• Korean Journal of Computational Design and Engineering
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• v.4 no.1
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• pp.52-59
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• 1999

The CAD/CAM System for deep drawing transfer die tin mechanical press process has been developed. The developed CAD system can generate the drawing of transfer die in mechanical press. Using thee results from CAD system, it can generate the NC data to machine die's elements on the CAD system. This system can reduce design man-hour an human errors. In order to construct the system, it is used to automated the design process and generate the NC data using concepts of the designing rule and the machining rule. The developed system is based on the knowledge base system which is involved a lot of expert's technology in the practice field. Using AutoLISP language under the AutoCAD system, CTK customer language of SmartCAM is used as the overall CAD/CAM environment. Results of this system will be provide effective aids to the designer and manufacturer in this field.

• Computers and Concrete
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• v.28 no.6
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• pp.567-583
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• 2021

The use of waste materials as a binder or aggregate in the concrete mixture is a great step towards sustainability in the construction industry. Waste rubber (WR) can be used as coarse and fine aggregates in concrete and improves the crack resistance, impact resistance, and fatigue life of the produced concrete. However, the mechanical properties of rubberized concrete degrade significantly by replacing the natural aggregate with WR. To have accurate estimations of the mechanical properties of rubberized concrete, two machine learning methods consisting of artificial neural network (ANN) and neuro-fuzzy system (NFS) were served in this study. To do this, a comprehensive dataset was collected from reliable literature, and two scenarios were addressed for the selection of input variables. In the first scenario, the critical ratios of the rubberized concrete and the concrete age were considered as the input variables. In contrast, the mechanical properties of concrete without WR and the percentage of aggregate volume replaced by WR were assumed as the input variables in the second scenario. The results show that the first scenario models outperform the models proposed by the second scenario. Moreover, the developed ANN models are more reliable than the proposed NFS models in most cases.

• Advances in materials Research
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• v.3 no.2
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• pp.91-103
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• 2014

A study that demonstrates how to investigate the molded part quality and the consistency of injection process based on the rheological concept is proposed. It is important for plastic material whose melt viscosity is variable with respect to the processing condition. The formulations to couple the melt viscosity with injection pressure and fill time are derived first. Taking calculations of the measured pressure and the time by using these formulations, the melt viscosity in injection process can be determined on machine. As the relation between the injection speed and the melt viscosity is constructed, the influences of the setting parameter of injection machine on the molded part quality can be investigated through evaluating the state of the melt viscosity. In addition, a pressure sensor bushing (PSB) designed with a quick installation feature is also provided and validated. The results show that a higher injection speed improves the tensile strength of the molded part but also the consistency of the molded part quality. This work provides an alternative to evaluate the molding quality scientifically.

• Smart Structures and Systems
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• v.25 no.4
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• pp.459-469
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• 2020

The presence of cracks in mechanical components is a very important problem that, if it is not detected on time, can lead to high economic costs and serious personal injuries. This work presents a methodology focused on identifying cracks in unbalanced rotors, which are some of the most frequent mechanical elements in industry. The proposed method is based on Artificial Neural Networks that give a solution to the presented inverse problem. They allow to estimate unknown crack parameters, specifically, the crack depth and the eccentricity angle, depending on the dynamic behavior of the rotor. The necessary data to train the developed Artificial Neural Network have been obtained from the frequency spectrum of the displacements of the well- known cracked Jeffcott rotor model, which takes into account the crack breathing mechanism during a shaft rotation. The proposed method is applicable to any rotating machine and it could contribute to establish adequate maintenance plans.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.507-514
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• 2024

In the semiconductor manufacturing line, continuous efforts are being made to reduce the cost of products produced, and the demand for this is accelerating in the chemical mechanical polishing(CMP) process, and a representative example of these cost reduction items is the 5-Zone Ring. After about 150 hours of use in the CMP process, the thickness of the ring decreases to less than 1 mm and must be replaced with a new product. Therefore, in this study, bonding dispensers and press machines with a dispensing amount error of 10g±0.8% or less and a pressure uniformity of ±1.8% or less were developed to reduce semiconductor manufacturing costs by repeatedly regenerating worn parts of the retainer ring, and to minimize environmental pollution caused by industrial waste treatment.

• Structural Engineering and Mechanics
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• v.3 no.4
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• pp.373-382
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• 1995

The weighting strategy has received a great attention and has been widely applied to multicriterion optimization. This gaper examines a global criterion method (GCM) with the weighting objectives strategy in fuzzy structural engineering problems. Fuzziness of those problems are in their design goals, constraints and variables. Most of the constraints are originated from analysis of engineering mechanics. The GCM is verified to be equivalent to fuzzy goal programming via a truss design. Continued and mixed discrete variable spaces are presented and examined using a fuzzy global criterion method (FGCM). In the design process a weighting parameter with fuzzy information is introduced into the design and decision making. We use a uniform machine-tool spindle as an illustrative example in continuous design space. Fuzzy multicriterion optimization in mixed design space is illustrated by the design of mechanical spring stacks. Results show that weighting strategy in FGCM can generate both the best compromise solution and a set of Pareto solutions in fuzzy environment. Weighting technique with fuzziness provides a more relaxed design domain, which increases the satisfying degree of a compromise solution or improves the final design.

• Smart Structures and Systems
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• v.6 no.3
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• pp.335-347
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• 2010

Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.56
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• pp.65-85
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• 2000

Industrial accidents have been consistently increased in terms of medical costs, lost work days, and incidence rates every year in Korea. Since the infrastructure of the industry changed shifts rapidly from 1980s in the developing countries such as South Korea, the nature and magnitude of the industrial accidents have also undergone a major shift. The situation is especially severe in small-to-medium sized industry(SMI). This article reports the development of a systematic evaluation system of risk factors specifically for the SMIs. The new approach introduced by this article is geared to the systematic identification and evaluation of the injuries from power press machines using the Analytic Hierarchy Process with the key evaluation data generated and evaluated by the employees on site. A total of 21 companies was studied and surveyed using the hierarchical structures of the cause-effect relationship of the mechanical injuries and their countermeasures. For the relative weighting of each risk factor, separate questionnaire survey was conducted for the selected workers from each company who had worked for more than 10 years in press work. Most participants (48 out of 62) replied that human attributes were the most significant factors for mechanical injuries fellowed by administration, machine, and work environment factors. The result also showed that the self-motivated risk assessment and safety enhancement activities would be an effective and efficient way of managing the risk factors in the SMIs.

• Structural Engineering and Mechanics
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• v.90 no.4
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• pp.403-415
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• 2024

We looked at how the damping qualities of epoxy composites changed when different amounts of graphite nanoplatelets (GNP) were added, from 0% to 6% by weight. A mix of free and forced vibration tests helped us find the key GNP content that makes the damper ability better the most. We also created a Representative Volume Element (RVE) model to guess how the alloys would behave mechanically and checked these models against testing data. An Artificial Neural Network (ANN) was also used to guess how these compounds would react to motion. With proper hyperparameter tweaking, the ANN model showed good correlation (R²=0.98) with actual data, indicating its ability to predict complex material behavior. Combining these methods shows how GNPs impact epoxy composite mechanical properties and how machine learning might improve material design. We show how adding GNPs to epoxy composites may considerably reduce vibration. These materials may be used in industries that value vibration damping.