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Machine Learning for Object Recognition in Manufacturing Applications
Huitaek Yun,Eunseob Kim,Dong Min Kim,Hyung Wook Park,Martin Byung-Guk Jun 한국정밀공학회 2023 International Journal of Precision Engineering and Vol.24 No.4
Feature recognition and manufacturability analysis from computer-aided design (CAD) models are indispensable technologies for better decision making in manufacturing processes. It is important to transform the knowledge embedded within a CAD model to manufacturing instructions for companies to remain competitive as experienced baby-boomer experts are going to retire. Automatic feature recognition and computer-aided process planning have a long history in research, and recent developments regarding algorithms and computing power are bringing machine learning (ML) capability within reach of manufacturers. Feature recognition using ML has emerged as an alternative to conventional methods. This study reviews ML techniques to recognize objects, features, and construct process plans. It describes the potential for ML in object or feature recognition and offers insight into its implementation in various smart manufacturing applications. The study describes ML methods frequently used in manufacturing, with a brief introduction of underlying principles. After a review of conventional object recognition methods, the study discusses recent studies and outlooks on feature recognition and manufacturability analysis using ML.
Chengwen Han,Kyeong Bin Kim,Seok-Woo Lee,Martin Byung-Guk Jun,Young Hun Jeong 한국정밀공학회 2021 International Journal of Precision Engineering and Vol.22 No.9
Currently, carbon fiber-reinforced plastic (CFRP) is a material with potential uses for various industries due to its excellent properties. However, the severe tool wear in its machining is an evitable problem because it deteriorates product quality and productivity at the same time. Timely replacement of the tool should be taken in advance to reduce the influences of this drawback. In sum, the accurate estimation of the tool wear plays a crucial role in CFRP machining. Therefore, in this study, a tool wear estimation in CFRP drilling is presented. The method used is based on discrete wavelet transformation (DWT) of the thrust force signal and an artificial neural network (ANN). Two valuable features related to tool wear are identified and then extracted using DWT. The tool wear is estimated by using an ANN with two features adapted from DWT. Consequently, the tool wear, especially flank wear, in CFRP drilling can be accurately estimated using the proposed method.
CNN-based Human Recognition and Extended Kalman Filter-based Position Tracking Using 360° LiDAR
정기범(Kibum Jung),권성환(Sung Hwan Kweon),전병국(Martin Byung-Guk Jun),정영훈(Young Hun Jeong),양승한(Seung-Han Yang) Korean Society for Precision Engineering 2022 한국정밀공학회지 Vol.39 No.8
The collaboration of robots and humans sharing workspace, can increase productivity and reduce production costs. However, occupational accidents resulting in injuries can increase, by removing the physical safety around the robot, and allowing the human to enter the workspace of the robot. In preventing occupational accidents, studies on recognizing humans, by installing various sensors around the robot and responding to humans, have been proposed. Using the LiDAR (Light Detection and Ranging) sensor, a wider range can be measured simultaneously, which has advantages in that the LiDAR sensor is less impacted by the brightness of light, and so on. This paper proposes a simple and fast method to recognize humans, and estimate the path of humans using a single stationary 360° LiDAR sensor. The moving object is extracted from background using the occupied grid map method, from the data measured by the sensor. From the extracted data, a human recognition model is created using CNN machine learning method, and the hyper-parameters of the model are set, using a grid search method to increase accuracy. The path of recognized human is estimated and tracked by the extended Kalman filter.
Fabrication of Thick Microfiber Mats Using Melt-Electrospinning
김정화(Jeong Hwa Kim),신광준(Gwang June Shin),전병국(Martin Byung-Guk Jun),정영훈(Young Hun Jeong) Korean Society for Precision Engineering 2021 한국정밀공학회지 Vol.38 No.7
Recently, porous structures of nano/microfibers are receiving great attention because of their excellent mechanical properties, surface area to volume ratio, and permeability. In this study, thick microfiber mats were fabricated using a melt-electrospinning process in a controlled manner. A melt-electrospinning equipment including a three-axis precision motion control with pneumatic dispensing was constructed. The diameter and deposition pattern of melt-electrospun microfibers with respect to the barrel temperature and pressure were investigated. Based on identified effects of process conditions on microfiber geometry, thick microfiber mats with various properties were successfully fabricated using melt-electrospinning with snake scanning and iterative layering. Their mechanical properties and porosities were then compared and analyzed.
Geometrical Simulation Model for Milling of Carbon Fiber Reinforced Polymers (CFRP)
Xingyu Fu,송경은,Dong Min Kim,Zhengyang Kang,Martin Byung-Guk Jun 한국정밀공학회 2022 International Journal of Precision Engineering and Vol.23 No.11
This paper proposes a geometrical 3D milling simulation algorithm for Carbon Fiber Reinforced Polymer (CFRP) milling. In this simulation model, milling tools are simplified into layers of circles while CFRP lami- nates are simplified into layers of Dexel lines, which can realize simulations for various complex milling conditions. Significant geometrical parameters, for ex-ample, cutting angle and cutting length, can be computed with high efficiency. With some geometry-related physical models, the machining results can be pre-dicted for the entire milling process. The effectiveness of this simulation model has been validated by the milling force prediction and the delamination pre-diction. The performance of this simulation model benefits industrial CFRP manufacturing and provides a new method for online or long-time-interval simulation of CFRP machining.
Fu Xingyu,Zhou Fengfeng,Peddireddy Dheeraj,Kang Zhengyang,Jun Martin Byung-Guk,Aggarwal Vaneet 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.3
In this work, we present a boundary oriented graph embedding (BOGE) approach for the graph neural network to assist in rapid design and digital prototyping. The cantilever beam problem has been solved as an example to validate its potential of providing physical field results and optimized designs using only 10 ms. Providing shortcuts for both boundary elements and local neighbor elements, the BOGE approach can embed unstructured mesh elements into the graph and performs an efficient regression on large-scale triangular-mesh-based finite element analysis (FEA) results, which cannot be realized by other machine-learning-based surrogate methods. It has the potential to serve as a surrogate model for other boundary value problems. Focusing on the cantilever beam problem, the BOGE approach with 3-layer DeepGCN model achieves the regression with mean square error (MSE) of 0.011 706 (2.41% mean absolute percentage error) for stress field prediction and 0.002 735 MSE (with 1.58% elements having error larger than 0.01) for topological optimization. The overall concept of the BOGE approach paves the way for a general and efficient deep-learning-based FEA simulator that will benefit both industry and Computer Aided Design (CAD) design-related areas.
Comparative Analysis and Monitoring of Tool Wear in Carbon Fiber Reinforced Plastics Drilling
김경빈(Kyeong Bin Kim),서장훈(Jang Hoon Seo),김태곤(Tae-Gon Kim),전병국(Martin Byung-Guk Jun),정영훈(Young Hun Jeong) Korean Society for Precision Engineering 2020 한국정밀공학회지 Vol.37 No.11
Recently, carbon fiber-reinforced plastic (CFRP) has been attracting much attention in various industries because of its beneficial properties such as excellent strength, modulus per unit density, and anti-corrosion properties. However, there are several issues in its application to various fields. Severe tool wear issues in its machining have been noted as one of the most serious problems because it induces various serious machining failures such as delamination and splintering. In this regard, timely tool replacement is essential for reducing the influence of tool wear. In this study, tool wear, especially flank wear, in the CFRP drilling was investigated and monitored. First, the reproducibility of tool wear under the same machining condition was experimentally evaluated. And it is demonstrated that tool wear may remarkably differ even though the same machining condition is applied to the tools. Then, tool wear monitoring based on the feed motor torque was applied to the detection of tool life ending in the CFRP drilling process. Consequently, it was demonstrated that the average and maximum detection error of the tool life end were less than 7 and 14%, respectively.