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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 March 2023) | Viewed by 15650

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Special Issue Editors

Dr. Hongyun So

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Guest Editor

Department of Mechanical Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: MEMS (micro-electro-mechanical systems); harsh-environment sensors; biomedical microdevices; microfluidics; microfabrication; electronics cooling
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Dr. Jong-Won Park

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Guest Editor

Department of Reliability Assessment, Korea Institute of Machinery and Materials, Daejeon 34103, Republic of Korea
Interests: accelerated life and degradation test; fuzzy control; sensor fusion; robot dynamics and manipulator design; hydropneumatic system
Special Issues, Collections and Topics in MDPI journals

Dr. Sunghan Kim

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Guest Editor

School of Mechanical Engineering, Chung-Ang University, Dongjak-gu, Seoul 06974, Republic of Korea
Interests: bionanocomposties; micro/nanomechanics; sensors; nanofluid; surface/interface engineering; tribology; finite element analysis; hydrogen energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Reliable processes are critical in various fields including chemical, mechanical, electrical, material, and biomedical engineering with various scales from nano to macro. Recently, the development and characterization of theoretical and experimental reliability analysis have emerged as advanced technologies for reliability assessment and prognostics and health management for various materials, systems, and industrial products.

This Special Issue aims to provide insights for researchers to discuss recent advanced reliable processes and techniques, and applications to address many challenging engineering problems. Unique and novel manufacturing, modeling, and numerical methods of any industrial applications are of special interest. We invite contributions to this Special Issue on topics including but not limited to the follows:

Materials and Characterization

Fracture and Failure
Fatigue
Corrosion
Metals
Alloys/Composites

Reliable Processes and Methods

Reliability analysis and modeling
Design for reliability
Numerical methods
Risk assessment and safety
Non-destructive testing and evaluation (NDT&E)

PHM (Prognostics and Health Management)

Advanced sensors
Big data analytics
Prognostics methodology
Smart manufacturing
Structural health management
Accelerated life and degradation test

Applications include, but are not limited to

Mechanical engineering (manufacturing, devices, MEMS, robotics, microfluidics, etc.)
Aerospace engineering (aircraft, missiles, satellites, drones, etc.)
Chemical engineering (synthesis, separation, mixing, mass transfer, etc.)
Energy systems (batteries, turbine, solar cell, electrical vehicles, etc.)

Dr. Hongyun So
Dr. Jong-Won Park
Dr. Sunghan Kim
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

reliability
manufacturing
materials
health management

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Published Papers (10 papers)

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Research

15 pages, 4058 KiB

Open AccessArticle

A Study of a Domain-Adaptive LSTM-DNN-Based Method for Remaining Useful Life Prediction of Planetary Gearbox

by Zixuan Liu, Chaobin Tan, Yuxin Liu, Hao Li, Beining Cui and Xuanzhe Zhang

Processes 2023, 11(7), 2002; https://doi.org/10.3390/pr11072002 - 3 Jul 2023

Cited by 3 | Viewed by 1303

Abstract

Remaining Useful Life (RUL) prediction is an important component of failure prediction and health management (PHM). Current life prediction studies require large amounts of tagged training data assuming that the training data and the test data follow a similar distribution. However, the RUL-prediction data of the planetary gearbox, which works in different conditions, will lead to statistical differences in the data distribution. In addition, the RUL-prediction accuracy will be affected seriously. In this paper, a planetary transmission test system was built, and the domain adaptive model was used to Implement the transfer learning (TL) between the planetary transmission system in different working conditions. LSTM-DNN network was used in the data feature extraction and regression analysis. Finally, a domain-adaptive LSTM-DNN-based method for remaining useful life prediction of Planetary Transmission was proposed. The experimental results show that not only the impact of different operating conditions on statistical data was reduced effectively, but also the efficiency and accuracy of RUL prediction improved. Full article

(This article belongs to the Special Issue Reliability and Engineering Applications)

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20 pages, 5540 KiB

Open AccessArticle

Improvement of Assembly Manufacturing Process through Value Stream Mapping and Ranked Positional Weight: An Empirical Evidence from the Defense Industry

by Fandi Achmadi, Budi Harsanto and Akhmad Yunani

Processes 2023, 11(5), 1334; https://doi.org/10.3390/pr11051334 - 26 Apr 2023

Cited by 2 | Viewed by 3193

Abstract

This study aims to improve the assembly manufacturing process to solve the workload imbalances by combining value stream mapping (VSM) and ranked positional weight (RPW). An empirical study was conducted in a defense manufacturing firm located in Indonesia. The study specifically focused on 155 components and 56 tasks distributed among 43 assembly workstations in one weapon product. The results of the analysis showed a significant reduction in the total cycle time, from 5121 s (85.35 min) to 3620 s (60.33 min), or a decrease of 29%. Additionally, the study found improvements in the balance of the assembly line as measured by balance delay, line efficiency, and smoothness index performance indicators. The application of VSM and RPW in this study is unique in the context of the defense industry, as it provides empirical analysis on cycle time and assembly line balance, which is rarely studied. The results of this study contribute to the advancement of literature in the field and provide valuable insights for other organizations in the defense industry and other manufacturing industries. By improving the efficiency and balance of the weapon assembly line, this study has the potential to increase productivity and reduce waste. Full article

(This article belongs to the Special Issue Reliability and Engineering Applications)

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15 pages, 5086 KiB

Open AccessArticle

Fatigue Life and Crack Initiation in Monopile Foundation by Fatigue FE Analysis

by Zhen-Ming Wang, Kyong-Ho Chang, Shazia Muzaffer and Mikihito Hirohata

Processes 2023, 11(5), 1317; https://doi.org/10.3390/pr11051317 - 24 Apr 2023

Cited by 3 | Viewed by 1802

Abstract

The construction of new renewable energy infrastructures and the development of new ocean resources continues to proceed apace. In this regard, the increasing size and capacity of offshore wind turbines demands that the size of their accompanying supporting marine structures likewise increase. The types of marine structures utilized for these offshore applications include gravity base, monopile, jacket, and tripod structures. Of these four types, monopile structures are widely used, given that they are comparatively easy to construct and more economical than other structures. However, constant exposure to harsh cyclic environmental loads can cause material deterioration or the initiation of fatigue cracks, which can then lead to catastrophic failures. In this paper, a 3D fatigue finite element analysis was performed to predict both the fatigue life and the crack initiation of a welded monopile substructure. The whole analysis was undertaken in three steps. First, a 3D non-steady heat conduction analysis was used to calculate the thermal history. Second, a thermal load was induced, as an input in 3D elastoplastic analysis, in order to determine welding residual stresses and welding deformation. Finally, the plastic strain and residual stress were used as inputs in a 3D fatigue FE analysis in order to calculate fatigue crack initiation and fatigue life. The 3D fatigue finite element analysis was based on continuum damage mechanics (CDM) and elastoplastic constitutive equations. The results obtained from the 3D fatigue finite element analysis were compared with hot spot stresses and Det Norske Veritas (DNV-GL) standards. Full article

(This article belongs to the Special Issue Reliability and Engineering Applications)

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16 pages, 5451 KiB

Open AccessFeature PaperArticle

Comparative Analysis of Machine Learning Approaches to Predict Impact Energy of Hydraulic Breakers

by Sung-Hyun Kim, Jong-Won Park and Jae-Hoon Kim

Processes 2023, 11(3), 772; https://doi.org/10.3390/pr11030772 - 5 Mar 2023

Cited by 1 | Viewed by 2355

Abstract

Impact energy, the main performance subject of hydraulic breakers, is required to evaluate value from consumers. This study proposes a neural network algorithm-based model to predict the impact energy of a hydraulic breaker without measuring it. The proposed model was developed using 1451 data points for various parameters as an input to predict the impact energy of hydraulic breakers in a small class to a large class. Different machine learning methods have been studied, including correlation analysis, linear regression, and neural networks. The results revealed that the working pressure, working flow rate, chisel diameter, nitrogen gas pressure, operating frequency, and power significantly influenced impact energy formation. The results obtained provide a reliable model for predicting the impact energy of hydraulic circuit breakers of various sizes. Full article

(This article belongs to the Special Issue Reliability and Engineering Applications)

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22 pages, 6037 KiB

Open AccessArticle

Intelligent Fault Diagnosis Method for Gearboxes Based on Deep Transfer Learning

by Zhenghao Wu, Huajun Bai, Hao Yan, Xianbiao Zhan, Chiming Guo and Xisheng Jia

Processes 2023, 11(1), 68; https://doi.org/10.3390/pr11010068 - 27 Dec 2022

Cited by 8 | Viewed by 1743

Abstract

The complex operating environment of gearboxes and the easy interference of early fault feature information make fault identification difficult. This paper proposes a fault diagnosis method based on a combination of whale optimization algorithm (WOA), variational mode decomposition (VMD), and deep transfer learning. First, the VMD is optimized by using the WOA, and the minimum sample entropy is used as the fitness function to solve for the K value and penalty parameter α corresponding to the optimal decomposition of the VMD, and the correlation coefficient is used to reconstruct the signal. Second, the reconstructed signal after reducing noise is used to generate a two-dimensional image using the continuous wavelet transform method as the transfer learning target domain data. Finally, the AlexNet model is used as the transfer object, which is pretrained and fine-tuned with model parameters to make it suitable for early crack fault diagnosis in gearboxes. The experimental results show that the method proposed in this paper can effectively reduce the noise of gearbox vibration signals under a complex working environment, and the fault diagnosis method of using transfer learning is effective and achieves high accuracy of fault diagnosis. Full article

(This article belongs to the Special Issue Reliability and Engineering Applications)

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19 pages, 5502 KiB

Open AccessArticle

Study of the Possibilities of Improving Maintenance of Technological Equipment Subject to Wear

by Vlad Alexandru Florea, Andreea Cristina Ionică, Adrian Florea, Răzvan-Bogdan Itu and Mihai Popescu-Stelea

Processes 2022, 10(12), 2550; https://doi.org/10.3390/pr10122550 - 30 Nov 2022

Cited by 1 | Viewed by 1700

Abstract

The rapid development of science and technology, and the restructuring of the mining extraction industry, bring about profound changes in the structure and complexity of technological equipment used in mining. In this paper, the Reliability Centered Maintenance (RCM) method has been applied to analyze the components of the KSW-460NE shearer machine, which fails quite frequently. The cutter drums do not match from a constructive point of view, and the concrete operation conditions, alongside the picks (being in direct contact with coal and hard inclusions) and guides are submitted to intense abrasion wear, showing a great number of failures. The data collected following the machine’s exploitation allowed parameter determination characterizing the reliability of the components mentioned, the manner of failure, and the effects. Using calculation methods, it has been possible to facilitate the interpretation of the result in view of establishing measures required to improve maintenance of the dominant components of the machine, determining replacement intervals, in accordance with an imposed reliability and maintainability. The results of the study assist in the choice of suitable hardening materials for the reconditioning of cutter drums and guides that are necessary for practical trials, by which their operating times, and replacement intervals, respectively, might be additionally improved. Full article

(This article belongs to the Special Issue Reliability and Engineering Applications)

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24 pages, 12043 KiB

Open AccessArticle

A New Modified Exponent Power Alpha Family of Distributions with Applications in Reliability Engineering

by Zubir Shah, Dost Muhammad Khan, Zardad Khan, Muhammad Shafiq and Jin-Ghoo Choi

Processes 2022, 10(11), 2250; https://doi.org/10.3390/pr10112250 - 1 Nov 2022

Cited by 8 | Viewed by 1763

Abstract

Probability distributions perform a very significant role in the field of applied sciences, particularly in the field of reliability engineering. Engineering data sets are either negatively or positively skewed and/or symmetrical. Therefore, a flexible distribution is required that can handle such data sets. In this paper, we propose a new family of lifetime distributions to model the aforementioned data sets. This proposed family is known as a “New Modified Exponent Power Alpha Family of distributions” or in short NMEPA. The proposed family is obtained by applying the well-known T-X approach together with the exponential distribution. A three-parameter-specific sub-model of the proposed method termed a “new Modified Exponent Power Alpha Weibull distribution” (NMEPA-Wei for short), is discussed in detail. The various mathematical properties including hazard rate function, ordinary moments, moment generating function, and order statistics are also discussed. In addition, we adopted the method of maximum likelihood estimation (MLE) for estimating the unknown model parameters. A brief Monte Carlo simulation study is conducted to evaluate the performance of the MLE based on bias and mean square errors. A comprehensive study is also provided to assess the proposed family of distributions by analyzing two real-life data sets from reliability engineering. The analytical goodness of fit measures of the proposed distribution are compared with well-known distributions including (i) APT-Wei (alpha power transformed Weibull), (ii) Ex-Wei (exponentiated-Weibull), (iii) classical two-parameter Weibull, (iv) Mod-Wei (modified Weibull), and (v) Kumar-Wei (Kumaraswamy–Weibull) distributions. The proposed class of distributions is expected to produce many more new distributions for fitting monotonic and non-monotonic data in the field of reliability analysis and survival analysis. Full article

(This article belongs to the Special Issue Reliability and Engineering Applications)

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20 pages, 5029 KiB

Open AccessArticle

Diesel Engine Fault Diagnosis Method Based on Optimized VMD and Improved CNN

by Xianbiao Zhan, Huajun Bai, Hao Yan, Rongcai Wang, Chiming Guo and Xisheng Jia

Processes 2022, 10(11), 2162; https://doi.org/10.3390/pr10112162 - 22 Oct 2022

Cited by 16 | Viewed by 2416

Abstract

The safe operation of diesel engines performs a vital function in industrial production and life. Because diesel engines often work in harsh environmental conditions, they are prone to failure. Therefore, this paper proposes a fault analysis method based on a combination of optimized variational mode decomposition (VMD) and improved convolutional neural networks (CNN) to address the necessary need for preventive maintenance of diesel engines. The authentic vibration sign is first decomposed by using the (VMD) algorithm, then the greatest range of decomposition layers is decided by using scattering entropy and the useful components are preferentially chosen for reconstruction. The continuous wavelet transform (CWT) records preprocessing method is then delivered to radically change the noise-reduced vibration sign into a time-frequency map, which is fed into the CNN for model coaching and extraction of fault features. Finally, fault classification is realized by support vector machine (SVM) with excellent classification performance. Through preset fault experiments on diesel engines, it is established that the technique proposed in this paper can successfully identify fault states, and the classification accuracy is higher than alternative methods. Full article

(This article belongs to the Special Issue Reliability and Engineering Applications)

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15 pages, 6172 KiB

Open AccessFeature PaperArticle

Evaluation of the Reliability and Lifetime Prediction of 150 GHz Athermal AWG Module with Metal Temperature Compensation Board

by Kwang-Su Yun, Chong-Hee Yu, Kwon-Seob Lim, Wan-Chun Kim, Su-Yong Kim and Insu Jeon

Processes 2022, 10(10), 2120; https://doi.org/10.3390/pr10102120 - 18 Oct 2022

Cited by 1 | Viewed by 1568

Abstract

We have developed a 17-channel (150 GHz-spacing) athermal arrayed waveguide grating (AAWG), which has a wider operation range than that of the existing AWGs, by designing a metal structure assembly that reduces the temperature dependence of the wavelength. For an operation temperature range from −40 °C to 85 °C, the center wavelengths of all channels had a wavelength stability of ±0.04 nm and the insertion loss variation was less than ±0.78 dB. The accelerated life test showed that the predicted service life was expected to be more than 41.7 years. Full article

(This article belongs to the Special Issue Reliability and Engineering Applications)

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13 pages, 2760 KiB

Open AccessArticle

On Reassessment of the HWMA Chart for Process Monitoring

by Muhammad Riaz, Shabbir Ahmad, Tahir Mahmood and Nasir Abbas

Processes 2022, 10(6), 1129; https://doi.org/10.3390/pr10061129 - 5 Jun 2022

Cited by 11 | Viewed by 2345

Abstract

In the recent literature of process monitoring, homogeneously weighted moving average (HWMA) type control charts have become quite popular. These charts are quite efficient for early detection of shifts, especially of smaller magnitudes, in process parameters such as location and dispersion. A recent study pointed out a few concerns related to HWMA charts that mainly relate to its steady-state performance. It needs to be highlighted that the initial studies on HWMA focused only on the zero-state performance of the chart relative to other well-known memory charts. This study reinvestigates the performance of the HWMA chart under zero and steady states at various shifts. Using the Monte Carlo simulation method, a detailed comparative analysis of the HWMA chart is carried out relative to the exponentially weighted moving average (EWMA) chart with time-varying limits. For several values of design parameters, the in-control and out-of-control performance of these charts is evaluated in terms of the average run length (ARL). It has been observed that the structure of the HWMA chart has the ability to safeguard the detection ability and the run-length properties under various delays in process shifts. More specifically, it has been found that HWMA chart is superior to the EWMA chart for several shift sizes under zero state and is capable of maintaining its dominance in case the process experiences a delay in shift. However, the steady-state performance depends on the suitable choice of design parameters. This study provides clear cut-offs where HWMA and EWMA are superior to one another in terms of efficient monitoring of the process parameters. Full article

(This article belongs to the Special Issue Reliability and Engineering Applications)

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