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Mathematics
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Mathematical Methods Applied to Quality Control and Mechanical Engineering
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Mathematical Methods Applied to Quality Control and Mechanical Engineering

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 2686

Special Issue Editors

Dr. Rongxi Wang

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Guest Editor
State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: quality control; reliability analysis; signal processing; uncertainty analysis; mathematic modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jianmin Gao

E-Mail Website
Guest Editor
State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: modeling of complex systems; quality control in manufacturing systems; mathematic modeling of complex systems
Dr. Kai Sun

E-Mail Website
Guest Editor
Institute of Microelectronics (IME) of the Chinese Academy of Sciences, Beijing 100029, China
Interests: reliability modeling and data analysis of electronic devices

Special Issue Information

Dear Colleagues,

Benefiting from the rapid development of computing power and control methods, probability statistics, linear algebra, and other mathematics methods have been successfully applied in many engineering fields, such as quality control and mechanical engineering. Furthermore, these mathematics methods are becoming the required tools for engineers. The latest mathematics methods and application scenarios have been steadily attracting attention from academic scholars and industry. However, there have not been systematic collections that cover the mathematics requirements in quality control and mechanical engineering, and the latest developments in mathematic theories.

A partial list of topics includes mathematic modeling, solution techniques and applications of mathematics methods in quality control and mechanical engineering, variational formulations and numerical algorithms related to quality control, uncertainty analysis in quality control, and other basic computational methodologies.

Dr. Rongxi Wang
Prof. Dr. Jianmin Gao
Dr. Kai Sun
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. Mathematics is an international peer-reviewed open access semimonthly 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

  • applied mathematics
  • mathematic modeling in engineering
  • variational formulations and numerical algorithms
  • solution techniques
  • computational quality control
  • uncertainty analysis in engineering
  • computational methods

Published Papers (2 papers)

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Research

18 pages, 2424 KiB  
Article
A Novel Model Validation Method Based on Area Metric Disagreement between Accelerated Storage Distributions and Natural Storage Data
by Bin Suo, Yang Qi, Kai Sun and Jingyuan Xu
Mathematics 2023, 11(11), 2511; https://doi.org/10.3390/math11112511 - 30 May 2023
Cited by 1 | Viewed by 741
Abstract
It has been a challenge to quantify the credibility of the accelerated storage model until now. This paper introduces a quantitative measurement named the CMADT (Creditability Metric of Accelerated Degradation Test), which quantifies the credibility of the accelerated aging model based on available [...] Read more.
It has been a challenge to quantify the credibility of the accelerated storage model until now. This paper introduces a quantitative measurement named the CMADT (Creditability Metric of Accelerated Degradation Test), which quantifies the credibility of the accelerated aging model based on available data. The relevant criterion data are obtained from the natural storage test. CMADT is a credibility metric obtained by measuring the difference in the metric area between the probability distribution of the accelerated storage model and its criterion data. In addition, the accelerated aging model might include multiple parameters resulting in several single-parameter CMADTs. This paper proposes a method that integrates several single-parameter CMADT metrics into a single metric to assess the overall credibility of the accelerated storage model. Moreover, CMADT is universal for different scales of sample data. The cases addressed in this paper show that CMADT helps designers and decision-makers judge the credibility of the result obtained by the accelerated storage model intuitively and makes it easier to compare various products horizontally. Full article
(This article belongs to the Special Issue Mathematical Methods Applied to Quality Control and Mechanical Engineering)
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18 pages, 16581 KiB  
Article
Error Propagation Model Using Jacobian-Torsor Model Weighting for Assembly Quality Analysis on Complex Product
by Yue Xi, Zhiyong Gao, Kun Chen, Hongwei Dai and Zhe Liu
Mathematics 2022, 10(19), 3534; https://doi.org/10.3390/math10193534 - 28 Sep 2022
Cited by 4 | Viewed by 1429
Abstract
The assembly quality of a complex product is the result of the combined effects of multiple manufacturing stages, including design, machining and assembly, and it is influenced by associated elements with complex coupling mechanisms. These elements generate and transmit assembly quality deviations during [...] Read more.
The assembly quality of a complex product is the result of the combined effects of multiple manufacturing stages, including design, machining and assembly, and it is influenced by associated elements with complex coupling mechanisms. These elements generate and transmit assembly quality deviations during the assembly process which are difficult to analyze and express effectively. Current studies have focused on the analysis and optimization of the assembly surface errors of single or few components, while lacking attention to the impact of errors on the whole product. Therefore, in order to solve the above problem, an assembly quality deviation analysis (AQDA) model is constructed in this paper to analyze the deviation transfer process in the assembly process of complex products and to obtain the key features to optimize. Firstly, the assembly process information is extracted and the assembly quality network model is established on the basis of complex networks. Second, the Jacobian-Torsor (J-T) model is introduced to form a network edge weighting method suitable for the assembly process to objectively express the error propagation among product part features. Third, an error propagation model (EPM) is designed to simulate the error propagation and diffusion processes in the assembly network. Finally, the assembly process of an aero-engine fan rotor is used as an example for modeling and analysis. The results show that the proposed method can effectively identify the key assembly features in the assembly process of complex products and determine the key quality optimization points and monitoring points of the products, which can provide a decision basis for product quality optimization and control. Full article
(This article belongs to the Special Issue Mathematical Methods Applied to Quality Control and Mechanical Engineering)
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