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Modeling, Optimization and Design Method of Metal Manufacturing Processes

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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: closed (25 October 2022) | Viewed by 22139

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Prof. Dr. Guoqing Zhang

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School of Mechanical Engineering, Shenzhen University, Lihu Campus, Xueyuan Avenue, Nanshan District, Shenzhen 518060, China
Interests: ultra-precision machining process and technology; diamond tool; smart machining; machining equipment; advanced cutting technology; design of instruments and equipment for in extreme environments
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Dr. Zejia Zhao

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Guest Editor

College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Interests: ultra-precision machining; micro or nano scale machining; cutting of difficult-to-cut materials; electropusling treatment; numerical modeling; material science
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Dr. Wai Sze YIP

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Guest Editor

Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
Interests: ultra-precision machining technology; ultra-precision machining of difficult-to-cut materials; sustainable precision machining; sustainability development of precision manufacturing
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Special Issue Information

Dear Colleagues,

Metal manufacturing is a fundamental, indispensable technology to process raw metals into desired products, which significantly promotes the development of industry and our society. Though a large amount of research has been conducted to investigate the metal manufacturing processes, there are still some challenges due to the lack of comprehensive understanding of material deformation during the manufacturing process. To meet today’s high demands for the cost and efficiency of manufacturing, efforts should be devoted to exploring appropriate methods to design, model and optimize the metal manufacturing processes.

We are pleased to invite you to publish original research or review articles in the new Special Issue entitled “Modeling, Optimization and Design Method of Metal Manufacturing Processes”. Meanwhile, may we kindly invite you to promote the journal among your colleagues, and invite them to publish in this Special Issue.

This Special Issue aims to study some essential issues in metal manufacturing processes. Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Finite element modelling of manufacturing;
Molecular dynamic studies of manufacturing;
Optimization of manufacturing processes;
Novel design methods of manufacturing;
Conventional machining and precision machining;
Non-conventional machining;
Additive manufacturing;
Forming, bending and welding.

We look forward to receiving your contributions.

Dr. Guoqing Zhang
Dr. Zejia Zhao
Dr. Wai Sze YIP
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. Processes 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

modeling
optimization
design
difficult-to-deform metals
machining
forming
additive manufacturing
non-conventional machining

Published Papers (12 papers)

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Editorial

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3 pages, 170 KiB

Open AccessEditorial

Special Issue on “Modeling, Optimization and Design Method of Metal Manufacturing Processes”

by Guoqing Zhang, Zejia Zhao and Wai Sze YIP

Processes 2022, 10(11), 2461; https://doi.org/10.3390/pr10112461 - 21 Nov 2022

Viewed by 928

Abstract

Metal manufacturing processes are essential techniques to convert raw materials into desired metal products, which contributes significantly to the growth of industry and our society [...] Full article

(This article belongs to the Special Issue Modeling, Optimization and Design Method of Metal Manufacturing Processes)

Research

Jump to: Editorial, Review

18 pages, 1977 KiB

Open AccessArticle

A Moving Window Double Locally Weighted Extreme Learning Machine on an Improved Sparrow Searching Algorithm and Its Case Study on a Hematite Grinding Process

by Huating Liu, Jiayang Dai and Xingyu Chen

Processes 2023, 11(1), 169; https://doi.org/10.3390/pr11010169 - 05 Jan 2023

Viewed by 1000

Abstract

In this paper, a double locally weighted extreme learning machine model based on a moving window is developed to realize process modeling. To improve model performances, an improved sparrow-searching algorithm is proposed to optimize the parameters of the proposed model. The effectiveness of the proposed model and algorithm are verified by data from a hematite grinding process. The experimental results show that the proposed algorithm can significantly improve the global search ability and convergence speed in the parameter optimization of the proposed model. The proposed model can correctly estimate the grinding particle size which is expected to be applied to other complex industries. Full article

(This article belongs to the Special Issue Modeling, Optimization and Design Method of Metal Manufacturing Processes)

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25 pages, 8083 KiB

Open AccessArticle

Numerical Prediction of the Performance of Chamfered and Sharp Cutting Tools during Orthogonal Cutting of AISI 1045 Steel

by Zakaria Ahmed M. Tagiuri, Thien-My Dao, Agnes Marie Samuel and Victor Songmene

Processes 2022, 10(11), 2171; https://doi.org/10.3390/pr10112171 - 23 Oct 2022

Cited by 6 | Viewed by 1418

Abstract

This paper presents a numerical investigation of the effects of chamfered and sharp cemented carbide tools using finite element method-based DEFORM-2D software and cutting parameters on different machining characteristics during the orthogonal cutting of AISI 1045 steel. The objective is to study the interactions between chamfer width, chamfer angle, sharp angle and the cutting speed and feed rate on the cutting temperature, effective stress and wear depth. These effects were investigated statistically using the analysis of variance (ANOVA) test. The obtained numerical results showed that for the chamfer tool, high values of temperature, stress and wear depth were obtained for chamfer widths of 0.35 mm and 0.45 mm. In terms of combined influences, for the cutting temperature and stress, a strong interaction between the cutting speed and chamfer width was obtained. For the sharp tool design, and in terms of temperature, strong interactions are mostly observed between cutting speeds and feed rates. The ANOVA showed that for both chamfer and sharp tools, the feed rate, the cutting speed and their interactions are the most significant parameters that influence temperature and stress. Full article

(This article belongs to the Special Issue Modeling, Optimization and Design Method of Metal Manufacturing Processes)

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16 pages, 2094 KiB

Open AccessArticle

Temperature Prediction Model for a Regenerative Aluminum Smelting Furnace by a Just-in-Time Learning-Based Triple-Weighted Regularized Extreme Learning Machine

by Xingyu Chen, Jiayang Dai and Yasong Luo

Processes 2022, 10(10), 1972; https://doi.org/10.3390/pr10101972 - 30 Sep 2022

Cited by 3 | Viewed by 1218

Abstract

In a regenerative aluminum smelting furnace, real-time liquid aluminum temperature measurements are essential for process control. However, it is often very expensive to achieve accurate temperature measurements. To address this issue, a just-in-time learning-based triple-weighted regularized extreme learning machine (JITL-TWRELM) soft sensor modeling method is proposed for liquid aluminum temperature prediction. In this method, a weighted JITL method (WJITL) is adopted for updating the online local models to deal with the process time-varying problem. Moreover, a regularized extreme learning machine model considering both the sample similarities and the variable correlations was established as the local modeling method. The effectiveness of the proposed method is demonstrated in an industrial aluminum smelting process. The results show that the proposed method can meet the requirements of prediction accuracy of the regenerative aluminum smelting furnace. Full article

(This article belongs to the Special Issue Modeling, Optimization and Design Method of Metal Manufacturing Processes)

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14 pages, 12628 KiB

Open AccessArticle

Experimental Investigation on the Machinability of PCBN Chamfered Tool in Dry Turning of Gray Cast Iron

by Ganggang Yin, Jianyun Shen, Ze Wu, Xian Wu and Feng Jiang

Processes 2022, 10(8), 1547; https://doi.org/10.3390/pr10081547 - 07 Aug 2022

Cited by 8 | Viewed by 1327

Abstract

Polycrystalline cubic boron nitride (PCBN) tools are widely used for hard machining of various ferrous materials. The edge structure of the PCBN cutting tool greatly affects the machining performance. In this paper, dry turning experiments were conducted on gray cast iron with a PCBN chamfered tool. Both the cutting temperature and the cutting force were measured, and then the surface quality and tool wear mechanisms were analyzed in detail. It was found that the cutting temperature and cutting force increased with the increase in feed rate, depth of cut, and cutting speed. The surface roughness firstly decreased, and then increased with an increase in feed rate. The minimum surface roughness was obtained with a feed rate of 0.15 mm/r which exceeded the tool chamfer width. The PCBN tool wear mode was mainly micro notches on the rake face and micro chipping on the tool chamfer, while the adhesion wear mechanism was the main tool wear mechanism. Full article

(This article belongs to the Special Issue Modeling, Optimization and Design Method of Metal Manufacturing Processes)

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13 pages, 5467 KiB

Open AccessArticle

Effects of Coating Parameters of Hot Filament Chemical Vapour Deposition on Tool Wear in Micro-Drilling of High-Frequency Printed Circuit Board

by Fung Ming Kwok, Zhanwen Sun, Wai Sze Yip, Kwong Yu David Kwok and Suet To

Processes 2022, 10(8), 1466; https://doi.org/10.3390/pr10081466 - 27 Jul 2022

Cited by 4 | Viewed by 1478

Abstract

High-frequency and high-speed printed circuit boards (PCBs) are made of ceramic particles and anisotropic fibres, which are difficult to machine. In most cases, severe tool wear occurs when drilling high-frequency PCBs. To protect the substrate of the drills, diamond films are typically fabricated on the drills using hot filament chemical vapour deposition (HFCVD). This study investigates the coating characteristics of drills with respect to different HFCVD processing parameters and the coating characteristics following wear from machining high-frequency PCBs. The results show that the methane concentration, processing time and temperature all have a significant effect on the grain size and coating thickness of the diamond film. The grain size of the film obviously decreases as does the methane concentration, while the coating thickness increases. By drilling high-frequency PCBs with drills with nanocrystalline and microcrystalline grain sizes, it is discovered that drills with nanocrystalline films have a longer tool life than drills with microcrystalline films. The maximum length of the flank wear of the nanocrystalline diamond-coated drill is nearly 90% less than microcrystalline diamond-coated tools. Moreover, drills with thinner films wear at a faster rate than drills with thicker films. The findings highlight the effects of HFCVD parameters for coated drills that process high-frequency PCBs, thereby contributing to the production of high quality PCBs for industry and academia. Full article

(This article belongs to the Special Issue Modeling, Optimization and Design Method of Metal Manufacturing Processes)

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14 pages, 7187 KiB

Open AccessFeature PaperArticle

Influence of Material Microstructure on Machining Characteristics of OFHC Copper C102 in Orthogonal Micro-Turning

by Chuan-Zhi Jing, Ji-Lai Wang, Xue Li, Yi-Fei Li and Lu Han

Processes 2022, 10(4), 741; https://doi.org/10.3390/pr10040741 - 11 Apr 2022

Cited by 2 | Viewed by 1314

Abstract

Micro-cutting is different from conventional cutting in its mechanics. The workpiece material is not considered to be homogeneous in the micro-cutting process. As a result, it is critical to comprehend how microstructure affects surface integrity, cutting forces, and chip formation. In this paper, we experimented with micro-turning on oxygen-free high-conductivity (OFHC) copper with different microstructures after annealing. Feed rate parameters were smaller than, larger than, and equal to the grain size, respectively. Experimental results show that when the feed rates are equivalent to the grain size, the surface roughness of the machined surface is low and the width of the flake structure on the free surface of chips is minimal, and the explanations for these occurrences are connected to dislocation slip. Full article

(This article belongs to the Special Issue Modeling, Optimization and Design Method of Metal Manufacturing Processes)

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9 pages, 2826 KiB

Open AccessFeature PaperArticle

Characterization of Refining the Morphology of Al–Fe–Si in A380 Aluminum Alloy due to Ca Addition

by Meng Wang, Yu Guo, Hongying Wang and Shengsheng Zhao

Processes 2022, 10(4), 672; https://doi.org/10.3390/pr10040672 - 30 Mar 2022

Cited by 3 | Viewed by 1864

Abstract

Aluminum–silicon (Al–Si) alloys are the most commonly cast aluminum alloys. Fe is the most deleterious element for Al–Si die casting alloys, as its existence causes the precipitation of substantial intermetallics that result in the unsatisfactory mechanical performance of the alloy, such as its ductility. Hence, controlling the morphology and formation of the AlFeSi phase, particularly the β-AlFeSi phase, is vital for improving the ductility of Al–Si die casting alloys. Herein, Ca was added to the A380 alloy, and the morphological changes resulting from the influence of Ca on the AlFeSi phase were characterized. The outcomes revealed that according to different cooling rates, specific amounts of Ca addition (0.01–0.1 wt.%) were capable of refining α-AlFeSi and β-AlFeSi morphology and transforming the β-AlFeSi phase into α-AlFeSi. Moreover, Ca addition could also modify eutectic silicon. The transformation mechanism and refining role of Ca in AlFeSi and the different morphologies of Al₂CaSi₂ were analyzed. Full article

(This article belongs to the Special Issue Modeling, Optimization and Design Method of Metal Manufacturing Processes)

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12 pages, 6766 KiB

Open AccessArticle

The Influence of Size Effect to Deformation Mechanism of C5131 Bronze Structures of Negative Poisson’s Ratio

by Jiaqi Ran, Gangping Chen, Fuxing Zhong, Li Xu, Teng Xu and Feng Gong

Processes 2022, 10(4), 652; https://doi.org/10.3390/pr10040652 - 28 Mar 2022

Cited by 2 | Viewed by 2075

Abstract

3D auxetic structures, which present negative Poisson’s ratio in the uniaxial compression deformation, is an ideal artificial material for meta-implants because of its lightweight, good material property and suitable porosity for bone recovery compared with conventional meta-biomaterials. Selective laser melting (SLM) is commonly used to produce metallic 3D auxetic structures but limited by the melting temperature and reflect rate of the material, and micro assembled (MA) structures is an alternative manufacturing process. However, the influence of size effect in 3D auxetic structures and the difference of the constitutive model of 3D auxetic structure produced by SLM and MA have not been discussed. In tandem of this, the mechanical property comparison of 3D auxetic structures produced by SLM and MA is conducted and a structural surface layer model for 3D auxetic structures is proposed. The result indicated that both SLM and MA structure can achieve auxetic effect. It is found that the Poisson’s ratio of the SLM and MA structures decrease when increasing the size factor of the structure, and the negative Poisson’s ratio effect is more obvious when the Young’s modulus is relatively small. FE simulation result of Poisson’s ratio is closer to experimental result of MA structures due to complexity of 3D auxetic structures. This paper thus provides a relatively helpful constitutive model for the prediction of the mechanical behavior of 3D auxetic structure. Full article

(This article belongs to the Special Issue Modeling, Optimization and Design Method of Metal Manufacturing Processes)

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20 pages, 2793 KiB

Open AccessFeature PaperArticle

Steelmaking Process Optimised through a Decision Support System Aided by Self-Learning Machine Learning

by Doru Stefan Andreiana, Luis Enrique Acevedo Galicia, Seppo Ollila, Carlos Leyva Guerrero, Álvaro Ojeda Roldán, Fernando Dorado Navas and Alejandro del Real Torres

Processes 2022, 10(3), 434; https://doi.org/10.3390/pr10030434 - 22 Feb 2022

Cited by 4 | Viewed by 1816

Abstract

This paper presents the application of a reinforcement learning (RL) algorithm, concretely Q-Learning, as the core of a decision support system (DSS) for a steelmaking subprocess, the Composition Adjustment by Sealed Argon-bubbling with Oxygen Blowing (CAS-OB) from the SSAB Raahe steel plant. Since many CAS-OB actions are selected based on operator experience, this research aims to develop a DSS to assist the operator in taking the proper decisions during the process, especially less experienced operators. The DSS is intended to supports the operators in real-time during the process to facilitate their work and optimise the process, improving material and energy efficiency, thus increasing the operation’s sustainability. The objective is that the algorithm learns the process based only on raw data from the CAS-OB historical database, and on rewards set according to the objectives. Finally, the DSS was tested and validated by a developer engineer from the CAS-OB steelmaking plant. The results show that the algorithm successfully learns the process, recommending the same actions as those taken by the operator 69.23% of the time. The algorithm also suggests a better option in 30.76% of the remaining cases. Thanks to the DSS, the heat rejection due to wrong composition is reduced by 4%, and temperature accuracy is increased to 83.33%. These improvements resulted in an estimated reduction of 2% in CO₂ emissions, 0.5% in energy consumption and 1.5% in costs. Additionally, actions taken based on the operator’s experience are incorporated into the DSS knowledge, facilitating the integration of operators with lower experience in the process. Full article

(This article belongs to the Special Issue Modeling, Optimization and Design Method of Metal Manufacturing Processes)

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Review

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30 pages, 19861 KiB

Open AccessReview

Recent Advances in the Equal Channel Angular Pressing of Metallic Materials

by Lang Cui, Shengmin Shao, Haitao Wang, Guoqing Zhang, Zejia Zhao and Chunyang Zhao

Processes 2022, 10(11), 2181; https://doi.org/10.3390/pr10112181 - 25 Oct 2022

Cited by 7 | Viewed by 2225

Abstract

Applications of a metallic material highly depend on its mechanical properties, which greatly depend on the material’s grain sizes. Reducing grain sizes by severe plastic deformation is one of the efficient approaches to enhance the mechanical properties of a metallic material. In this paper, severe plastic deformation of equal channel angular pressing (ECAP) will be reviewed to illustrate its effects on the grain refinement of some common metallic materials such as titanium alloys, aluminum alloys, and magnesium alloys. In the ECAP process, the materials can be processed severely and repeatedly in a designed ECAP mold to accumulate a large amount of plastic strain. Ultrafine grains with diameters of submicron meters or even nanometers can be achieved through severe plastic deformation of the ECAP. In detail, this paper will give state-of-the-art details about the influences of ECAP processing parameters such as passes, temperature, and routes on the evolution of the microstructure of metallic materials. The evolution of grain sizes, grain boundaries, and phases of different metallic materials during the ECAP process are also analyzed. Besides, the plastic deformation mechanism during the ECAP process is discussed from the perspectives of dislocation slipping and twinning. Full article

(This article belongs to the Special Issue Modeling, Optimization and Design Method of Metal Manufacturing Processes)

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42 pages, 16502 KiB

Open AccessReview

A Review of the Preparation, Machining Performance, and Application of Fe-Based Amorphous Alloys

by Zexuan Huo, Guoqing Zhang, Junhong Han, Jianpeng Wang, Shuai Ma and Haitao Wang

Processes 2022, 10(6), 1203; https://doi.org/10.3390/pr10061203 - 16 Jun 2022

Cited by 8 | Viewed by 3690

Abstract

Amorphous alloy is an emerging metal material, and its unique atomic arrangement brings it the excellent properties of high strength and high hardness, and, therefore, have attracted extensive attention in the fields of electronic information and cutting-edge products. Their applications involve machining and forming, make the machining performance of amorphous alloys being a research hotspot. However, the present research on amorphous alloys and their machining performance is widely focused, especially for Fe-based amorphous alloys, and there lacks a systematic review. Therefore, in the present research, based on the properties of amorphous alloys and Fe-based amorphous alloys, the fundamental reason and improvement method of the difficult-to-machine properties of Fe-based amorphous alloys are reviewed and analyzed. Firstly, the properties of amorphous alloys are summarized, and it is found that crystallization and high temperature in machining are the main reasons for difficult-to-machine properties. Then, the unique properties, preparation and application of Fe-based amorphous alloys are reviewed. The review found that the machining of Fe-based amorphous alloys is also deteriorated by extremely high hardness and chemical tool wear. Tool-assisted machining, low-temperature lubrication assisted machining, and magnetic field-assisted machining can effectively improve the machining performance of Fe-based amorphous alloys. The combination of assisted machining methods is the development trend in machining Fe-based amorphous alloys, and even amorphous alloys in the future. The present research provides a systematic summary for the machining of Fe-based amorphous alloys, which would serve as a reference for relevant research. Full article

(This article belongs to the Special Issue Modeling, Optimization and Design Method of Metal Manufacturing Processes)

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