Earticle

With the development of CNC(Computer Numerical Control) and communication technology, the connotation and functions of Distributed Numerical Control have been greatly enlarged. In this study, we develop and implement a Distributed Numerical Control system that has real time and multi-tasking operation capability for the machining cell with various NC(Numerical Control) and CNC machines. With the consideration of economy, generalization and extension, this system is interfaced with Shop Floor Control System, Machine Control System and Tool Preparation System using advanced networking method. In the implementation phase, we use the ORACLE DBMS (Database Management System) as the DBMS and Microsoft Visual C++ as the programming tools.

이 연구는 대학들이 현재 구축 활용하고 있는 역량중심 교육과정의 학습성과 평가체계의 특징과 문제점을 탐색하고, 역량중심 교육과정을 재개념화함으로써 향후 역량중심 교육과정의 학습성과 평 가를 위해 도입할 수 있는 대안적인 평가 패러다임은 무엇인지 살펴보기 위한 목적으로 진행되었다. 이 연구의 결과는 다음과 같다. 첫째, 3개 국내 대학의 역량중심 학습성과 평가체계를 살펴봄으로써 기존의 역량중심 학습성과 평가의 특징을 성과중심 평가, 객관적 평가, 전문가중심 평가의 세 가지 로 추출하였다. 그리고 기존 평가 패러다임은 성취준거 이외의 성과를 놓치게 되고 교육활동의 개 선을 어렵게 하며, 전문가의 학문적 편향을 배제하지 못하고 평가 결과의 환류가 어렵다는 문제점 이 있음을 밝혔다. 둘째, 역량중심 교육과정을 인간의 외적인 행동이나 지식 등의 인지적 요소와 자 아개념, 가치관, 동기 등의 정의적인 요소의 총체적인 개념으로 재개념화하였다. 셋째, 이와 같은 역 량중심 교육과정에 대한 재개념화를 기반으로 향후 역량중심 학습성과 평가는 과정 모니터링이 가 능한 평가체제로 구축해야 함을 제안하였다. 또한 면담, 관찰, 사례연구 등의 질적이고 자연주의적인 방법을 보완적으로 활용하며, 이해관계자를 참여시키는 평가를 통해 평가결과의 환류를 촉진해야 함을 제안하였다.

This paper aims to explore that features and problems of existing evaluation approaches to see the change of students’ achievements and prove the courses’ effectiveness in competence-based curriculum on university, to examine what alternative evaluation approaches available for see the change of students’ achievements in it by reconceptualization of competence-based curriculum from now on. The results of this investigation was as follows. First, by study that evaluation system of students’ achievements on competence- based curriculum of three Korean universities, features of existing evaluation approaches organized the three most important characteristics of objective based approach, quantitative based approach, experts oriented approach. And existing evaluation approaches making that loss other applicable performance than criteria, difficult to curriculum’ improvement and hard to overcome academic bias of experts, difficult to feedback of evaluation’s results. Second, I highlights that competence-based curriculum clear need understanding of holistic concept to contain not only of human external behavior and cognitive factor as knowledge, but of affective components as self-concept, values, motivation. Third, based on reconceptualization competence-based curriculum as holistic approach, for see the change of students’ achievements of competence-based curriculum from now on, I propose that need evaluation system of process monitoring. And I propose that need qualitative and naturalistic evaluation to complementary using methods as interview, observation, case study, transforming evaluation based on stakeholder engagement.

Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance's importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.

Since grinding is a more complicated process than any other machining process, it is hard for operators to setup a grinding machine properly and to find out correctly abnormal grinding states resulting in damages to products. Abnormalities would be caused by improper setup, improper dressing/grinding conditions which are likely to be occurred without skilled operators' attention. In this study, an AE monitoring unit is developed to help operators conduct with ease setup, and set properly dressing/grinding conditions. AErms(root-mean-square) signal being monitored, on-going process states during grinding and dressing is visualized for machine operators to judge whether the processes are in good condition. Evaluation tests are carried out on centerless grinding machines-both cylindrical and internal. The developed AE monitoring system is verified to be useful to check grinding/dressing states in process even in the centerless grinding of which process is most unknown among various grinding methods because of the complex structure.

On-line process control equipment for CO$_{2}$ laser cutting is not available for industrial applications. The major part of the industrial laser cutting machines are adjusted off-line by highly educated engineers. The quality inspection of the sample is visual and referred to different quality scales. Due to the lack of automation the potential laser users hesitate to implement the cutting method. The first step toward an automation of the process is the development of a process monitoring system and the research is cincentrated on the area of on-line quality monitoring during CO$_{2}$ laser cutting. The method is based on the detection of the emitted light from the cutting front by photo diode. The developed monitoring system consists of the OP Amplifier, A/D convertor, power supply and PC. The signal from the photo diode has been undertaken from Fourier analysis and statistical analysis with real time. The photograph of striation pattern was taken by metallurgical microscope. As a result, it is possible to predict the striation pattern according to the beam traveling speed.

In highly automated milling process, in-process monitoring of the malfunction is indispensable to ensure efficient cutting operation. Among many malfunctions in milling process, chatter vibration deteriorates surface finish, tool life and productivity. In this study, the monitoring system of chatter vibration for face milling process is proposed and experimentally estimated. The monitoring system employs two types of sensor such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are extracted in time domain for the input patterns of neural network to reduce time delay in signal processing state. The resultes of experimental evaluation show that the system works well over a wide range of cutting conditions.

In order to achieve and maintain dimensional accuracy in laser micro-machining, dominant parameters such as laser power and laser focus position need to be monitored and controlled real time. Also, in order to selectively machine multi-layered materials, the material being presently machined need to be recognized. This paper presents an auto-focusing (AF) module to keep laser focus on a large-area surface; a real-time laser power stabilizing module based on optical attenuation; and a laser-induced breakdown spectroscopy (LIBS) module. With these monitoring modules, position error in laser focus on a 4" silicon wafer was kept below $4{\mu}m$, initially $51{\mu}m$, and laser power stability of a UV laser source was improved from 1.6% to 0.3%. Also, the material transition from polyimide to copper in machining of FCCL (flexible copper clad laminate) was successfully observed.

Recently, in order to achieve high flexibility of manufacture, monitoring and control strategies of a new type have been developed. Since the generation of built-up edge on the cutting tool damages the surface finish of the workpiece, the monitoring system of built-up edge is an important process monitoring. In this study, the analyzing methods of cutting force signal to detect the built-up edge during cutting process are described. The cutting force signals are analyzed using the mean, standard deviation and mean to standard deviation of this cutting signals. We can obtain the guide to detect the built-up edge during turning process.

This paper presents a methodology for In-Process monitoring of tool wear by using ultrasonic sensor in turning operation. An integrated single ultrasonic transducer operation at a frequency of 10MHz is placed in contact with the insert tip. The change in amount of the reflected energy from the nose and flank of the tool can be related to the level of tool wear and the mechanical integrity of the tool. As the results, the tool wear monitoring system based on the ultrasonic pulse-echo method was proposed, it is useful to determine a tool life and tool change time.

A monitoring system is necessary to make the polishing process more reliable in order to ensure the high quality and performance of the final products. Generally, AE (Acoustic Emission) is known to be closely related to the material removal rate (MRR). As the surface becomes rougher, the MRR and AE increase. Therefore, the surface roughness can be indirectly estimated using the AE signal measured during the polishing. In this study, an AE sensor-based monitoring system was fabricated to detect the very small AE signal resulting from the friction between a tool and a workpiece during polishing. The performance of this monitoring system was estimated according to polishing conditions, the relation between the level of the AE RMS and the surface roughness during the polishing was investigated.

The shoes process management monitoring system for supporting SCM is developed in this study. This system consists of a monitoring program, a conveyer system, a pneumatic unit, a controller, an USB camera, and a server and a client computer. To operate the developed system easily, the monitoring program using LabVIEW in the Windows environment is developed. This program consists of 5 modules: production management, inventory control, media management, defective management, and communication management. The developed system has several advantages: reduced time for managing process work, decreased labor costs, effective operation, and continuous work without an operator. Nowadays advanced manufacturing companies are trying to find a way to check the performance of their production equipments and plants from remote sites. Thus, to manage the developed system from remote sites, communication network is constructed. In order to evaluate the performance of the monitoring system, experiments were performed. The experimental results showed that the developed system provided a reliable performance and a stable communication.

ISRM(International Society of Rock Mechanics) 표준시험법에 의해 24시간 이상 105±3℃를 유지할 수 있는 건조로에서 암석시험편을 건조시키며 무게의 변화를 모니터링할 수 있는 시스템을 구축하고, 무게 변화 모니터링 결과로부터 점근선법에 의한 고체무게 산출법을 제안하였다. 무게 모니터링에 의한 점근선법에 의하면 이론적으로 무한한 시간동안 건조시켰을 경우에 해당되는 이상적인 고체무게를 얻을 수 있다. 또한 표면건조수포화 시료를 이용하여 모니터링을 시작할 경우에는 모니터링 자료부터 건조밀도, 유효공극률 및 수포화 함수율을 모두 산출할 수 있다. 강화군 석모도에서 채취한 암석을 이용하여 ISRM 표준시험법과 본 연구에서 제안한 점근선법에 의해 결정되는 고체무게를 비교한 결과, 0.1% 이상의 차이를 보이는 경우도 있으나 대부분의 암석에서 그 차이가 0.05% 이내로 매우 미미하였다. 그러나 고체무게에서의 이러한 미미한 차이가 산출되는 유효공극률 및 수포화 함수율에서는 매우 큰 차이로 나타나 20% 내외의 차이로 증폭되며 특수한 경우에는 40% 이상의 오차를 보이기도 하였다. 현대의 계측기술 및 디지털기술을 이용하면 본 연구에서 제안한 무게 모니터링 시스템의 구현이 가능하므로 무게 모니터링에 의한 점근선법은 정확한 고체무게 결정의 하나의 대안이 될 수 있을 것으로 판단된다.

A new method for determining solid weight has been suggested by monitoring the sample weight during drying process. By monitoring the weight decay in a specific time step during the drying process in an oven capable of maintaining a temperature of 105℃±3℃ for a period of at least 24 hours, as is suggested in ISRM (International Society of Rock Mechanics), an asymptote can be calculated by curve fitting. The asymptote can be an ideal solid weight, which can be assumed to be dried for infinite time period. Starting the monitoring from surface-dry saturated sample, effective porosity and water content can also be calculated directly from the monitoring data. Comparison of the solid weight determined by ISRM suggested method and by asymptote method showed very small difference, less than 0.05% difference in most cases of rock samples from Seokmo Island. However, resultant effective porosity and water content showed the difference as big as approximately 20%, which is not negligible any more. Considering advances of modern digital technology and measurement, monitoring the sample weight during the drying process is no more a high-tech. And the asymptote method by weight monitoring can be an alternative reflecting advances of technologies.

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.

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.

This paper introduces a new monitoring technique which utilizes an adaptive signal processing for feature generation, coupled with a multilayered merual network for pattern recognition. The cutting force signal in face milling operation was modeled by a low order discrete autoregressive model, shere parameters were estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(recursive least square) method with discounted measurements. The influences of the adaptation algorithm parameters as well as some considerations for modeling on the estimation results are discussed. The sensitivity of the extimated model parameters to the tool state(new and worn tool)is presented, and the application of a multilayered neural network to tool state monitoring using the previously generated features is also demonstrated with a high success rate. The methodology turned out to be quite suitable for in-process tool wear monitoring in the sense that the model parameters are effective as tool state features in milling operation and that the classifier successfully maps the sensors data to correct output decision.

PC 사용을 제어하고 관리하기 위하여 각 PC에서 실행하는 프로세스를 모니터링하는 시스템에 대한 연구는 여러 형태로 이루어져 왔다. 그러나 다수 PC에서 각 PC의 실행 프로세스를 모니터링하고 파일로 레포팅하는 방식의 시스템에 대한 연구는 찾아보기 어려운 실정이다. 이에 본 연구에서는 다수 PC 환경에서 각 PC를 모니터링하고 서버로 파일 레포팅하고, 레포팅 정보를 수합ㆍ정리하는 기능을 갖는 시스템을 설계 및 구현하고자 한다. 본 연구의 시스템을 이용할 경우 각 PC를 사용하는 학습자는 실행 프로세스가 관리자에게 레포팅된다는 것을 인지하게 되어 불필요한 프로세스 실행을 자제하는 효과를 가져올 수 있을 것으로 기대한다.

Many various researches about the PC execution process monitoring system have studied. Nevertheless, it is hard to find research of a system that multi-PC based each PC can be monitoring and reporting by file type. In this study, it designed and implemented that each PC can be monitoring and reporting by files to a server in multi-PCs' environments. Moreover it can gather and organize informations of reporting. It is expected users can recognize that execution processes are reporting. It causes to reduce execution unnecessary process.

프로세스 모니터링은 프로세스를 수행하는 과정에서 발생하는 조직 내외부에서 발생하는 다양 한 사건 관련 정보를 지속적으로 관측하여 이에 대한 실시간 접근성을 제공한다. 이를 위해 프로세스 진행 중에 기록되는 데이터와 최종 수행 결과 간의 상관관계를 추출하여 룰로 정의하고 룰의 조건부 를 탐색하는 접근법이 널리 사용된다. 그러나 이러한 룰 기반 모니터링을 실시간 프로세스 진행 상황 에 적용하는 경우, 지표의 부재 및 늦은 감지의 문제가 한계점으로 등장한다. 이를 해결하기 위하여 본 논문에서는 대체 기법을 도입한 실시간 프로세스 모니터링 방법론을 제안하였다. 대체 기법이란 전 체 데이터 테이블에서 누락된 데이터를 대신할 수 있는 특정 값을 찾기 위한 기법이다. 실시간으로 진 행 중인 프로세스를 모니터링하는 상황에서 관측 시점에 따라 필연적으로 존재하는 비관측 정보를 데 이터 결측으로 간주하고 대체 기법을 이용하여 발생 가능한 추후 진행 상태를 추정할 수 있다. 제안 된 대체 기반 실시간 프로세스 모니터링을 통해 프로세스 진행 중 전 관측 시점에 걸쳐 현 진행 시점 까지 수집된 정보에 근거한 최종 수행 결과를 실시간으로 예측할 수 있다.

Process monitoring provides a real-time access to critical process attributes generated during process executions. A widely used approach for process monitoring is a rule-based monitoring. It detects specific conditions about attributes causing corresponding results, which is defined in If (condition) Then (status) rule. However, when the rule-based approach is applied to a real-time process monitoring that attributes are recorded sequentially along with execution order of tasks, it shows some limitations like no indicator or late warnings. To improve these limitations, we propose a novel approach for real-time process monitoring by adopting imputation for treating unobserved attributes. Imputation aims at replacing missing or non-sampled attributes with any specific unit with similar characteristics. During the real-time monitoring, there exist unobserved attributes according to the real-time progress of ongoing processes, which can be considered as inevitable missing with monitoring period. In this situation, imputation methods can be used to assume probable progress after the period. Therefore, probable outcomes given the current progress of ongoing process can be predicted in real-time during entire monitoring periods.