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

Deadline for manuscript submissions: 10 August 2025 | Viewed by 10521

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Special Issue Editors

Dr. Abhilash Puthanveettil Madathil

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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

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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
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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

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Keywords

machine tools
advanced manufacturing
precision manufacturing
intelligent automation
digital twin technologies

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Published Papers (9 papers)

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Research

17 pages, 6430 KiB

Open AccessArticle

Performance Investigation of Coated Carbide Tools in Milling Procedures

by Paschalis Charalampous

Appl. Sci. 2025, 15(7), 3765; https://doi.org/10.3390/app15073765 - 29 Mar 2025

Viewed by 245

Abstract

The optimization of the manufacturing conditions in milling processes composes a crucial task for enhancing machining efficiency and extending the tool’s lifespan. This study presents an investigation of the cutting tool’s performance under varying machining parameters via the generation of an experimental dataset that was obtained through laboratory-controlled milling operations. Based on this dataset, artificial intelligence (AI) models, including artificial neural network (ANN), k-nearest neighbors (KNN), and support vector regression (SVR), were developed in order to predict the tool’s life as a function of the milling conditions. Additionally, finite element method (FEM) simulations were conducted to estimate tool wear and analyze the manufacturing process at a numerical level. In particular, FE models were utilized to compute the milling forces and the corresponding developed stress fields, as well as to assess the cutting tool’s performance based on certain machining variables. Furthermore, a comparative analysis between AI-driven forecasts and FEM simulations was performed to evaluate their effectiveness and reliability. The findings provide insights into the advantages and limitations of both methodologies, guiding the optimization of coated carbide tool performance. The outcomes of this study contribute to the advancement of predictive modeling in machining processes, offering a data-driven approach for improved tool wear assessment. Full article

(This article belongs to the Special Issue Machine Tools, Advanced Manufacturing and Precision Manufacturing)

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18 pages, 10827 KiB

Open AccessArticle

Statistical Analysis of Cutting Force and Vibration in Turning X5CrNi18-10 Steel

by Csaba Felhő and Tanuj Namboodri

Appl. Sci. 2025, 15(1), 54; https://doi.org/10.3390/app15010054 - 25 Dec 2024

Viewed by 777

Abstract

X5CrNi18-10 is a corrosion-resistant steel that has become popular in the automotive, marine, food, nuclear, and other industries. Chromium alloyed in the X5CrNi18-10 increases the material’s toughness, which influences the cutting phenomena such as the cutting force and vibration. It is necessary to investigate the effect of the machining parameters on the X5CrNi18-10 turning, particularly the feed, which has significant effects on the cutting phenomena. The objective of this research is to investigate the correlation between the feed and cutting phenomena to improve the product quality, reduce machining disruptions, and optimize the parameters for a low cutting speed and vibration. Statistical analysis has shown promise in identifying the impact of variables using correlation analysis and estimated marginal means plots. This study highlights the findings of the Pearson’s correlation analysis between the feed, active cutting force, and active vibration as well as the estimated marginal means plots between the machining parameters and cutting phenomena. The results indicate that there is a strong correlation between the feed and active cutting force with a coefficient of correlation of 0.688, as well as the feed and active vibration with a coefficient of correlation of 0.697. The estimated marginal means plots indicate that as the cutting speed increases, the value of the active vibration and the active force decreases. Full article

(This article belongs to the Special Issue Machine Tools, Advanced Manufacturing and Precision Manufacturing)

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20 pages, 6092 KiB

Open AccessArticle

Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens

by Emmanouil-Marinos Mantalas, Vasileios D. Sagias, Paraskevi Zacharia and Constantinos I. Stergiou

Appl. Sci. 2025, 15(1), 7; https://doi.org/10.3390/app15010007 - 24 Dec 2024

Cited by 1 | Viewed by 900

Abstract

This paper explores the integration of adaptive neuro-fuzzy inference systems (ANFIS) with additive manufacturing (AM) to enhance the prediction of mechanical properties in 3D-printed components. Despite AM’s versatility in producing complex geometries, achieving consistent mechanical performance remains challenging due to various process parameters and the anisotropic behavior of printed parts. The proposed approach combines the learning capabilities of neural networks with the decision-making strengths of fuzzy logic, enabling the ANFIS to refine printing parameters to improve part quality. Experimental data collected from AM processes are used to train the ANFIS model, allowing it to predict outputs such as stress, strain, and Young’s modulus under various printing parameters values. The predictive performance of the model was assessed with the root mean square error (RMSE) and coefficient of determination (R²) as evaluation metrics. The study initially examined the impact of key parameters on model performance and subsequently compared two fuzzy partitioning techniques—grid partitioning and subtractive clustering—to identify the most effective configuration. The experimental results and analysis demonstrated that ANFIS could dynamically adjust key printing parameters, leading to significant improvements in the prediction accuracy of stress, strain, and Young’s modulus, showcasing its potential to address the inherent complexities of additive manufacturing processes. Full article

(This article belongs to the Special Issue Machine Tools, Advanced Manufacturing and Precision Manufacturing)

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17 pages, 6606 KiB

Open AccessArticle

An Attempt to Establish a Mathematical Model for an Unconventional Worm Gear with Bearings

by Simion Haragâș, Roland Ninacs, Ovidiu Buiga, Lucian Tudose, Alexandru Haragâș, Ioana Monica Sas-Boca and Felicia Aurora Cristea

Appl. Sci. 2024, 14(23), 10833; https://doi.org/10.3390/app142310833 - 22 Nov 2024

Viewed by 955

Abstract

The aim of this paper is to develop a mathematical model for an unconventional worm gear consisting of a globoid worm and a worm wheel where the teeth are bearings. Using rolling elements such the teeth of the worm wheel (ball bearings) transforms the sliding friction to rolling friction during the process of worm gear meshing, improving power. The geometry of the component elements of the gear is analyzed in correlation with its kinematics. After the creation of the mathematical model, it is validated both analytically (through complex graphic representations) and experimentally (by creating, for a particular case, the 3D model and the concrete physical model (prototype) of the gear through 3D printing). Full article

(This article belongs to the Special Issue Machine Tools, Advanced Manufacturing and Precision Manufacturing)

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17 pages, 4938 KiB

Open AccessArticle

Additive Manufacturing of Ceramic Reference Spheres by Stereolithography (SLA)

by Víctor Meana, Pablo Zapico, Eduardo Cuesta, Sara Giganto, Lorenzo Meana and Susana Martínez-Pellitero

Appl. Sci. 2024, 14(17), 7530; https://doi.org/10.3390/app14177530 - 26 Aug 2024

Cited by 2 | Viewed by 1502

Abstract

Additive Manufacturing (AM) is advancing technologically towards the production of components for high-demand mechanical applications with stringent dimensional accuracy, leveraging metallic and ceramic raw materials. The AM process for ceramic components, known as Ultraviolet Laser Stereolithography (SLA), enables the fabrication of unique parts or small batches without substantial investments in molds and dies, and avoids the problems associated with traditional manufacturing, which involves multiple stages and final machining for precision. This study addresses the need to produce reference elements or targets for metrological applications, including verification, adjustment, or calibration of 3D scanners and mid- to high-range optical sensors. Precision spheres are a primary geometry in this context due to their straightforward mathematical definition, facilitating rapid and accurate error detection in equipment. Our objective is to exploit this novel SLA process along with the advantageous optical properties of technical ceramics (such as being white, matte, lightweight, and corrosion-resistant) to materialize these reference objects. Specifically, this work involves the fabrication of alumina hemispheres using SLA. The manufacturing process incorporates four design variables (wall thickness, support shape, fill type, and orientation) and one manufacturing variable (the arrangement of spheres on the printing tray). To evaluate the impact of the design variables, dimensional and geometric parameters (GD&T), including diameters, form errors, and their distribution on the surface of the sphere, have been characterized. These measurements are conducted with high accuracy using a Coordinate Measuring Machine (CMM). The study also examines the influence of these variables in the dimensional and geometric accuracy of the spheres. Correlations between various parameters were identified, specifically highlighting critical factors affecting process precision, such as the position of the piece on the print tray and the wall thickness value. The smallest diameter errors were recorded at the outermost positions of the tray (rear and front), while the smallest shape errors were found at the central position, in both cases with errors in the range of tens of micrometers. In any case, the smallest deformations were observed with the highest wall thickness (2 mm). Full article

(This article belongs to the Special Issue Machine Tools, Advanced Manufacturing and Precision Manufacturing)

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16 pages, 1868 KiB

Open AccessArticle

Hermite Quartic Splines for Smoothing and Sampling a Roughing Curvilinear Spiral Toolpath

by Cédric Leroy, Sylvain Lavernhe and Édouard Rivière-Lorphèvre

Appl. Sci. 2024, 14(17), 7492; https://doi.org/10.3390/app14177492 - 24 Aug 2024

Viewed by 998

Abstract

From an industrial point of view, the milling of

2.5 D

From an industrial point of view, the milling of

2.5 D

cavities is a frequent operation, consuming time and presenting optimization potential, especially through a judicious choice of the tool trajectory. Among the different types of trajectories, some have a general spiral-like aspect and can potentially offer a reduced machining time. They are called curvilinear trajectories and are obtained by interpolation between structure curves, which are the numerical solutions of a partial differential equation. In this case, the machine tool will connect points, and the trajectory will be made up of small segments. While these trajectories exhibit all the necessary qualities on a macroscopic level for rapid tool movement, the tangential discontinuities at a microscopic scale, inherent in the discretization, significantly increase the machining time. This article proposes a method to reparameterize the structure curves of the curvilinear spiral with a set of

C^{2}

connected Hermit quartic spline patches. This creates a smooth toolpath that can be machined at an average feedrate closer to the programmed one and will, de facto, reduce the machining time. This article shows that the proposed method increases on two representative geometries of cavities and toolpath quality indicators, and reduces the milling time from

10 %

18 %

as compared to the PDE curvilinear spiral generation method proposed by Bieterman and Sandström. In addition, the proposed method is suitable for any non-convex pocket, with or without island(s). Full article

(This article belongs to the Special Issue Machine Tools, Advanced Manufacturing and Precision Manufacturing)

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17 pages, 10154 KiB

Open AccessArticle

Calibration of a Hybrid Machine Tool from the Point of View of Positioning Accuracy

by Slobodan Tabakovic, Milan Zeljkovic, Sasa Zivanovic, Alexander Budimir, Zoran Dimic and Aleksandar Kosarac

Appl. Sci. 2024, 14(12), 5275; https://doi.org/10.3390/app14125275 - 18 Jun 2024

Viewed by 1069

Abstract

The development of machine tools in the last twenty years includes, among other things, the application of mechanisms with a non-linear kinematic structure as the mechanical basis of machines. This results in significant improvements in kinematic characteristics and problems related to non-linear dependencies of the accuracy of the drive elements and the realization of movement in the machine’s external coordinates. The paper presents an approach to machine tool calibration based on the original O-X glide mechanism based on the ISO 230-4 standard with the mono- and bi-directional compensation of systematic errors and adaptation to the specifics of the mechanism’s kinematics. A machine tool prototype was designed and built for the research presented in the paper. The obtained results indicate the possibility of applying the existing recommendations and standards for testing the accuracy of machine tools with the need to correct the methodology by using linear and non-linear kinematic structures in machine tools. Full article

(This article belongs to the Special Issue Machine Tools, Advanced Manufacturing and Precision Manufacturing)

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12 pages, 9231 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1405

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, 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

Open AccessArticle

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

Cited by 4 | Viewed by 1351

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, 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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