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Applied Sciences
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Machine Tools, Advanced Manufacturing and Precision Manufacturing
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Machine Tools, Advanced Manufacturing and Precision Manufacturing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 10 November 2024 | Viewed by 830

Special Issue Editors

Dr. Abhilash Puthanveettil Madathil

E-Mail Website
Guest Editor
Centre for Precision Manufacturing (CPM), University of Strathclyde, Glasgow, UK
Interests: digital manufacturing; AI/ML; digital twins; precision manufacturing
Prof. Dr. Xichun Luo

E-Mail Website
Guest Editor
Centre for Precision Manufacturing, Department of Design, Manufacturing and Engineering Management, University of Strathclyde, Glasgow G1 1XJ, UK
Interests: ultra-precision machining; hybrid micromachining; nanofabrication; digital manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As modern industries continue to evolve, the fields of machine tools, advanced manufacturing, and precision manufacturing have gained paramount importance in driving innovation, efficiency, and quality across various sectors. The manufacturing domain is encountering numerous unforeseen challenges due to stringent quality demands, miniaturization, the emergence of new materials, sustainability concerns, mass customization, and automation requirements. Addressing these challenges is now more relevant than ever. In this context, this Special Issue titled ‘Machine Tools, Advanced Manufacturing and Precision Manufacturing’ aims to explore cutting-edge research, technological advancements, and interdisciplinary approaches that drive the manufacturing domain forward.

This Special Issue aims to provide a platform for fostering knowledge exchange and collaboration among experts from academia and industry by welcoming submissions that delve into topics such as novel machining techniques, micro-nano manufacturing, intelligent automation, precision measurement and control, digital twin technologies, Industry 4.0 applications, and sustainable manufacturing practices. Researchers, academics, and practitioners are encouraged to contribute original research articles, reviews, case studies, and technical notes on recent developments, challenges, and future trends in our proposed topic.

Dr. Abhilash Puthanveettil Madathil
Prof. Dr. Xichun Luo
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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly 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 tools
  • advanced manufacturing
  • precision manufacturing
  • intelligent automation
  • digital twin technologies

Published Papers (2 papers)

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Research

12 pages, 9231 KiB  
Article
Three-Dimensional Printed Attachments: Analysis of Reproduction Accuracy Compared to Traditional Attachments
by Angela Mirea Bellocchio, Elia Ciancio, Ludovica Ciraolo, Serena Barbera and Riccardo Nucera
Appl. Sci. 2024, 14(9), 3837; https://doi.org/10.3390/app14093837 - 30 Apr 2024
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Abstract
Background: The aim of this study was to propose a new 3D printing method for attachment production and compare the reproduction accuracy of traditional attachments with the proposed 3D-printed attachments. Methods: A standardized 3D model attachment was created with the dimensions of 3, [...] Read more.
Background: The aim of this study was to propose a new 3D printing method for attachment production and compare the reproduction accuracy of traditional attachments with the proposed 3D-printed attachments. Methods: A standardized 3D model attachment was created with the dimensions of 3, 2, and 2 mm for the apico-coronal, mesio-distal, and vestibulo-lingual dimensions, respectively. A 3D ideal model of the maxillary arch was used to apply four standardized attachments on the vestibular surface of selected teeth. The obtained model with placed attachments was used to reproduce composite attachments via the conventional method. A transfer template was used to bond with the flow composite resin 3D-printed attachment on a new arch model without attachments. The models with traditional attachments and 3D-printed attachments were scanned and overlapped with the original CAD model with attachments. To assess the attachment precision, vertical and horizontal cutting planes were used on the overlapped models. The outcome selection focused on puff analysis (excess composite material evaluation) and shape analysis (attachment accuracy evaluation). Results: The results indicated that the 3D-printed attachments showed significant differences (p < 0.05) compared to the traditional attachments. The descriptive statistics showed the higher discrepancies compared to the CAD model of the traditionally created attachments in the shape (0.85 mm) and puff dimension (1.02 mm). Conclusion: Custom 3D-printed attachment production is an effective method for achieving greater attachment precision. Full article
(This article belongs to the Special Issue Machine Tools, Advanced Manufacturing and Precision Manufacturing)
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31 pages, 11748 KiB  
Article
Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
by Shen-Yung Lin and Chia-Jen Hsieh
Appl. Sci. 2024, 14(9), 3811; https://doi.org/10.3390/app14093811 - 29 Apr 2024
Viewed by 236
Abstract
This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis, [...] Read more.
This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis, and rotary combined with dual-axis). The results suggest that the rotary (z-axis oscillation) ultrasonic-assisted method may provide better performance. Subsequently, this superior ultrasonic-assisted method was applied both with and without laser locally preheating assistance, respectively. Using a Taguchi orthogonal array, milling process parameters (spindle speed, feed rate, and radial depth of cut) were planned for experiments with the same cutting tool and the workpiece just mentioned above. The surface roughness serves as the objective function while being constrained by cutting-tool life. The characteristics of the smaller-the-better in the Taguchi method were applied to determine the optimal combination of process parameters. Based on the optimal milling process parameters obtained and the superior hybrid-assisted method adopted, milling experiments were repeatedly performed to collect the data on cutting force and cutting-tool wear. Feature engineering was performed on the cutting force signals, and different domain characteristics from both the time and frequency domains were extracted. Hereafter, feature selection by random forest and data standardization were further applied to feature extractions, and the data processing was thus completed. For the processed data, a cutting-tool wear prediction model was constructed by ensemble learning. This method leverages various machine learning regression models, including decision tree, random forest, extremely randomized tree, light gradient boosting machine, extreme gradient boosting, AdaBoost, stochastic gradient descent, support vector regression, linear support vector regression, and multilayer perceptron. After hyper-parameter tuning, the ensemble voting regression prediction was performed based on these ten mentioned models. The experimental results demonstrate that the ensemble voting regression model surpasses the performance of each individual machine learning regression model. In addition, this regression model achieves a coefficient of determination (R2) of 0.94576, a root mean square error (RMSE) of 0.24348, a mean squared error (MSE) of 0.05928, and a mean absolute error (MAE) of 0.18182. Therefore, the ensemble learning approach has been proven to be a feasible and effective method for monitoring cutting-tool wear. Full article
(This article belongs to the Special Issue Machine Tools, Advanced Manufacturing and Precision Manufacturing)
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