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Metals
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State-of-the-Art Processing of Metals and Alloys
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State-of-the-Art Processing of Metals and Alloys

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 6982

Special Issue Editor

Dr. Jeong Hoon Ko

E-Mail Website
Guest Editor
Singapore Institute of Manufacturing Technology (SIMTech): Agency for Science, Technology and Research (A*STAR), Singapore
Interests: Metal processing; Digital solutions;Dynamics; Vibration/Chatter; Numerical and analytical models; Robotic processing; AI approaches; FEM; Feed scheduling; Virtual prototyping of vehicle system

Special Issue Information

Dear Colleagues,

Digital solutions are critical to digitize processes and system configurations, control outputs, reduce errors, and improve productivity in precision metal processing industries toward full automation. Dynamic integral errors, thermal issues, stress-induced deformation, unknown metamaterial behavior, process-in loop robotic control, and so on are of major concern to enhance the accuracy and productivity of metal processing.  Numerical modeling and analysis have been advanced for the last decades, and the new approaches for digital solutions are being pursued for a breakthrough in advanced metal processing. The state of art research for numerical and analytical solutions, advanced CAD/CAM/CAE, integrated digital modeling, robotic operations, and artificial intelligence approaches has been key to overcome high competitive costs and tighten quality requirements for smart metal processing. The in-depth academic achievements and contributions in the addressed domains are expected to contribute to this special edition.

This Special Issue of Metals invites experts from around the world to submit papers related to digital solutions and applications for metal processing. Original articles in the domain of digitized numerical analysis, analytical modeling, advanced FEM, CAD/CAM, AI approaches, robotic operations, and so on are welcome to join this special issue. Clear validations with experimental data or benchmarking with other methods are necessary to prove the submitted articles' original contribution.  The edition targets the technical issues in the digitalization approaches for mechanics, dynamics, stress, thermal deformation, metamaterial, quality control, automation, and productivity for metal processing, including traditional and non-traditional ones. The original review and research articles in the addressed domains are also welcome.

Dr. Jeong Hoon Ko
Guest Editor

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. Metals 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 2600 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

  • Metal processing
  • Digital solutions
  • Numerical and analytical models
  • Robotic processing
  • AI approaches

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Published Papers (3 papers)

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Research

14 pages, 3871 KiB  
Article
Machining Stability Categorization and Prediction Using Process Model Guided Machine Learning
by Jeong Hoon Ko
Metals 2022, 12(2), 298; https://doi.org/10.3390/met12020298 - 9 Feb 2022
Cited by 3 | Viewed by 1908
Abstract
The time-domain dynamic process model is used to generate data and guides the stability criteria for machine learning, saving the experimental costs for a number of required data for the metal process. Fourier transformation of vibration data simulated using a dynamic process model [...] Read more.
The time-domain dynamic process model is used to generate data and guides the stability criteria for machine learning, saving the experimental costs for a number of required data for the metal process. Fourier transformation of vibration data simulated using a dynamic process model generates the feature lists including multiple frequencies and amplitudes at each process condition. The feature lists for milling stability are analyzed for training the machine learning algorithm. The amplitude and frequency distributions may change according to the dynamic pattern of the machining stability. The vibration patterns are grouped into stable, chatter, and boundary conditions by performing data training using support vector machines and gradient tree boosting. In the high-speed milling of Al6061-T6 with 6000 to 18,000 RPM and variations of axial and radial depths of cuts, 2400 data sets of the time domain data were trained and tested. Actual experimental tests are carried out for new process conditions with the range of 9890 to 28,470 RPM and 989 to 2847 mm/min. The experimental stability outcomes are compared with predictions from the algorithms. Stability is accurately predicted over new conditions with around 0.9 prediction accuracy, which means the methodology can be used to predict, categorize, and monitor stability in end milling processes. Full article
(This article belongs to the Special Issue State-of-the-Art Processing of Metals and Alloys)
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13 pages, 9507 KiB  
Article
Numerical and Experimental Analysis of Hardening Distortions of Drawpieces Produced in Hot Stamping Process
by Ireneusz Wróbel
Metals 2021, 11(3), 457; https://doi.org/10.3390/met11030457 - 10 Mar 2021
Cited by 1 | Viewed by 2663
Abstract
The paper presents the results of a numerical simulation of the distortions in drawpieces, generated during the production of the drawpieces using the hot stamping method. A division of the distortions is proposed depending on their shape and based on the industrial experience [...] Read more.
The paper presents the results of a numerical simulation of the distortions in drawpieces, generated during the production of the drawpieces using the hot stamping method. A division of the distortions is proposed depending on their shape and based on the industrial experience of the author of this publication, i.e., concerning flexure and torsion (skewing). Numerical simulations of the hot stamping process were performed for the representative drawpieces, in which the hardening distortions can be assigned to the above specified forms. A numerical compensation of the hardening distortions was proposed to obtain, after the compensation process, a drawpiece complying with the requirements concerning shape and dimensional tolerances. The results of the simulations have been confirmed in the course of the experimental studies. Conclusions and recommendations for the analysis of the process were also elaborated. Full article
(This article belongs to the Special Issue State-of-the-Art Processing of Metals and Alloys)
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16 pages, 5162 KiB  
Article
Digitized Stress Function-Based Feed Rate Scheduling for Prevention of Mesoscale Tool Breakage during Milling Hardened Steel
by Yifan Gao, Jeong Hoon Ko and Heow Pueh Lee
Metals 2021, 11(2), 215; https://doi.org/10.3390/met11020215 - 26 Jan 2021
Cited by 1 | Viewed by 1881
Abstract
In this article, a digitized stress function-based feed rate scheduling algorithm is formulated for the prevention of tool breakage while having an optimum material removal rate in mesoscale rough milling of hardened steel. Instead of setting limits to the cutting forces and material [...] Read more.
In this article, a digitized stress function-based feed rate scheduling algorithm is formulated for the prevention of tool breakage while having an optimum material removal rate in mesoscale rough milling of hardened steel. Instead of setting limits to the cutting forces and material removal rates, the presented method regulates the tool’s stresses. A 3D coupled Eulerian-Lagrangian finite element method (FEM) model is used to simulate a 3D chip flow-based stress according to the mesoscale tool’s rotation during cutting of hardened steel. Maximum uncut chip thickness and tool engaging angle of the uncut chip is identified as the fundamental driving factors of tool breakage in down milling configuration. Furthermore, a multiple linear regression model is formed to digitize the stress with two major factors for digitized feed scheduling. The optimum feed rates for each segment along the tool path can be obtained through finite element models and a multiple linear regression model. The feed rate scheduling method is validated through cutting experiments with tool paths of linear and arc segments. In a series of experimental validations, the algorithm demonstrated the capability of reducing the machining time while eliminating cutting tool breakages. Full article
(This article belongs to the Special Issue State-of-the-Art Processing of Metals and Alloys)
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