• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.12
• /
• pp.2899-2908
• /
• 2000

Methodology of volumetric error identification is presented to improve the accuracy of NC machine tools by using a reference artifact and a touch trigger probe. Homogeneous transformation matrix and kinematic chain are used for modeling the geometric and thermal errors of a three-axis vertical machining center. The reference artifact is designed and fabricated to identify the model parameters by machine tool metrology. Parameters in the error model are able to be identified and updated by direct measurement of the reference artifact on the machine tool under the actual conditions which include the thermal interactions of error sources. The proposed method can speed up and simplify volumetric error identification processes.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.32 no.10
• /
• pp.897-904
• /
• 2008

Egg grading machine is used in poultry raising industry to classify eggs by their weight and to pack up them. Packer holder mechanism is a main part of the egg grading machine, of which role is to take eggs fallen from conveyor belt, and afterward to transfer eggs vertically to mold tray. The vertical motion of packer holder is usually driven by slider-crank mechanism or cam. This paper describes development of the cam in packer holder based on kinematic analysis of packer holder mechanism and measurement of acceleration and noise of the cam to verify performance of it. Several cams that are designed and manufactured by the author of this paper according to different design specification are compared to determine the best solution for egg transfer in the packer holder mechanism.

• Advances in nano research
• /
• v.14 no.3
• /
• pp.225-234
• /
• 2023

In this study, we aim to use big data resources and statistical analysis to obtain a reliable instruction to reach high-quality and high yield agricultural yields. In this regard, soil type data, raining and temperature data as well as wheat production in each year are collected for a specific region. Using statistical methodology, the acquired data was cleaned to remove incomplete and defective data. Afterwards, using several classification methods in machine learning we tried to distinguish between different factors and their influence on the final crop yields. Comparing the proposed models' prediction using statistical quantities correlation factor and mean squared error between predicted values of the crop yield and actual values the efficacy of machine learning methods is discussed. The results of the analysis show high accuracy of machine learning methods in the prediction of the crop yields. Moreover, it is indicated that the random forest (RF) classification approach provides best results among other classification methods utilized in this study.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.51 no.1
• /
• pp.94-101
• /
• 2015

The heat treatment machine based on immersion was developed to reduce temperature difference during netting process and appraised it performance compared current heat treatment machine using high pressure. It was also reviewed the optimum heat treatment procedures for PBSAT monofilament net in accordance with the immersion time and temperature. The procedure was based on physical measurement such as breaking load, elongation and angle of the mesh for PBSAT monofilament. The water temperature gap of the treatment machine based on immersion was less than $1^{\circ}C$. and the energy consumption was also increased in high temperature condition. It was identified that the optimum temperature was $75^{\circ}C$ and its optimum processing time was between 15 minutes and 20 minutes to get qualified physical properties.

• Journal of Ocean Engineering and Technology
• /
• v.34 no.4
• /
• pp.277-284
• /
• 2020

Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
• /
• 2001.11a
• /
• pp.43-52
• /
• 2001

Condition monitoring plays a vital role since it sustains the reliable operation of industrial plant and machinery in the pursuit of economic whole life operation. In order to achieve this goal, it is needed to monitor various parameters of mechanical system such as vibration, wear, temperature, and etc., and finally to diagnosis the root causes of any possible abnormal machine condition. In this work, we constructed a rotor system where various types of functional machine failures occurred frequently in industry were induced. Characteristics of the machine failure were monitored simultaneously by the on-line measurement of vibration, wear and temperature. Result showed that these parameters responded differently to the induced functional machine failure. The availability of each parameter on effective condition monitoring was discussed in this work.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1998.03a
• /
• pp.143-147
• /
• 1998

As the accuracy of manufactured goods needed high-accuracy processing has made the efficiency of NC and measurment technology develop, the innovation of machine tools has influence the development of the semi-conductor and optical technology. We can mention that a traction role of the acceleration for the development like that depends on the development of the measurement technics - Stylus instrument method, STM, SEM, Laser interferometer method - which are used for measuring the movement accuracy of machine tools. The movement error factors in movement accuracy are expressed as yaw, roll, and pitch etc. Machining center has 21 movement error factors including of 3 axies joint errors because that has 3 axies and has been measured as the standard of the unloaded condition until now inspite of getting static, dynamic, and servo-gain errors in the case of expending the error range. Therefore, this study tries to measure position accuracy according to loading on the X-Y table of the machining center.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2002.10a
• /
• pp.980-984
• /
• 2002

This paper has studied thermal characteristics of machine tool to develope high speed spindle and optimum design condidering the thermal deformation. Comparing the test data of temperature measurement and structural analysis data using FEM, we verified the test validity and predicted thermal deformation, influence of spindle generation of heat, and established cooling system to prevent the thermal deformation. 1) The temperature rise of spindle system depends on increasing number of rotation and shows sudden doubling increment of number of rotation over 7,000rpm. 2) Oil jacket cooling can be effective cooling method below 8,000rpm but, over 8,000rpm, it shows the decrement of cooling effect. 3) Comparing FEM analysis results and revolution test results, we can confirm approximate temperature change consequently, it is possible to simulate temperature rise and thermal distribution on the inside of spindle system. 4) We can confirm that simulated approach by FEM analysis can be effective method in thermal-appropriate design.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2002.10a
• /
• pp.353-358
• /
• 2002

A feature-based inspection planning system is proposed in this research to develop more efficient measuring methodology for the OMM (On-machine measurement) for complicated workpiece having many primitive form features. This paper focuses on the development of the CAIP (computer-aided inspection system) methodologies. The optimum inspection sequences for the features are determined by analyzing the feature information such as the nested relations and the possible probe approaching directions of the features, and forming feature groups. A series of heuristic rules are developed to accomplish it. Also, each feature is decomposed into its constituent geometric elements, and then the number of sampling points, the locations of the measuring point, the optimum probing path are determined by applying the fuzzy logic, Hammersley's method, and the TSP algorithm. To verify the proposed methodologies, simulations are carried out and the results are analyzed.

• Journal of Sensor Science and Technology
• /
• v.32 no.6
• /
• pp.378-385
• /
• 2023

Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.