Improving product quality is a crucial factor in determining the competitiveness and business efficiency of enterprises. This study investigates the influence of the cutting parameters, including the cutting speed, the depth of cut, and the feed rate on the surface roughness and the residual stress during the turning of AISI 304 austenitic stainless steel. Moreover, the work aims to determine optimal cutting parameters to satisfy both surface roughness and residual stress requirements. The mathematical model of the relationship between the machining parameters and the performance characteristics was formulated based on the response surface methodology (RSM) and the Box-Behnken design of the experiments. Pareto optimal solution applying natural-inspired algorithm (Bat Algorithm) is proposed to solve the bi-objective optimization problem to obtain the lowest surface roughness and minimal residual stress. The optimum cutting parameters selected by the manufacturing planners from the Pareto optimal fronts are calculated to comply with the production requirements.
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Multi-Objective Optimization for Turning Process of 304 Stainless Steel Based on Dung Beetle Optimizer-Back Propagation Neural Network and Improved Particle Swarm Optimization Huan Xue, Tao Li, Jie Li, Yansong Zhang, Shiyao Huang, Yongchun Li, Chongwen Yang, Wenqian Zhang Journal of Materials Engineering and Performance.2024; 33(8): 3787. CrossRef
This paper proposes a UKF-Based indoor localization method that evaluates the optimal position of a robot by fusing the position information from encoders and the distance information of the obstacle measured by ultrasonic sensors. UKF is a method of evaluating the robot’s position by transforming optimal sigma points extracted using the unscented transform and is advantageous for the localization of a nonlinear system. To solve the problem of the specular reflection effect of ultrasonic sensors, we propose a validation gate that evaluates the reliability of the ranges measured by sonar sensors, that can maximize the quality of the position evaluation. The experimental results showed that the method is stable and convergence of the position error regardless of the size of the initial position error and the length of the sampling time.
Social interest in the 4th industry, intelligent factories, and smart manufacturing is continually growing along with the core technologies like big data and artificial intelligence, which can generate meaningful information by collecting and accumulating sensor data. Demand for industrial automation equipment is increasing worldwide due to the efforts needed to modernize manufacturing facilities, reduce automation and cycle time, and improve quality. Currently, the majority of research is focused on the development of automation facilities and improving productivity. The research on the contents of real-time data considering the characteristics of the cutting machine plasma machine is insufficient. In this study, based on the current data measured according to cutting current and cutting speed, a reference value for cutting quality is presented and the optimal process parameter has been selected. A model for predicting cutting quality by introducing the Mahalanobis Distance Method is presented. An attempt has been made to derive selection and optimal cutting process variables. Based on the predictive model, threshold values were specified and used in real-time data to consider the correlations between multivariate variables and evaluate the degree of scattering around the average of specific values of each variable. Also, process parameters suitable for surface roughness were calculated.
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A quantitative diagnostic method of feature coordination for machine learning model with massive data from rotary machine Yoonjae Lee, Byeonghui Park, Minho Jo, Jongsu Lee, Changwoo Lee Expert Systems with Applications.2023; 214: 119117. CrossRef
Silicon nanoparticles: fabrication, characterization, application and perspectives Taeyeong Kim, Jungchul Lee Micro and Nano Systems Letters.2023;[Epub] CrossRef
Feature selection algorithm based on density and distance for fault diagnosis applied to a roll-to-roll manufacturing system Hyogeun Oh, Yoonjae Lee, Jongsu Lee, Changbeom Joo, Changwoo Lee Journal of Computational Design and Engineering.2022; 9(2): 805. CrossRef
Impact of Sensor Data Characterization with Directional Nature of Fault and Statistical Feature Combination for Defect Detection on Roll-to-Roll Printed Electronics Yoonjae Lee, Minho Jo, Gyoujin Cho, Changbeom Joo, Changwoo Lee Sensors.2021; 21(24): 8454. CrossRef
In the printed circuit board (PCB) manufacturing industry, the yield is an important management factor as it significantly affects the product cost and quality. However, in real situations, it is difficult to ensure a high yield in a manufacturing process, because the products are manufactured through numerous nanoscale manufacturing operations. Thus, for improving the yield, it is necessary to analyze the key process parameters and equipment parameters that result in a low yield. In this study, critical equipment parameters that affect the yield were extracted through a mutual analysis of the equipment parameters (x) and process parameters (y) in the plastic ball grid array (PBGA) manufacturing process. To this end, the study uses the correlation coefficient to apply the heuristic algorithm that extracts critical parameters that keep the redundancy among the equipment parameters to a minimum and exert maximum impact on the critical process parameters. Additionally, by using the general regression neural network technique, the effects of the critical equipment parameters on the process parameters were confirmed. The test results were applied to the PBGA production line and an improvement in the yield was confirmed.
A smart factory with Big Data analytics is getting attention because of its ability to automate and make the manufacturing environment more intelligent. At the same time, higher reliability is required with a drastic increase in complexity and uncertainty within the current system of manufacturing fields. The pump is considered as one of the most crucial equipment as it can affect the overall manufacturing performance of the manufacturing processes and it needs to be timely diagnosed of its mechanical condition as a top priority. In this research, we propose an operation system of centrifugal pumps and a data-driven fault diagnostic model that is developed by collecting relevant multivariate data from several natures. Proposed machine learning models can be used for detecting and diagnosing pump faults via analytical processes containing signal preprocessing and feature engineering procedures. Simulation and case studies from rotating machinery have demonstrated the effectiveness of the proposed analytical framework not only for attaining quantitative reliability but practical usages in actual manufacturing fields as well.
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A Study on 3D Printing Conditions Prediction Model of Bone Plates Using Machine Learning Song Yeon Lee, Yong Jeong Huh Journal of the Korean Society for Precision Engineering.2022; 39(4): 291. CrossRef
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In this paper, the DSC and TGA for phenol and silica/phenolic composite were carried out by increasing temperature up to 950℃ with variable heating rate, to figure out basic thermal and pyrolysis characteristics of the composite. Also, the aim was to obtain the activation energy and the frequency factor which are the main parameters of the Arrhenius equation based on the Kissinger theory. The activation energy and frequency factor were used for the ablation model as material property. To confirm surface temperature distribution and recession of missiles, the CFD analysis using ANSYS Fluent R18.2 was performed to examine the thermal fluid characteristics of the hypersonic flight environment. Subsequently, the analysis results were applied as boundary conditions to a 2D axisymmetric pyrolysis and ablation model. Finally, pyrolysis and ablation analysis were performed using the ablation analysis code SAMCEF AMARYLLIS V.17, which uses the specific ablation module based on finite element code by applying carbon/phenolic and cork materials up to t = 10 s.
A major goal of triboelectric generator is to improve its power output by identifying and optimizing the factors contributing to the harvesting capability. In this study, we developed a double-contact triboelectric nanogenerator (DC-TENG) capable of two contact and separation pairs by adding an additional air-gap layer. The voltage and current output was characterized as a function of the contact speed, position, stroke time (ST), standstill time (SST), and the existence of two air-gaps. The voltage and current output increased non-linearly with decreasing the times. The DC-TENG produced the maximum voltage and current output when the ratio of ST to SST was 7 to 3. Our prototype resembling a pavement block was capable of lighting 144 LED lights by producing a maximum output of 650 V, 25 μA at a pressure of 0.5 kgf/cm².
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Improvement of Dielectric Polarization Characteristic for a Highly Sensitive Flexible Triboelectric Sensor Seo-Yeon So, Sang-Hu Park Journal of the Korean Society for Precision Engineering.2022; 39(5): 357. CrossRef
In the selective laser melting (SLM) process, a three-dimensional part is manufactured based on the formation of numerous molten tracks. Consequently, the generated melt pool in the scanning process of each track exhibits close relation to the internal defect formation and the quality of the fabricated part. In this study, a numerical model of single-track scanning of the SLM process is presented to analyze the melt pool characteristics for various process conditions. The presented model considers the thermal behavior of the powder material including the phase change and densification during the SLM process. The temperature-dependent energy absorption and the increase in effective energy absorptivity due to the keyhole mode melting are also incorporated in the heat flux model to evaluate the process conditions in the presence of high energy density. Moreover, the single-track specimens were manufactured under various process conditions for validation of the proposed model. The predicted melt pool dimensions, as well as the melting modes (Conduction/Keyhole), demonstrated good agreement with the experimental measurements. Based on the analysis results, the process boundaries (Keyhole/Lack-of-Fusion) for the SLM process of AlSi10Mg are provided and the potential application of the proposed model for exploring the process window is discussed.
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Extreme gradient boosting-based multiscale heat source modeling for analysis of solid-state phase transformation in additive manufacturing of Ti-6Al-4V Yeon Su Lee, Kang-Hyun Lee, Min Gyu Chung, Gun Jin Yun Journal of Manufacturing Processes.2024; 113: 319. CrossRef
This paper reports the effectiveness of the introduction of NGDG-SOFC (Natural Gas-Fueled Distributed Generation Solid Oxide Fuel Cell) as a solution to social problems that could arise in the unification era due to the power shortage in North Korea. Under the actual operating conditions of the plant, a stack that operates at a voltage of 33.87 V and current of 31.24 A was modeled with a gross output of 1.06 kW and a net output of 1.00 kW considering the balance of plant (BOP) consumption power. Considering the average primary energy consumption in the ASEAN countries in 2020, 2,870 MW was estimated as the amount of power generation required in North Korea. Also, the gross area of the plant and the annual fuel cost were estimated. Consequently, it is concluded that the area of 861 km2 which corresponds to 0.71 percent of the gross area of North Korea, and fuel cost of about 1,474 million $/year are required. The introduction of NGDG-SOFC plants is believed to follow the global trend of renewable energy and resolving the power shortage in North Korea in an eco-friendly manner.
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Distributed generation parameter optimization method based on fuzzy C-means clustering under the Internet of Things architecture Xin Yao, Liyun Xing, Ping Xin Energy Reports.2021; 7: 106. CrossRef
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