In order to predict the process window of laser powder bed fusion (LPBF) for printing metallic components, the calculation of volumetric energy density (VED) has been widely calculated for controlling process parameters. However, because it is assumed that the process parameters contribute equally to heat input, the VED still has limitation for predicting the process window of LPBF-processed materials. In this study, an explainable machine learning (xML) approach was adopted to predict and understand the contribution of each process parameter to defect evolution in Ti alloys in the LPBF process. Various ML models were trained, and the Shapley additive explanation method was adopted to quantify the importance of each process parameter. This study can offer effective guidelines for fine-tuning process parameters to fabricate high-quality products using LPBF.

This study selected two labor-intensive processes in harsh environments among domestic food production processes. It analyzed their improvement effectiveness using 3-dimensional (3D) simulation. The selected processes were the “frozen storage source transfer and dismantling process” (Case 1) and the “heavily loaded box transfer process” (Case 2). The layout, process sequence, man-hours, and output of each process were measured during a visit to a real food manufacturing factory. Based on the data measured, the 3D simulation model was visually analyzed to evaluate the operational processes. The number of workers, work rate, and throughput were also used as comparison and verification indicators before and after the improvement. The throughput of Case 1 and Case 2 increased by 44.8% and 69.7%, respectively, compared to the previous one, while the utilization rate showed high values despite the decrease, confirming that the actual selected process alone is a high-fatigue and high-risk process for workers. As a result of this study, it was determined that 3D simulation can provide a visual comparison to assess whether the actual process improvement has been accurately designed and implemented. Additionally, it was confirmed that preliminary verification of the process improvement is achievable.

In this study, we proposed a simulator for the development of a digital multi-process welding machine and a welding process monitoring system. The simulator, which mimics the data generation process of the welding machine, is composed of process control circuit, peripheral device circuit, and wireless communication circuit. Utilizing this simulator, we aimed to develop a welding process monitoring system that can monitor the welding situations of four multi-process welding machines and three processes each, with data transmission through wireless communication. Through the operation of the proposed simulator, sequential digital processing of multi-process welding data and wireless communication were achieved. The welding process monitoring system enabled real-time monitoring and accumulation of the process data. The selection of upper and lower limits for process variables was carried out using a deep neural network based on allowable changes in bead shape, enabling the management of welding quality by applying a process control technique based on the trend of received data.

The injection molding process is a process in which thermoplastic resin is heated and made into a fluid state, injected under pressure into the cavity of a mold, and then cooled in the mold to produce a product identical to the shape of the cavity of the mold. It is a process that enables mass production and complex shapes, and various factors such as resin temperature, mold temperature, injection speed, and pressure affect product quality. In the data collected at the manufacturing site, there is a lot of data related to good products, but there is little data related to defective products, resulting in serious data imbalance. In order to efficiently solve this data imbalance, undersampling, oversampling, and composite sampling are usally applied. In this study, oversampling techniques such as random oversampling (ROS), minority class oversampling (SMOTE), ADASYN(Adaptive Synthetic Sampling), etc., which amplify data of the minority class by the majority class, and complex sampling using both undersampling and oversampling, are applied. For composite sampling, SMOTE+ENN and SMOTE+Tomek were used. Artificial neural network techniques is used to predict product quality. Especially, MLP and RNN are applied as artificial neural network techniques, and optimization of various parameters for MLP and RNN is required. In this study, we proposed an SA technique that optimizes the choice of the sampling method, the ratio of minority classes for sampling method, the batch size and the number of hidden layer units for parameters of MLP and RNN. The existing sampling methods and the proposed SA method were compared using accuracy, precision, recall, and F1 Score to prove the superiority of the proposed method.

사출성형공정은 열가소성 수지를 가열하여 유동상태로 만들어 금형의 공동부에 가압 주입한 후에 금형 내에서 냉각시키는 공정으로, 금형의 공동모양과 동일한 제품을 만드는 방법이다. 대량생산이 가능하고, 복잡한 모양이 가능한 공정으로, 수지온도, 금형온도, 사출속도, 압력 등 다양한 요소들이 제품의 품질에 영향을 미친다. 제조현장에서 수집되는 데이터는 양품과 관련된 데이터는 많은 반면, 불량품과 관련된 데이터는 적어서 데이터불균형이 심각하다. 이러한 데이터불균형을 효율적으로 해결하기 위하여 언더샘플링, 오버샘플링, 복합샘플링 등이 적용되고 있다. 본 연구에서는 랜덤오버샘플링(ROS), 소수 클래스 오버 샘플링(SMOTE), ADASTN 등의 소수클래스의 데이터를 다수클래스만큼 증폭시키는 오버샘플링 기법을 활용하고, 데이터마이닝 기법을 활용하여 품질예측을 하고자 한다.

Ball stud parts are manufactured by a cold forging process, and fastening with other parts is secured through a head part cutting process. In order to improve process quality, stabilization of the forging quality of the head is given priority. To this end, in this study, a predictive model was developed for the purpose of improving forging quality. The prediction accuracy of the model based on 450 data sets acquired from the manufacturing site was low. As a result of gradually multiplying the data set based on FE simulation, it was expected that it would be possible to develop a predictive model with an accuracy of about 95%. It is essential to build automated labeling of forging load and dimensional data at manufacturing sites, and to apply a refinement algorithm for filtering data sets. Finally, in order to optimize the ball stud manufacturing process, it is necessary to develop a quality prediction model linked to the forging and cutting processes.

For Dry Storage of Spent Nuclear Fuel (SNF), all moisture must be removed from the dry storage canister through subjected to a drying process to ensure the long-term integrity. In NUREG-1536, the evacuation of most water contained within the canister is recommended a pressure of 0.4 kPa (3 torr) to be held in the canister for at least 30 minutes while isolated from active vacuum pumping as a measure of sufficient dryness in the canister. In the existing drying process, the determination of drying end point was determined using a dew point sensor indirectly. Various methods are being studied to quantify the moisture content remaining inside the canister. We presented a moisture quantification method using the drying process variables, like as temperature, pressure, and relative humidity operation data. During the drying process, it exists in the form of a mixed gas of water vapor and air inside the canister. At this time, if the density of water vapor in the mixed gas discharged out of the canister by the vacuum pump is known, the mass of water removed by vacuum drying can be calculated. The canister is equipped with a pressure gauge, thermometer and dew point sensor. The density of water vapor is calculated using the pressure, temperature and relative humidity of the gas obtained from these sensors. First, calculate the saturated water vapor pressure, and then calculate the humidity ratio. The humidity ratio refers to the ratio of water vapor mass to the dry air mass. After calculating the density of dry gas, multiply the density by the humidity ratio to calculate the density of water vapor (kg/m3). Multiply the water vapor density by the volume flow (m3/s) to obtain the mass value of water (kg). The calculated mass value is the mass value obtained per second since it is calculated through the flow data obtained every second, and the amount of water removed can be obtained by summing all the mass values. By comparing this value with the initial moisture content, the amount of moisture remaining inside the canister can be estimated. The validity of the calculations will be verified through an experimental test in the near future. We plan to conduct various research and development to quantify residual water, which is important to ensure the safety of the drying process for dry storage.

In this study, as part of the paradigm shift for manufacturing innovation, data from the multi-stage cold forging process was collected and based on this, a big data analysis technique was introduced to examine the possibility of quality prediction. In order for the analysis algorithm to be applied, the data collection infrastructure corresponding to the independent variable affecting the quality was built first. Similarly, an infrastructure for collecting data corresponding to the dependent variable was also built. In addition, a data set was created in the form of an independent variable-dependent variable, and the prediction accuracy of the quality prediction model according to the traditional statistical analysis and the tree-based regression model corresponding to the big data analysis technique was compared and analyzed. Lastly, the necessity of changing the manufacturing environment for the use of big data analysis in the manufacturing process was added.

This study studied a system that can redesign the production site layout and respond with dynamic simulation through fabric production process innovation for smart factory promotion and digital-oriented decision making of the production process. We propose to reflect the required throughput and throughput per unit facility of fabric production process as probability distribution, and to construct data-driven metabolism such as data collection, data conversion processing, data rake generation, production site monitoring and simulation utilization. In this study, we demonstrate digital-centric field decision smartization through architectural design for the smartization of fabric production plants and dynamic simulations that reflect it.

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.

Nowadays, since there are so many big data available everywhere, those big data can be used to find useful information to improve design and operation by using various analysis methods such as data mining. Especially if we have event log data that has execution history data of an organization such as case_id, event_time, event (activity), performer, etc., then we can apply process mining to discover the main process model in the organization. Once we can find the main process from process mining, we can utilize it to improve current working environment. In this paper we developed a new method to find a final diagnosis of a patient, who needs several procedures (medical test and examination) to diagnose disease of the patient by using process mining approach. Some patients can be diagnosed by only one procedure, but there are certainly some patients who are very difficult to diagnose and need to take several procedures to find exact disease name. We used 2 million procedure log data and there are 397 thousands patients who took 2 and more procedures to find a final disease. These multi-procedure patients are not frequent case, but it is very critical to prevent wrong diagnosis. From those multi-procedure taken patients, 4 procedures were discovered to be a main process model in the hospital. Using this main process model, we can understand the sequence of procedures in the hospital and furthermore the relationship between diagnosis and corresponding procedures.

With the recent development of manufacturing technology and the diversification of consumer needs, not only the process and quality control of production have become more complicated but also the kinds of information that manufacturing facilities provide the user about process have been diversified. Therefore the importance of big data analysis also has been raised. However, most small and medium enterprises (SMEs) lack the systematic infrastructure of big data management and analysis. In particular, due to the nature of domestic manufacturing companies that rely on foreign manufacturers for most of their manufacturing facilities, the need for their own data analysis and manufacturing support applications is increasing and research has been conducted in Korea. This study proposes integrated analysis platform for process and quality analysis, considering manufacturing big data database (DB) and data characteristics. The platform is implemented in two versions, Web and C/S, to enhance accessibility which perform template based quality analysis and real-time monitoring. The user can upload data from their local PC or DB and run analysis by combining single analysis module in template in a way they want since the platform is not optimized for a particular manufacturing process. Also Java and R are used as the development language for ease of system supplementation. It is expected that the platform will be available at a low price and evolve the ability of quality analysis in SMEs.

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.

Due to sudden transition to intellectual society corresponding with fast technology progress, companies and nations need to focus on development and guarantee of intellectual property. The possession of intellectual property has been the important factor of competition power. In this paper we developed the efficient patent search process with big data analysis tool R. This patent search process consists of 5 steps. We result that at first this process obtain the core patent search key words and search the target patents through search formula using the combination of above patent search key words.

IWRAP 프로그램은 수로에서의 위험성을 평가하는데 유용한 프로그램이다. 그러나 이 프로그램의 기본 버전의 경우 AIS 데이터를 수집하는 기능이 포함되지 않아서, 더구나 베트남과 같은 개발도상국에서는 해상교통량 통계 데이터가 없는 실정이다. 사용자들은 수동으로 준비하여 입력하여야 한다. 이 연구는 IWRAP Mk2 프로그램 기본 버전을 사용하는데 있어 AIS 데이터를 전처리(pre-process) 할 수 있는 프로그램을 개발하고자 하였으며, 선박 형태, 선박 크기, 통과 시간 등으로 분류한 통항로에서의 선박 통항 척수 및 항로 배치와 같이 해역 내 해상교통에 관한 정보들을 사용자에게 제공하도록 고안되었다. 이렇게 개발된 통합 AIS 프로그램(Total AIS, TOAIS)은 베트남 Vung Tau 해역의 AIS 수집 데이터를 전처리할 수 있는지에 대하여 검증하였다. 그 결과, 통합 AIS 프로그램에서 전처리한 데이터를 이용한 IWRAP 프로그램은 베트남 해역의 위험성을 효율적으로 평가할 수 있었다.

Survival and development of manufacturing business depends on how the companies make competitive product and put out on the market. In other words, the core competitiveness of manufacturing business is "product" and companies’ competitiveness and sustainable growth could be guaranteed by management from product deveIopment to release onto the market. However, there are few examples about establishing successful system yet. The purpose of this study is to analyze on how PLM affect the performance. In particular, findout how iIlegal copying of PLM system that contains know-how gives company negative effect and emphasize why companies should recognize the importance of iIIegaI copying.

Business processes are often of long duration, and include internal worker's decision making, which makes business processes to be exposed to many exceptional situations. These properties of business processes makes it difficult to guarantee successful termination of business processes at the design phase. The behavioral properties of business processes mainly depends on the data aspects of business processes. To formalize the data aspect of process modeling, this paper proposes a graph-based model, called Data Dependency Graph (DDG), constructed from dependency relationships specified between business data. The paper also defines a mechanism of describing a set of mapping rules that generates a process model semantically equivalent to a DDG, which is accomplished by allocating data dependencies to component activities.