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Machines
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Precision Engineering in Manufacturing: Challenges and Future
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Precision Engineering in Manufacturing: Challenges and Future

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 7350

Special Issue Editor

Prof. Dr. Qingshun Bai

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: ultra-precision machining and micro/nano-manufacturing; ultra-clean manufacturing theory and technology; graphene basic theory and application; design and manufacture of precision machinery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision engineering is one of the fastest-growing subfields of manufacturing research. However, there are still some bottlenecks and challenges that need to be overcome. Due to these difficulties, the technology-driven features and leading roles of precision manufacturing processes are not prominent. These difficulties can be overcome by creating novel manufacturing techniques that could potentially offer greater design independence, multi-material processing capabilities, and high throughput as a complement to the current manufacturing framework. All these fit well with ‘Machines’ scope: mechanical engineering, industrial design, mechanical systems, machines and related components, and machine design.

This is a call for papers for a Special Issue on "Precision Engineering in Manufacturing: Challenges and Future". This special issue aims to present state-of-the-art development in precision engineering research focusing on computational and experimental approaches for designing, analyzing, optimizing, developing and promoting the manufacturing process. It also critically examines their future research directions and development trends in the next decade and beyond to highlight potential solutions to current challenges and explore better ways to overcome process limitations. These concepts will serve as the foundation for the discussions of breakthroughs and advances that can propel the growth of precision manufacturing in this special issue. The topics of interest include but are not limited to the following:

  • Precision machining techniques and methodology
  • Design & analysis of precision engineering in manufacturing
  • Ultra-precision machining and Micro/Nano technology
  • Manufacturing systems and machine tools design
  • Machining mechanism and process
  • Measurement technology in precision engineering
  • Additive manufacturing and hybrid additive/subtractive manufacturing
  • Smart manufacturing technology
  • Functional materials and advanced manufacturing in precision engineering

It is our pleasure to invite you to submit a manuscript related to any topics mentioned above for this Special Issue. Full papers, communications, and reviews are all welcome.

Prof. Dr. Qingshun Bai
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. Machines 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

  • precision machining techniques and methodology
  • design & analysis of precision engineering in manufacturing
  • ultra-precision machining and micro/nano technology
  • manufacturing systems and machine tools design
  • machining mechanism and process
  • measurement technology in precision engineering
  • additive manufacturing and hybrid additive/subtractive manufacturing
  • smart manufacturing technology
  • functional materials and advanced manufacturing in precision engineering

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Related Special Issue

Published Papers (4 papers)

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Research

16 pages, 9283 KiB  
Article
A Toolpath Planning Method for Optical Freeform Surface Ultra-Precision Turning Based on NURBS Surface Curvature
by Xuchu Wang, Qingshun Bai, Siyu Gao, Liang Zhao and Kai Cheng
Machines 2023, 11(11), 1017; https://doi.org/10.3390/machines11111017 - 9 Nov 2023
Cited by 4 | Viewed by 1964
Abstract
As the applications for freeform optical surfaces continue to grow, the need for high-precision machining methods is becoming more and more of a necessity. Different toolpath strategies for the ultra-high precision turning of freeform surfaces can have a significant impact on the quality [...] Read more.
As the applications for freeform optical surfaces continue to grow, the need for high-precision machining methods is becoming more and more of a necessity. Different toolpath strategies for the ultra-high precision turning of freeform surfaces can have a significant impact on the quality of the machined surfaces. This paper presents a novel toolpath planning method for ultra-precision slow tool servo diamond turning based on the curvature of freeform surfaces. The method analyzes the differential geometric properties of freeform surfaces by reconstructing NURBS freeform surfaces. A mathematical model is constructed based on the parameters of different positions of the freeform surface, toolpath parameters, and tool residual height. Appropriate toolpath parameters can be calculated to generate the optical freeform ultra-precision slow tool servo diamond turning toolpath. Compared with the toolpaths generated by the traditional Archimedes spiral method, the ultra-precision slow tool servo diamond turning toolpath planning method proposed in this paper can generate more uniform toolpaths on the freeform surfaces and keep the residual tool height within a small range. Full article
(This article belongs to the Special Issue Precision Engineering in Manufacturing: Challenges and Future)
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13 pages, 5619 KiB  
Article
A Theoretical and Experimental Identification with Featured Structures for Crucial Position-Independent Geometric Errors in Ultra-Precision Machining
by Li Zhang and Shaojian Zhang
Machines 2023, 11(9), 909; https://doi.org/10.3390/machines11090909 - 14 Sep 2023
Cited by 1 | Viewed by 1013
Abstract
In ultra-precision machining (UPM), position-independent geometric errors (PIGEs), i.e., squareness errors, have a crucial impact upon the form accuracy of a machined surface. Accordingly, more research work has been conducted in PIGE identification, to improve the form accuracy. However, the general identification methods [...] Read more.
In ultra-precision machining (UPM), position-independent geometric errors (PIGEs), i.e., squareness errors, have a crucial impact upon the form accuracy of a machined surface. Accordingly, more research work has been conducted in PIGE identification, to improve the form accuracy. However, the general identification methods were developed without consideration of the specific squareness errors for crucial PIGEs under the form errors of the machining process. Therefore, a new method with featured structures was proposed, to identify crucial PIGEs in UPM. Firstly, a volumetric error model was developed for PIGEs, to discuss the relationship between squareness errors and their resulting machining form errors. Secondly, following the developed model, some featured structures have been proposed with their machining form errors, to significantly indicate crucial PIGEs. Finally, a series of UPM and measuring experiments were conducted for the featured structures, and then their machining form errors were measured and extracted with specific squareness errors for the identification of crucial PIGEs. The theoretical and experimental results revealed that the proposed method is simple and efficient with the featured structures to accurately identify crucial PIGEs in UPM. Significantly, the study offers a deep insight into high-quality fabrication in UPM. Full article
(This article belongs to the Special Issue Precision Engineering in Manufacturing: Challenges and Future)
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23 pages, 7201 KiB  
Article
Multi-Objective Optimization of AISI P20 Mold Steel Machining in Dry Conditions Using Machine Learning—TOPSIS Approach
by Adel T. Abbas, Neeraj Sharma, Zeyad A. Alsuhaibani, Abhishek Sharma, Irfan Farooq and Ahmed Elkaseer
Machines 2023, 11(7), 748; https://doi.org/10.3390/machines11070748 - 18 Jul 2023
Cited by 5 | Viewed by 1623
Abstract
In the present research, AISI P20 mold steel was processed using the milling process. The machining parameters considered in the present work were speed, depth of cut (DoC), and feed (F). The experiments were designed according to an L27 orthogonal array; therefore, [...] Read more.
In the present research, AISI P20 mold steel was processed using the milling process. The machining parameters considered in the present work were speed, depth of cut (DoC), and feed (F). The experiments were designed according to an L27 orthogonal array; therefore, a total of 27 experiments were conducted with different settings of machining parameters. The response parameters investigated in the present work were material removal rate (MRR), surface roughness (Ra, Rt, and Rz), power consumption (PC), and temperature (Temp). The machine learning (ML) approach was implemented for the prediction of response parameters, and the corresponding error percentage was investigated between experimental values and predicted values (using the ML approach). The technique for order of preference by similarity to ideal solution (TOPSIS) approach was used to normalize all response parameters and convert them into a single performance index (Pi). An analysis of variance (ANOVA) was conducted using the design of experiments, and the optimized setting of machining parameters was investigated. The optimized settings suggested by the integrated ML–TOPSIS approach were as follows: speed, 150 m/min; DoC, 1 mm; F, 0.06 mm/tooth. The confirmation results using these parameters suggested a close agreement and confirmed the suitability of the proposed approach in the parametric evaluation of a milling machine while processing P20 mold steel. It was found that the maximum percentage error between the predicted and experimental values using the proposed approach was 3.43%. Full article
(This article belongs to the Special Issue Precision Engineering in Manufacturing: Challenges and Future)
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17 pages, 3843 KiB  
Article
Effect of Wiper Edge Geometry on Machining Performance While Turning AISI 1045 Steel in Dry Conditions Using the VIKOR-ML Approach
by Adel T. Abbas, Neeraj Sharma, Mahmoud S. Soliman, Magdy M. El Rayes, Rakesh Chandmal Sharma and Ahmed Elkaseer
Machines 2023, 11(7), 719; https://doi.org/10.3390/machines11070719 - 6 Jul 2023
Cited by 4 | Viewed by 1509
Abstract
AISI 1045 can be machined well in all machining operations, namely drilling, milling, turning, broaching and grinding. It has many applications, such as crankshafts, rollers, spindles, shafts, and gears. Wiper geometry has a great influence on cutting forces (Fr, Ff [...] Read more.
AISI 1045 can be machined well in all machining operations, namely drilling, milling, turning, broaching and grinding. It has many applications, such as crankshafts, rollers, spindles, shafts, and gears. Wiper geometry has a great influence on cutting forces (Fr, Ff, Fc and R), temperature, material removal rate (MRR) and surface roughness (Ra). Wiper inserts are used to achieve good surface quality and avoid the need to buy a grinding machine. In this paper, an optimization-based investigation into previously reported results for Taguchi’s based L27 orthogonal array experimentations was conducted to further examine effect of the edge geometry on the turning performance of AISI 1045 steel in dry conditions. Three input parameters used in current research include the cutting speed (Vc), feed (f) and depth of cut (ap), while performance measures in this research were Ra, Fr, Ff, Fc, R, temperature (temp) and MRR. The Vise Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was used to normalize and convert all the performance measures to a single response known as the VIKOR-based performance index (Vi). The machine learning (ML) approach was used for the prediction and optimization of the input variables. A correlation plot is developed between the input variable and Vi using the ML approach. The optimized setting suggested by Vi-ML is Vc: 160 m/min; ap: 1 mm and f: 0.135 mm/rev, and the corresponding value of Vi was 0.2883, while the predicted values of Ra, Fr, Ff, Fc, R, temp and MRR were 2.111 µm, 43.85 N, 159.33 N, 288.13 N, 332,16 N, 554.4 °C and 21,600 mm3/min, respectively. Full article
(This article belongs to the Special Issue Precision Engineering in Manufacturing: Challenges and Future)
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