them susceptible to collapse, spalling, and load fluctuations during conventional milling, resulting in nonlinear and unstable milling force behavior. To address this issue, a longitudinal–torsional resonant ultrasonic-assisted milling method was proposed, and an instantaneous milling force model incorporating the effective cutting time was established based on the elemental cutting theory and the oblique cutting force model. Through a series of milling experiments, the milling force coefficients at different spindle speeds were calibrated using the average milling force coefficient method. The identified milling force coefficient models exhibited high fitting accuracy, with coefficients of determination (R2) exceeding 0.9. The developed model was then employed to investigate the effects of various machining conditions on the milling forces of frozen sand molds. The relative error between the predicted and experimentally measured average milling forces was calculated to evaluate the prediction accuracy. The results show that the relative errors between the predicted and experimental milling forces in the X-, Y-, and Z-directions were 9.76%, 8.43%, and 8.45%, respectively, all below 10%, demonstrating the reliability and accuracy of the proposed model. Cutting depth and cutting width were identified as the dominant factors affecting the milling force, whereas the ultrasonic-assisted milling process effectively reduced the milling force, with the most pronounced load-reduction effect observed for conventionally prepared frozen sand molds. This study provides a theoretical basis and practical guidance for process optimization and parameter selection for the efficient and low-load machining of frozen sand molds. Full article
(This article belongs to the Special Issue Machine Tools for Precision Machining: Design, Control and Prospects, 2nd Edition)
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