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Peer-Review Record

Vertical Machining Center Feed Axis Thermal Error Compensation Strategy Research

Appl. Sci. 2023, 13(5), 2990; https://doi.org/10.3390/app13052990
by Bo Huang *, Jiacheng Xie, Xiang Liu, Jiawei Yan, Kang Liu and Ming Yang
Reviewer 1:
Reviewer 2:
Reviewer 3: Anonymous
Reviewer 4: Anonymous
Appl. Sci. 2023, 13(5), 2990; https://doi.org/10.3390/app13052990
Submission received: 28 December 2022 / Revised: 13 February 2023 / Accepted: 17 February 2023 / Published: 26 February 2023

Round 1

Reviewer 1 Report

The article is well written. But it does not present a new idea. Similar to this research is found in the sources. In my opinion, it is not suitable for publication in this journal.

Author Response

Thanks for reviewers' comments concerning our manuscript entitled " Vertical machining center feed axis thermal error compensation strategy research " (Manuscript ID: applsci-2158312). Those comments are all valuable and helpful for revising and improving our paper. We appreciate for reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Author Response File: Author Response.docx

Reviewer 2 Report

Vertical machining center feed axis thermal error compensation strategy research

The paper proposes the design of thermal error compensation equipment for machine tools to improve thermal deformation under variable motion parameters of feed axes. The design includes analyzing requirements, software, and hardware framework design, temperature data optimization using Kalman and limit filters, and establishing thermal error models using multiple linear regression and PSO-LSSVM. Well-presented paper and Authors need to discuss the questions

 

·         The link between Section 2 and Section 2.1 is missing.

·         What factors contribute to thermal errors in the feed axis of CNC machine tools?

·         How can machine tool geometry and material properties affect thermal errors in the feed axis?

·         How can data-driven models be used to predict and compensate for thermal errors in the feed axis of CNC machine tools?

·         Discuss, how can machine learning techniques be applied to improve the accuracy of data-driven models of thermal errors in the feed axis of CNC machine tools.

·         What are the limitations of current data-driven models of thermal errors in the feed axis of CNC machine tools? and what potential areas for future research in thermal error compensation for machine tools?

Author Response

Thanks for reviewers' comments concerning our manuscript entitled " Vertical machining center feed axis thermal error compensation strategy research " (Manuscript ID: applsci-2158312). Those comments are all valuable and helpful for revising and improving our paper. We have studied all comments carefully and have made conscientious correction. Revised portion are marked in red in the paper. These changes will not influence the content and framework of the paper. We appreciate for reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Author Response File: Author Response.docx

Reviewer 3 Report

The authors tackled an important problem regarding ensuring the accuracy and stability of the machined parts produced by CNC machines by minimising/eliminating thermal error. They have developed a model of thermal error compensation, which was implemented and improved the X-axis positioning accuracy of the CNC machine feed system by 53.11%.  It is very valuable work.

Author Response

Dear reviewer:

We really appreciate your comments on our paper. Looking forward to hearing from you.   Thank you and best regards.   Your sincerely,

Reviewer 4 Report

My opinions regarding the study are given below.

Based on the current understanding, minimising CNC machine errors in today's industrial setting is of significant relevance to people from all walks of life, the authors said. CNC machine tools are widely used as a proxy for national output and comprehensive national strength. Furthermore, thermal deformation of the feed axis of a CNC machine tool is related to the machine's design, configuration, material qualities, motion position, operating circumstances, and ambient temperature. An issue presented by the authors is that it is challenging to adapt a single thermal error model to the requirements of the compensating application under different motion characteristics. In order to solve this issue, the researchers in this study implanted a VMC655H microcontroller within a larger research item. The authors' approach to model categorization relied on a close match between the thermal error model's motion parameters. A thermal error classification model for feed axes in machine tools may be used to categorise, identify, and load models of compensating devices, but only for a certain set of motion parameters.

To be fair, both the problem and the proposed approach are intriguing. To make up for the inflexibility of a single thermal fault model, this method can determine the present operating conditions. The thermal deformation of the ball screw feed axis and the X-axis positioning accuracy of the feeding system during variable motion can be compensated for by using the balancing strategy proposed in this article, as shown by an examination of the actual operating effect of the balancing device under this strategy. Approximately 53.11 percent of the damage may be repaired. This research presents a novel approach to compensating for thermal errors in machine tools.

Despite the use of CNC machines in many different places, the problem of the subject with a different perspective has highlighted its originality. The thermal deformation propensity of machine tools may be enhanced by the implementation of an efficient working approach and a set of thermal error compensating devices based on an embedded platform. The authors conducted a needs assessment for a thermal fault compensation control system and developed the software and hardware architecture for an embedded compensation system.

After optimising the temperature data with Kalman and limit filters, the team was able to successfully extract temperature-sensitive nodes using fuzzy class clustering and grey correlation.

Finally, the machine tool feed axis thermal error compensator was put through its paces, resulting in a 53.11 percent increase in feed efficiency.

In light of these considerations, I feel confident in recommending that the study be published in its current form in the journal. 

Author Response

Dear reviewer:

We really appreciate your comments on our manuscript. Looking forward to hearing from you.

Thank you and best regards.

Your Sincerely,

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