• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.05a
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• pp.223-226
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• 2002

To maximize the productivity in machining molds and dies, machine tools should operate at high speeds. However, the productivity of mold manufacturing has not increased significantly because CNC milling machines have massive slides, which do not allow rapid acceleration and deceleration during the frequent starts/stops encountered in machining molds and dies. This paper presents the use of composites for these slides to overcome this limitation. The vertical and horizontal slides of a large CNC machine were constructed by bonding high-modulus carbon-fiber epoxy composite sandwiches to welded steel structures using adhesives. These composite structures reduced the weight of the vertical and horizontal slides by 34% and 26%, respectively, and increased damping by 1.5 to 5.7 times without sacrificing the stiffness. Without much tuning, this machine had a positional accuracy of $\pm5\mu\textrm{m}$ per 300 m of the slide displacement.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.189-196
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• 2001

Machine tools of high-speed and high-precision are required for various fields of industry such as semiconductor, automobile, mold fabrication and so on. Light-weight machine tool structure is essential for reduction of production time through rapid transportation. Also, high damping capacity of the structure is required to obtain precise products without vibration during manufacturing. Composite materials have high potential for machine tool structures due to its high specific stiffness and good damping characteristics. In this study, the design and the manufacture of a hybrid machine tool structure using composite materials was attempted and the damping capacity was investigated experimentally.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.2
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• pp.137-143
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• 2022

Composite materials, being light-weight and of high mechanical strength, are increasingly used in various industries such as the aerospace, automobile, sporting-goods manufacturing, and ship-building industries. Recently, manufacturing of composite materials using 3D printers has increased. 3D-printed composite materials are made in free-form and adapted for end-use by adjusting the fiber content and orientation. However, research on the machining of 3D printed composite materials is limited. The aim of this study is to develop a machine learning method to select machining conditions for machining of 3D-printed composite materials. The composite material was composed of Onyx and carbon fibers and stacked sequentially. The experiments were performed using the following machining conditions: spindle speed, feed rate, depth of cut, and machining direction. Cutting forces of the different machining conditions were measured by milling the composite materials. PCA, a method of machine learning, was developed to select the machining conditions and will be used in subsequent experiments under various machining conditions.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.4
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• pp.34-47
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• 2000

Since carbon fiber epoxy composite materials have excellent properties for structures due to their high specific strength, high specific modulus, high damping and low thermal expansion, the hollow shafts made of carbon fiber epoxy composites have been widely used for power transmission shafts for motor vehicles , spindles of machine tools, motor base, bearing mount for tool up and manufacturing. The molded composite machine elements are not usually accurate enough for mechanical machine elements, which require turning drilling , cutting and grinding. The experiment are surface grinding wheel GC60 to the carbon fiber epoxy composite specimen with respect to staking angle [0]nT , [45]nT, [90]nT on the CNC grinding machine. In this paper, the surface grinding characteristics of composite plate, which are surveyed experimentally and analytically with respect to the grinding force, surface roughness and wheel loading according to the variable depth of cut, wheel velocity and table feed rate are investigated.

• Composites Research
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• v.33 no.5
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• pp.262-267
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• 2020

With increasing demands for high-speed machining and lightweight design of machine tools, increasing likeliness of generation of machine tool spindle vibrations has become an important issue. Spindle vibration has a significant impact on the surface finish of the workpiece in ultra-precision machining. It is necessary to resolve the machine tool spindle vibration in various machining processes to improve machining accuracy. In this paper, a TiC-SKH51 metal-matrix composite was used to suppress the vibration of the machine tool spindle. To confirm the dynamic characteristic of the TiC-SKH51 composite, impact hammer tests were conducted. After verifying the reliability of a finite element analysis (FEA) by comparing the results of the impact hammer test with the modal analysis using FEA, the analysis of the machine tool spindle model was performed. The FEA results show that the TiC-SKH51 composite applied machine tool spindle can be utilized to suppress the vibration generation.

• Steel and Composite Structures
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• v.48 no.5
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• pp.531-545
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• 2023

Composite dowels are implemented as a powerful alternative to headed studs for the efficient combination of Ultra High-Performance Concrete (UHPC) with high-strength steel in novel composite structures. They are required to provide sufficient shear resistance and ensure the transmission of tensile forces in the composite connection in order to prevent lifting of the concrete slab. In this paper, the load bearing capacity of puzzle-shaped and clothoidal-shaped dowels encased in UHPC specimen were investigated based on validated experimental test data. Considering the influence of the embedment depth and the spacing width of shear dowels, the characteristics of UHPC square plate on the load bearing capacity of composite structure, 240 numeric models have been constructed and analyzed. Three artificial intelligence approaches have been implemented to learn the discipline from collected experimental data and then make prediction, which includes Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), Adaptive Neuro-Fuzzy Inference System (ANFIS) and an Extreme Learning Machine (ELM). Among the factors, the embedment depth of composite dowel is proved to be the most influential parameter on the load bearing capacity. Furthermore, the results of the prediction models reveal that ELM is capable to achieve more accurate prediction.

• Structural Engineering and Mechanics
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• v.66 no.3
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• pp.399-409
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• 2018

This paper demonstrates the potential application of machine learning algorithms for approximate prediction of the load and deflection capacities of the novel type of Laced Steel Concrete-Composite (LSCC) beams proposed by Anandavalli et al. (Engineering Structures 2012). Initially, global and local responses measured on LSCC beam specimen in an experiment are used to validate nonlinear FE model of the LSCC beams. The data for the machine learning algorithms is then generated using validated FE model for a range of values of the identified sensitive parameters. The performance of four well-known machine learning algorithms, viz., Support Vector Regression (SVR), Minimax Probability Machine Regression (MPMR), Relevance Vector Machine (RVM) and Multigene Genetic Programing (MGGP) for the approximate estimation of the load and deflection capacities are compared in terms of well-defined error indices. Through relative comparison of the estimated values, it is demonstrated that the algorithms explored in the present study provide a good alternative to expensive experimental testing and sophisticated numerical simulation of the response of LSCC beams. The load carrying and displacement capacity of the LSCC was predicted well by MGGP and MPMR, respectively.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2003.10a
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• pp.199-202
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• 2003

The dynamic characteristics of composite tool bars depend on the clamping conditions such as clamping force, stiffness and surface characteristics of clamping parts as well as the basic structures. Therefore, in this work, the effects of clamping part conditions on the dynamic characteristics of cantilever type composite machine tool structures with clamped joint were investigated because the cantilever type machine tool structures are ideal cases for composite application to increase the natural frequency and damping of structures. New design of the clamping part was developed in order to improve shear properties of the clamping part and dynamic characteristics of composite tool bars. From FE analysis and Impulse response tests, dynamic characteristics were obtained with respect to the clamping part conditions of the new design.

• Computers and Concrete
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• v.33 no.6
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• pp.739-754
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• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.831-836
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• 2000

During machining, since more than 50% compliance of the cutting point in machine tool structures comes from headstocks, with the remainder coming from beds, slides and structural joints, the structural analysis of the headstock is very important to improve the static and dynamic performances. Especially, in case of machining hard and brittle materials such as glasses and ceramics with the grinding machine, the reinforced headstock with the high damping material is demanded. Since the fiber reinforced composite materials have excellent properties for structures, owing to its high specific modulus, high damping and low thermal expansion, it is expected that the dynamic and thermal characteristics of the headstock will be improved if they are employed as the materials fur headstock. In this paper, the design and the manufacturing methods as well as the static and dynamic characteristics of a steel-composite hybrid headstock were investigated analytically and experimentally to improve the performance of the grinding machine system.