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Machines
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Advanced Manufacturing and Quality Control for Engines
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Advanced Manufacturing and Quality Control for Engines

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electrical Machines and Drives".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 6324

Special Issue Editor

Prof. Dr. Sun Jin

E-Mail Website
Guest Editor
Institute of Manufacturing for Thin-walled Structures, Shanghai Jiaotong University, Shanghai 200241, China
Interests: intelligent manufacturing; manufacturing quality control; assembly technology development; industrial application

Special Issue Information

Dear Colleagues,

Engines, such as vehicle engines, aircraft engines, gas turbines, and so on, are the core components of power machines, and their manufacturing quality directly affects the performance and service life of the entire machine. Many scholars have studied engine performance indicators, and the application of these research results has greatly improved the manufacturing quality of engines in recent decades. Therefore, advanced engine manufacturing and quality control technologies play a vital role in improving engine quality and the application performance.

The quality improvement of engines is closely related to the processing quality and assembly accuracy of each component as well as to error detection and compensation control in the manufacturing process. Therefore, some manufacturing quality improvement methods, such as establishing a multi-process machining error transfer model through 3D-tolerant design, carrying out error detection and compensation, and manufacturing quality control, have been applied to improve the manufacturing quality of engines.

However, the analysis and calculation process of the existing dimensional tolerance design technology for engines is relatively complicated, and the reliability of the results still cannot meet the requirements of high-precision products, and it is difficult to analyze the uncertain dimensions (such as flexible parts) in product dimensions. In order to further improve product manufacturing accuracy and production efficiency, it is necessary to further improve the prediction accuracy of the analytical model and the adaptability of the error control technology to industrial production. By analyzing and predicting the large amount of data obtained during the product manufacturing process, the manufacturing quality of engines can be further improved.

The main objective of this Special Issue is to create a platform for scientists, engineers, and practitioners through which they can share their latest theoretical and technological results and discuss several issues to be considered as directions for future research in the field of error modeling and in the quality improvement of different kinds of engines, especially vehicle engines and aircraft engines. The papers to be published in this Special Issue are expected to provide recent results in advanced tolerance analysis, error modeling, and quality control, especially for cross-fertilizations between the fields of advanced error modeling and quality improvement. Papers containing comparative results on advanced error modeling and quality improvement are particularly welcome.

Prof. Dr. Sun Jin
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

  • intelligent manufacturing method and technology
  • process modeling
  • quality inspection and control
  • tolerance design and optimization
  • big data

Published Papers (6 papers)

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Research

13 pages, 2514 KiB  
Article
Investigating the Distribution of Flatness Measurements in Battery Manufacturing through Empirical Investigation and Statistical Theory
by Hangyu Li and Sun Jin
Machines 2023, 11(7), 723; https://doi.org/10.3390/machines11070723 - 08 Jul 2023
Viewed by 800
Abstract
The battery is an important part of the new energy electric vehicle, and the control of the flatness of its side plate/bottom plate is the key to quality improvement in mass production. However, there are few pieces of research on the flatness distribution [...] Read more.
The battery is an important part of the new energy electric vehicle, and the control of the flatness of its side plate/bottom plate is the key to quality improvement in mass production. However, there are few pieces of research on the flatness distribution form at present, and the distribution form is often assumed to be a normal distribution, which leads to a significant deviation between the tolerance design and quality control of the flatness and the reality. This paper establishes a statistical model of flatness distribution, its theoretical distribution form is deduced as a normal range distribution, and then the experimental data of the flatness distribution are collected to verify this conclusion. Determining the flatness distribution form has practical effects on improving manufacturing quality and reducing costs in battery manufacturing. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Quality Control for Engines)
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17 pages, 8263 KiB  
Article
Experimental Investigation and Modeling of Force-Induced Surface Errors for the Robot-Assisted Milling Process
by Yongqiao Jin, Qunfei Gu, Shun Liu and Changqi Yang
Machines 2023, 11(6), 655; https://doi.org/10.3390/machines11060655 - 18 Jun 2023
Cited by 1 | Viewed by 1095
Abstract
A series of experiments were performed aiming at controlling milling force-induced surface errors in the robot-assisted milling process, for the sub-area of the multi-stiffener reinforced inner wall of complex cylindrical thin-walled casting parts, to investigate the relationship between surface errors, milling forces, and [...] Read more.
A series of experiments were performed aiming at controlling milling force-induced surface errors in the robot-assisted milling process, for the sub-area of the multi-stiffener reinforced inner wall of complex cylindrical thin-walled casting parts, to investigate the relationship between surface errors, milling forces, and robot-assisted milling parameters. Firstly, based on the design of experiments (DoE) method, milling forces and surface errors were investigated based on a series of experiments with different groups of milling parameters. Secondly, the modeling of milling forces, surface errors, and milling parameters was realized by means of response surface methodology (RSM), then the parametric expression was obtained of the robot-assisted milling process. Finally, the parameters of the milling process toward the surface error were obtained based on an evolutionary algorithm. The results show that the surface errors are different for the different milling styles of down milling and up milling. In up milling processes, the surface errors are positive, and the actual material removal amounts are generally higher than the nominal ones, while negative in down milling processes. The surface errors induced by milling forces can be effectively controlled and reduced using process optimization in the robot-assisted milling process, while maintaining relatively high milling forces and high machining efficiency. This provides theoretical support for industry applications. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Quality Control for Engines)
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18 pages, 4927 KiB  
Article
Experimental Study and GRNN Modeling of Shrinkage Characteristics for Wax Patterns of Gas Turbine Blades Considering the Influence of Complex Structures
by Changhui Liu, Chenghong Jiang, Zhenfeng Zhou, Fei Li, Donghong Wang and Sansan Shuai
Machines 2023, 11(6), 645; https://doi.org/10.3390/machines11060645 - 13 Jun 2023
Viewed by 826
Abstract
With the continuous increase in power demand in aerospace, shipping, electricity, and other industries, a series of manufacturing requirements such as high precision, complex structure, and thin wall have been put forward for gas turbines. Gas turbine blades are the key parts of [...] Read more.
With the continuous increase in power demand in aerospace, shipping, electricity, and other industries, a series of manufacturing requirements such as high precision, complex structure, and thin wall have been put forward for gas turbines. Gas turbine blades are the key parts of the gas turbine. Their manufacturing accuracy directly affects the fuel economy of the gas turbine. Thus, how to improve the manufacturing accuracy of gas turbine blades has always been a hot research topic. In this study, we perform a quantitative study on the correlation between process parameters and the overall wax pattern shrinkage of gas turbine blades in the wax injection process. A prediction model based on a generalized regression neural network (GRNN) is developed with the newly defined cross-sectional features consisting of area, area ratio, and some discrete point deviations. In the qualitative analysis of the cross-sectional features, it is concluded that the highest accuracy of the wax pattern is obtained for the fourth group of experiments, which corresponds to a holding pressure of 18 bars, a holding time of 180 s, and an injection temperature of 62 °C. The prediction model is trained and tested based on small experimental data, resulting in an average RE of 1.5% for the area, an average RE of 0.58% for the area ratio, and a maximum MSE of less than 0.06  mm2 for discrete point deviations. Experiments show that the GRNN prediction model constructed in this study is relatively accurate, which means that the shrinkage of the remaining major investment casting procedures can also be modeled and controlled separately to obtain turbine blades with higher accuracy. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Quality Control for Engines)
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18 pages, 3254 KiB  
Article
Modeling of the Variation Propagation for Complex-Shaped Workpieces in Multi-Stage Machining Processes
by Fuyong Yang, Peiyue Zhang, Xiaobing Zhang, Juyong Cao and Yanfeng Xing
Machines 2023, 11(6), 603; https://doi.org/10.3390/machines11060603 - 01 Jun 2023
Viewed by 749
Abstract
Variation prediction and quality control for complex-shaped workpieces in automotive and aerospace fields with multi-stage machining processes have drawn significant attention because of the widespread application and increasing diversity of these kinds of workpieces. To finish the final workpieces with complex shapes, multiple [...] Read more.
Variation prediction and quality control for complex-shaped workpieces in automotive and aerospace fields with multi-stage machining processes have drawn significant attention because of the widespread application and increasing diversity of these kinds of workpieces. To finish the final workpieces with complex shapes, multiple setups and operations are often applied in machining processes. However, sources of geometric error, such as fixture error, datum error, machine tool path error, and the dimensional quality of the product, interact complicatedly at different stages. These complex interactions pose significant challenges to final product error prediction and reduction. Manufacturing error prediction based on stream of variation is an effective way to control the machining quality. However, there are few integrated models that can describe the interactions among types of geometric error sources from different stages for different kinds of complex workpieces. This paper proposes a modified error prediction model to systematically capture the interactions of different error sources among different operations for complex-shaped workpieces in multi-stage machining processes. Using differential motion vectors, the connection of all key variations from machine, fixture, and workpiece is established. This modified model can not only handle general fixture layouts for complex workpieces, but also introduce machining-induced variations. Based on this model, the main error sources identification method and error compensation method are proposed. In order to evaluate the effectiveness of the proposed method, engine blocks are used to be machined as an example. Compared with a machining process without a compensating strategy, the average machining error of the key feature is reduced by 80.5% after compensating for the main error sources. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Quality Control for Engines)
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17 pages, 3661 KiB  
Article
Characterization of Dimensional Variations in Turning Process for Multistep Rotary Shaft of High-Speed Motorized Spindle
by Ang Tian, Xueming Du, Shun Liu and Sun Jin
Machines 2023, 11(5), 561; https://doi.org/10.3390/machines11050561 - 16 May 2023
Cited by 1 | Viewed by 1001
Abstract
The surface accuracy of a multistep rotary shaft is very important in manufacturing and the assembly process of the high-speed motorized spindle of CNC machine tools, which is closely related to the machined dimensional variation induced by the turning process. This paper attempts [...] Read more.
The surface accuracy of a multistep rotary shaft is very important in manufacturing and the assembly process of the high-speed motorized spindle of CNC machine tools, which is closely related to the machined dimensional variation induced by the turning process. This paper attempts to enhance a comprehensive understanding of the impact of different locating-error sources and machine toolpaths on the machined dimensional variation for multistep rotary parts of the high-speed motorized spindle in the turning process. A modeling method and a compensation strategy of dimensional variation are introduced in this paper and based on the relationship definition between the error sources and the machined surface using the differential motion vector and stream-of-variation methods. Validation experiments were conducted to verify the proposed model. Additionally, the relationship between locating errors and dimensional variation was investigated with varied case studies, providing a theoretical methodology for the prediction and characterization of the expected dimensional variations of the surface machined with the given conditions. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Quality Control for Engines)
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20 pages, 3035 KiB  
Article
Path Planning for 5-Axis CMM Inspection Considering Path Reuse
by Wenzheng Zhao, Xueqi Wang and Yinhua Liu
Machines 2022, 10(11), 973; https://doi.org/10.3390/machines10110973 - 25 Oct 2022
Cited by 2 | Viewed by 1207
Abstract
The 5-axis Coordinate Measuring Machine (CMM) is widely used for quality data collection of the machining parts, such as cylinder blocks and heads of the engines. High efficient inspection path planning for multiple feature groups from different stations is one of the key [...] Read more.
The 5-axis Coordinate Measuring Machine (CMM) is widely used for quality data collection of the machining parts, such as cylinder blocks and heads of the engines. High efficient inspection path planning for multiple feature groups from different stations is one of the key tasks for CMM application. In engineering practice, the inspection planning of diverse feature groups accounts for large labor cost and process development cycle. To improve the efficiency of path generation for the complex machining part, a five-axis CMM inspection path planning method considering path length, probe rotation and path reusability is proposed. Firstly, the measuring points (MPs) are classified based on feasible inspection direction cone and accessibility of the MPs to achieve the minimum times of probe rotation. Then, the rapidly exploring random trees with multi-root node (RRT-MRNC) algorithm is proposed to implement local path planning considering inspection path reuse. Furthermore, intra-group and inter-group path is generated simultaneously based on the proposed enhanced Genetic Algorithm (GA) algorithm. In order to evaluate the effectiveness of the proposed method, the cylinder block path planning case is used. Compared with the benchmark methods, the total planning time based on the proposed planning method for the dynamic tasks was reduced by 55.2% and 54.9% respectively. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Quality Control for Engines)
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