• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.539-544
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• 1998

Generally, welding gap is a serious factor of a falling-off in weld quality among various kind of weld defect. Welding gap is created between two work piece in GMAW(Gas Metal Arc Welding) of horizontal fillet weld because surface of workpiece is not flat by cutting process. In these days, there were many attempts to detect welding gap. though we prevalently use the vision sensor or arc sensor in welding process, it is difficult to detect welding gap for improvement of welding quality. But we have a trouble to find relationship between welding gap and many welding parameters due to non-linearity of welding process. As mentioned about the various difficult problem, we can detect welding gap precisely using neural networks which are able to model non-linear function. Also, this paper was proposed real-time monitoring of weld bead shape to find effect of welding gap and to estimate weld quality. Monitoring of weld bead shape examined the correlation of welding parameters with bead eometry using learning ability of neural networks. Finally, the developed system, welding gap detecting system and bead shape monitoring system, is expected to the successful capability of automation of welding process by result of simulation.

• Journal of the Korean Society of Safety
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• v.34 no.4
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• pp.59-67
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• 2019

Since it is really hard to construct process models based on prior process knowledges, various statistical approaches have been employed to build fault diagnosis models. However, the crucial drawback of these approaches is that the solutions may vary according to the fault magnitude, even if the same fault occurs. In this study, the parameter monitoring approach is suggested. When a fault occurs in a chemical process, this leads to trigger the change of a process model and the monitoring parameters of process models is able to provide the efficient fault diagnosis model. A few important variables are selected and their predictive models are constructed by partial least square (PLS) method. The Euclidean norms of parameters of PLS models are estimated and a fault diagnosis can be performed as comparing with parameters of PLS models based on normal operational conditions. To improve the monitoring performance, cumulative summation (CUSUM) control chart is employed and the changes of model parameters are recorded to identify the type of an unknown fault. To verify the efficacy of the proposed model, Tennessee Eastman (TE) process is tested and this model can be easily applied to other complex processes.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.4
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• pp.387-394
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• 2016

Today, hot rolled mill plant are currently operating 24 hours a day. The main processes of hot-rolled steel mill are Reheating Furnace, Roughing Mill, Fishing Mill and the Down Coiler. In the process checking over the thickness and width of rolled material, temperature control is an important factor. It is quite often the cause of the work breakdown and find a bad elements for product. After the shipping out the product, when a claim occurs requires an investigation. In each process of the conventional thickness gauge, width, and thermometer. Operation by an independent data acquisition and this monitoring is problematic and straining of the operator. The monitoring process the data of each function and incorporate monitoring results can also check future work. The installation of this system will improve our efficiency and productivity. In the rolling process by developing more efficient operating quality monitoring system, this will be made possible.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.3
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• pp.217-224
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• 2016

This paper presents a new technique for monitoring the concrete curing process using embedded piezoelectric transducers via admittance measurements. When a piezoelectric transducer is embedded in a structure, the electrical impedance (admittance) of the transducer is coupled with the mechanical impedance of the host structure, which allows monitoring of the structural condition. In this study, the admittance signatures are used for monitoring the concrete curing process. This new method is based on an admittance-based sensor diagnostic process, in which the capacitance values of the piezoelectric transducers are dependent on the strength of the host structure. We numerically and experimentally investigated the variations in capacitive value during the curing process. The results demonstrate that there is a clear relationship between the concrete curing status and the slope, this indicates that the proposed method could be efficiently used for monitoring the curing status of a concrete structure.

• Journal of Korean Society for Quality Management
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• v.33 no.1
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• pp.22-31
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• 2005

With the common objective to improve process productivity and product quality, statistical process control (SPC) and engineering process control (EPC) have been widely used in the discrete-parts industry and the process industry, respectively. The major focus of SPC is on process monitoring, while that of EPC is on process adjustment. The emergence of the hybrid industry necessitates a synergistic combination of the two methods for an effective process control. This paper investigates the existing studies on SPC, EPC, and the integration of the two methods. This paper also presents future research issues in this field.

• Smart Structures and Systems
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• v.22 no.2
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• pp.231-237
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• 2018

A significant data problem is encountered with condition monitoring because the sensors need to measure vibration data at a continuous and sometimes high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer data than traditional data sampling methods. This sensing paradigm is applied to condition monitoring with an improved machine learning algorithm in this study. For the experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. The optimal signal features were then selected without the signal reconstruction process. For damage classification, we used the Variance Considered Machine, utilizing only the compressed data. The experimental results show that the proposed compressive sensing method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• Proceedings of the Safety Management and Science Conference
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• 1999.11a
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• pp.217-236
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• 1999

Engineering process control (EPC) is one of the techniques very widely used in process. EPC is based on control theory which aims at keeping the process on target. Statistical process control (SPC), also known as statistical process monitoring. The main purpose of SPC is to look for assignable causes (variability) in the process data. The combined SPC/EPC scheme is gaining recognition in the process industries where the process frequently experiences a drifting mean. This paper aims to study the difference between SPC and EPC in simple terms and presents a case study that demonstrates successful integration of SPC and EPC for a product in drifting industry. Statistical process control (SPC) monitoring of the special causes of a process, along with engineering feedback control such as proportional-integral-derivative (PID) control, is a major tool for on-line quality improvement.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.14-18
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• 2001

Rise in cutting force causes tool damage and worsens product quality resulting in machining accuracy deterioration. Especially, fragile material cutting brings about breakage of material and worsens product surface quality. In this study, we trace the locus of cutting force and examine the machined surface corresponding to the cutting force loci. and build up a monitoring system for deciding normal operation or not of cutting process.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.883-888
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• 1993

This paper proposes a monitoring method using an infrared temperature sensor in laser surface hardening process. To investigate the validity of the method a series of experiments are performed for various conditions. The experimental results show that the surface temperature depends upon the laser power, travelling speed and surface conditions of a specimen. Especially, the laser surface hardening process is greatly influenced by the surface conditions of the specimen, such as coating thickness and materials.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.26-31
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• 1994

The analysis of acoustic emission signals generated during machining has been proposed as a technique for studying both the fundamentals of the cutting process and process and as a methodology for detecting tool fracture on line. In this study, AE signals detected during End Milling were applied as the experimental test to sensing tool fracture on the CNC vertical milling machine. Because automatic monitoring of the cutting condition is one of the most important technologies in machining, the in-process detection of cutting tool life including fracture has been investigated by performing experimental test.