In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

In this study, acoustic and viscosity data are collected in real time during the ball milling process and analyzed for correlation. After fast Fourier transformation (FFT) of the acoustic data, changes in the signals are observed as a function of the milling time. To analyze this quantitatively, the frequency band is divided into 1 kHz ranges to obtain an integral value. The integrated values in the 2–3 kHz range of the frequency band decrease linearly, confirming that they have a high correlation with changes in viscosity. The experiment is repeated four times to ensure the reproducibility of the data. The results of this study show that it is possible to estimate changes in slurry properties, such as viscosity and particle size, during the ball milling process using an acoustic signal.

All machines deteriorate in performance over time. The phenomenon that causes such performance degradation is called deterioration. Due to the deterioration, the process mean of the machine shifts, process variance increases due to the expansion of separate interval, and the failure rate of the machine increases. The maintenance model is a matter of determining the timing of preventive maintenance that minimizes the total cost per wear between the relation to the increasing production cost and the decreasing maintenance cost. The essential requirement of this model is that the preventive maintenance cost is less than the failure maintenance cost. In the process mean shift model, determining the resetting timing due to increasing production costs is the same as the maintenance model. In determining the timing of machine adjustments, there are two differences between the models. First, the process mean shift model excludes failure from the model. This model is limited to the period during the operation of the machine. Second, in the maintenance model, the production cost is set as a general function of the operating time. But in the process mean shift model, the production cost is set as a probability functions associated with the product. In the production system, the maintenance cost of the equipment and the production cost due to the non-confirming items and the quality loss cost are always occurring simultaneously. So it is reasonable that the failure and process mean shift should be dealt with at the same time in determining the maintenance time. This study proposes a model that integrates both of them. In order to reflect the actual production system more accurately, this integrated model includes the items of process variance function and the loss function according to wear level.

This study suggests a model of production information system that can reduce manufacturing lead time and uniformize quality by using DNC S/W as a part of constructing production information management system in the industrial field of the existing marine engine block manufacturing companies. Under the effect of development of this system, the NC machine interface device can be installed in the control computer to obtain the quality information of the workpiece in real time so that the time to inspect the process quality and verify the product defect information can be reduced by more than 70%. In addition, the reliability of quality information has been improved and the external credibility has been improved. It took 30 minutes for operator to obtain, analyze and manage the quality information when the existing USB memory is used, but the communication between the NC controller computer and the NC controller in real time was completed to analyze the workpiece within 10 seconds.

Machines and facilities are physically or chemically degenerated by continuous usage. One of the results of this degeneration is the process mean shift. By the result of degeneration, non-conforming products and malfunction of machine occur. Therefore a periodic preventive resetting the process is necessary. This type of preventive action is called ‘preventive maintenance policy.’ Preventive maintenance presupposes that the preventive (resetting the process) cost is smaller than the cost of failure caused by the malfunction of machine. The process mean shift problem is a field of preventive maintenance. This field deals the interrelationship between the quality cost and the process resetting cost before machine breaks down. Quality cost is the sum of the non-conforming item cost and quality loss cost. Quality loss cost is due to the deviation between the quality characteristics from the target value. Under the process mean shift, the quality cost is increasing continuously whereas the process resetting cost is constant value. The objective function is total costs per unit wear, the decision variables are the wear limit (resetting period) and the initial process mean. Comparing the previous studies, we set the process variance as an increasing concave function and set the quality loss function as Cpm+ simultaneously. In the Cpm+, loss function has different cost coefficients according to the direction of the quality characteristics from target value. A numerical example is presented.

Machines are physically or chemically degenerated by continuous usage. One of the results of this degeneration is the process mean shift. Under the process mean shift, production cost, failure cost and quality loss function cost are increasing continuously. Therefore a periodic preventive resetting the process is necessary. We suppose that the wear level is observable. In this case, process mean shift problem has similar characteristics to the maintenance policy model. In the previous studies, process mean shift problem has been studied in several fields such as ‘Tool wear limit’, ‘Canning Process’ and ‘Quality Loss Function’ separately or partially integrated form. This paper proposes an integrated cost model which involves production cost by the material, failure cost by the nonconforming items, quality loss function cost by the deviation between the quality characteristics from the target value and resetting the process cost. We expand this process mean shift problem a little more by dealing the process variance as a function, not a constant value. We suggested a multiplier function model to the process variance according to the analysis result with practical data. We adopted two-side specification to our model. The initial process mean is generally set somewhat above the lower specification. The objective function is total integrated costs per unit wear and independent variables are wear limit and initial setting process mean. The optimum is derived from numerical analysis because the integral form of the objective function is not possible. A numerical example is presented.

본 연구에서는 알루미나 정밀여과 및 광촉매 코팅 폴리프로필렌의 혼성 수처리 공정에서 물역세척 시간 (back-flushing time, BT) 및 PP 구 변화의 영향을 알아보고, 알루미나 한외여과막와 동일한 PP 비드를 사용한 선행 결과와 비 교하였다. 물역세척 주기(FT)는 10분으로 고정한 채, BT를 6~30초로 변화시키면서, 그 영향을 180분 운전 후 막 오염에 의한 저항(Rf), 투과선속(J)과 총여과부피(VT) 측면에서 고찰하였다. BT가 길어질수록 Rf는 급격히 감소하고 J는 증가하였으나, VT는 BT 10초일 때 최대였다. 탁도의 처리효율은 99.0% 이상으로 BT의 영향이 보이지 않았다. 한편, 유기물 처리효율은 역세척 없 는 조건(NBF)에서 89.0%로 가장 높았으며, BT가 길어질수록 증가하였다. 막오염 측면에서 최적 PP 비드의 투입 농도는 20 g/L이었으나, 알루미나 한외여과막와 동일한 PP 비드를 사용한 선행 결과 최적 PP 비드의 농도는 40 g/L이었다. 탁도와 유기 물 처리효율은 PP 농도 30 g/L에서 최대였으나, 선행 결과 탁도와 유기물 처리효율은 모두 PP 농도 40 g/L에서 가장 높았다.

A number of plating companies have been exposed to the risk of fire due to unexpected temperature increasing of water in a plating bath. Since the companies are not able to forecast the unexpected temperature increasing of water and most of raw materials in the plating process have low ignition temperature, it is easy to be exposed to the risk of fire. Thus, the companies have to notice the changes immediately to prevent the risk of fire from plating process. Due to this reason, an agile and systematic temperature monitoring system is required for the plating companies. Unfortunately, in case of small size companies, it is hard to purchase a systematic solution and be offered consulting from one of the risk management consulting companies due to an expensive cost. In addition, most of the companies have insufficient research and development (R&D) experts to autonomously develop the risk management solution. In this article, we developed a real time remote temperature monitoring system which is easy to operate with a lower cost. The system is constructed by using Raspberry Pi single board computer and Android application to release an economic issue for the small sized plating manufacturing companies. The derived system is able to monitor the temperature continuously with tracking the temperature in the batch in a short time and transmit a push-alarm to a target-device located in a remoted area when the temperature exceeds a certain hazardous-temperature level. Therefore, the target small plating company achieves a risk management system with a small cost.

Odor compounds and air-born microorganisms are simultaneously emitted from various aeration processes such as aerobic digestion, food-waste compositing, and carcass decomposition facilities that are biologically-treating wastes with high organic contents. The air streams emitted from these processes commonly contain sulfur-containing odorous compounds such as hydrogen sulfide(H2S) and bacterial bioaerosols. In this study, a wet-plasma method was applied to remove these air-born pollutants and to minimize safety issues. In addition, the effects of a gas retention time and a liquid-gas ratio were evaluated on removal efficiencies in the wet-plasma system. At the gas reaction time of 1.8 seconds and the liquid-gas ratio of 0.05 mLaq/Lg, the removal efficiency of bioaerosol was approximately 75 %, while the removal efficiency of H2S was lower than 20 %, indicating that the gaseous compound was not effectively oxidized by the plasma reaction at the low liquid addition. When the liquid-gas ratio was increased to 0.25 mLaq/Lg, the removal efficiencies of both H2S and bioaerosol increased to greater than 99 %. At the higher liquid-gas ratio, more ozone was generated by the wet-plasma reaction. The ozone generation was significantly affected by the input electrical energy, and it needed to be removed before discharged from the process.

This paper reviews an implementation strategy of activity breakdown for the assessment of process time. In addition, the study proposes the classification models for estimating the process time of Time-Driven Activity-Based Costing (TDABC) based on various types of activity breakdown structures, including activity interface perspective, activity decomposition perspective and activity priority perspective.

This study used transmission electron microscopy (TEM) to investigate the micro-morphological features of two formaldehyde to urea (F/U) mole ratio liquid urea-formaldehyde (UF) resins with three hardener levels as a function of the curing time. The micro-morphological features of the liquid UF resins were characterized after different curing times. As a result, the TEM examination revealed the presence of globular/nodular structures in both liquid UF resins, while spherical particles were only visible in the low F/U mole ratio resins. The high F/U mole ratio liquid UF resins also showed extensive particle coalescence after adding the hardener, along with the appearance of complex filamentous networks. When the resins were cured with a higher amount of hardener and longer curing time, the spherical particles disappeared. For the low mole UF resins, the particles tended to coalesce with a higher amount of hardener and longer curing time, although discrete spherical particles were still observed in some regions. This is the first report on the distinct features of the crystal structures in low F/U mole ratio UF resins cured with 5% hardener and after 0.5 h of curing time. In conclusion, the present results indicate that the crystal structures of low F/U mole ratio UF resins are formed during the curing process.

In this paper, we investigate the statistical correlation of the time series for temperature measured at the heat box in the automobile drying process. We show, in terms of the sample variance, that a significant non-linear correlation exists in the time series that consist of absolute temperature changes. To investigate further the non-linear correlation, we utilize the volatility, an important concept in the financial market, and induce volatility time series from absolute temperature changes. We analyze the time series of volatilities in terms of the de-trended fluctuation analysis (DFA), a method especially suitable for testing the long-range correlation of non-stationary data, from the correlation perspective. We uncover that the volatility exhibits a long-range correlation regardless of the window size. We also analyze the cross correlation between two (inlet and outlet) volatility time series to characterize any correlation between the two, and disclose the dependence of the correlation strength on the time lag. These results can contribute as important factors to the modeling of forecasting and management of the heat box’s temperature.

Bi2Sr2CaCu2Ox(Bi-2212) and Bi2Sr2Ca2Cu3Oy(Bi-2223) high-Tc superconductors(HTS) have been manufactured by plasma spraying, partial melt process(PMP) and annealing treatment(AT). A Bi-2212/2223 HTS coating layer was synthesized through the peritectic reaction between a 0212 oxide coating layer and 2001 oxide coating layer by the PMP-AT process. The 2212 HTS layer consists of whiskers grown in the diffusion direction. The Bi-2223 phase and secondary phase in the Bi-2212 layer were observed. The secondary phase was distributed uniformly over the whole layer. As annealing time goes on, the Bi-2212 phase decreases with mis-orientation and irregular shape, but the Bi-2223 phase increases because a new Bi-2223 phase is formed inside the pre-existing Bi-2212 crystals, and because of the nucleation of a Bi-2223 phase at the edge of Bi-2212 crystals by diffusion of Ca and Cu-O bilayers. In this study the spray coated layer showed superconducting transitions with an onset Tc of about both 115 K, and 50 K. There were two steps. Step 1 at 115 K is due to the diamagnetism of the Bi-2223 phase and step 2 at 50 K is due to the diamagnetism of the Bi-2212 phase.

Workers are avoiding production/manufacturing sites due to the poor working environment and concern over safety. Small and medium-sized businesses introduce new equipment to secure safety in the production site or ensure effective process management by introducing the real-time monitoring technique for existing equipment. The importance of real-time monitoring of equipment and process in the production site can also be found in the ANSI/ISA-195 model. Note, however, that most production sites still use paper-based work slip as a process management technique. Data reliability may deteriorate because information on the present condition of the production site cannot be collected/analyzed properly due to manual data writing by the worker. This paper introduces the monitoring and process management technique based on a direct facility interface to secure safety in the field by improving the poor working environment and enhance there liability and real-time characteristics of the production data. Since the data is collected from equipment in real-time directly through the SIB-based interface and PLC-based interface, problems associated with workers’ manual data input are expected to be solved; safety can also be improved by enhancing workers’ attention to work by minimizing workers’ injuries and disruption.

It is not easy to establish the correct standard time and standard manhour in a process of small quantity batch production system, especially in a case of irregular quantity of production. Therefore, how to establish rational standard time about manufactu

In this study, the effect of milling time on the microstructure and phase transformation behaviors of Ni-12 wt.%B powders was investigated using vibratory ball milling process. X-ray diffraction patterns showed that the phase transformation of mixed Ni-B elemental powder occurred after 50 hours of milling, with a formation of nickel boride phases. Through the study of microstructures in mechanical alloying process, it was considered that ball milling strongly accelerates solid-state diffusions of the Ni and B atoms during mechanical alloying process. The results of X-ray photoelectron spectroscopy showed that most of B atoms in the powder were linked to Ni with a formation of nickel boride phases after 200 hours of milling. It was finally concluded that mechanical alloying using ball milling process is feasible to synthesize fine and uniform nickel boride powders.

In this research, the indium dissolution properties of the waste LCD panel powders were investigated as a function of milling time fabricated by high-energy ball milling (HEBM) process. The particle morphology of waste LCD panel powders changed from sharp and irregular shape of initial cullet to spherical shape with an increase in milling time. The particle size quickly decreased to 15 until the first minute, then decreased gradually about 6 with presence of agglomerated particles after 5 minutes, which increased gradually reaching a uniform size of 13 consist of agglomerated particles after 30 minutes. The glass recovery, after dissolution, was over 99% at initial cullet, which decreased to 90.1 and 78.6% with increasing milling time of 1 and 30 minute respectively, due to a loss in remaining powder of the surface ball and jar, as well as the filter paper. The dissolution amount of indium out of the initial cullet was 208 ppm before milling, turning into 223 ppm for the mechanically milled powder after 1 minute, and nearly 146~125 ppm with further increase in milling time because of the reaction surface decrease of powders due to agglomeration. With this process, maximum dissolving indium amount (223 ppm) could be achieved at a particle size of 15 with 1 minute of milling.

The reduction of setup time is very important in a lot production system. A punch press is a typical system of lot production. This paper describes a case study to reduce setup time of a punch press manufacturing system. Especially, this case study reduce