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.

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.

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.

In this research, the applicability of modified fouling index (MFI) on ultrapure water (UPW) production system was assessed to predict performance of reverse osmosis (RO) process. The practical study on MFI-UF was first performed at a pilot-scale UPW plant (Hwaseong-si, Gyeonggi-do, Korea), monitoring water quality parameters (i.e., conductivity, turbidity and TOC) as well as MFI-UF of pretreatment stage for 10 months. While water quality parameters were maintained in a stable manner, the MFI-UF was fluctuated implying the different propensity of RO influent. The increment of fouling potential was intimately related with RO performance, the aggravation of permeate quality. The sensitivity of MFI-UF was also verified by evaluating the fouling potential of reclaimed water in UPW production system.

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.

When monitoring an instrumental process, one often collects a host of data such as characteristic signals sent by a sensor in short time intervals. Characteristic data of short time intervals tend to be autocorrelated. In the instrumental processes often the practice of adjusting the setting value simply based on the previous one, so-called ‘adjacent point operation’, becomes more critical, since in the short run the deviations are harder to detect and in the long run they have amplified consequences. Stochastic modelling using ARIMA or AR models are not readily usable here. Due to the difficulty of dealing with autocorrelated data conventional practice is resorting to choosing the time interval where autocorrelation is weak enough then to using I-MR control chart to judge the process stability. In the autocorrelated instrumental processes it appears that using the Shewhart chart and the time interval data where autocorrelation is relatively not existent turns out to be a rather convenient and very useful practice to determine the process stability. However in the autocorrelated instrumental processes we intend to show that one would presumably do better using the EWMA control chart rather than just using the Shewhart chart along with some arbitrarily intervalled data, since the former is more sensitive to shifts given appropriate weights.

  This paper develops a process monitoring system for real-time process control. The practical case is studied on a small and medium marine equipment company. For business process reengineering of the company, we adopt an approach based on information eng

Assignable causes producing temporary deviation from the underlying system can influence on process adjustment and process monitoring in dynamic feedback control system. In this paper, the impact of assignable causes on EWMA forecasts and process adjustment which is based on the EWMA forecasts are derived for optimum control methods.