Since the National R&D Innovation Act was enacted in 2022, it became a crucial issue how to qualify or improve R&D activities and disseminate their outcomes. Many organizations have referred to various quality management standards such as the American National Standards Institute/American Society for Quality (ANSI/ASQ) Z1.13, International Organization for Standardization (ISO) 9001, and the American Society of Mechanical Engineers Nuclear Quality Assurance-1 (ASME NQA-1), as a means to set up their own quality system. ISO is the international standard for implementing a quality management system (QMS), which provides a framework and principles for managing an organization’s QMS, with the aim of ensuring that the organization consistently provide products or services that meet regulatory requirements. ISO 9001 can cover all aspects of an organization’s operations, and it can also be expanded to include R&D areas. The introduction of ISO 9001 to R&D aims to improve R&D practices and establish a standardized process framework for conducting R&D. ANSI/ASQ Z1.13 provides quality guidelines for research and consists of 10 sections covering various aspects of research quality, emphasizing ethical conduct, clear objectives, reliable data collection, and analysis. ASME NQA-1 is one of quality assurance standards for nuclear facility applications, but it has been extended and applied to R&D activities in the nuclear fields. It just focuses on planning, procedures, documentation, competence, equipment, and material control. KINAC has conducted extensive research on verifying and regulating nuclear activities while providing support for national nonproliferation technologies and policies. In addition to the quantitative growth achieved so far, efforts are being made to establish a qualitative and integrated management system. As a first step to achieve this goal, this study reviewed international standards and methodologies for research quality and derived the key components for R&D quality management. Moreover, the appropriate outline of quality management system framework was proposed for R&D as a regulatory support process, based on the ISO 9001. The implementation of quality management standards and procedures for R&D in KINAC, which could lead to improved research practices, more reliable data collection and analysis and increased efficiency in conducting R&D activities.

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.

본 연구에서는 하수 재이용을 위한 역삼투막 공정에서 전처리 정밀여과막(MF) 손상에 대한 누출되는 다양한 수 질변화로써 막 손상 검지 방안을 제시하였다. 이를 위하여 역삼투막 유입수질 적합성 평가지표인 SDI (silt density index)를 3에서 5의 범위 내에서 막 손상 시 검지 감도를 정량화하기 위하여 전처리 분리막이 1에서 3가닥 파단에 따라 SDI는 1.92에 서 6.11까지 증가한 결과를 확인할 수 있었다. 일반적으로 3을 기준으로 역삼투막 유입수질로 설정하였을 때 분리막이 3가닥 까지 파단이 되어야만 막 손상 검지가 가능하다는 것을 의미하며 역삼투막의 오염은 잠재적으로 가속화되어 효율을 저하시 킬 수 있다. 또한 이때 누출되는 입자성과 유기물질에 대하여 0.45 μm 이상의 크기만 걸러주는 입자계수는 입도분포별 막 파 단 개수에 따라 일정한 패턴을 확인할 수 없었으며, TOC 농도는 약 2배의 변화패턴으로써 SDI와의 상관관계로써 TOC가 막 손상 수질지표로써 신뢰성이 높은 것으로 확인되었다. 수질분석결과와 더불어 USEPA에서 제시하는 막 손상 검지 방법 중 압력손실시험과 이를 기반으로 LRVDIT 모델의 적합성 평가를 한 결과 막 손상 또는 역삼투막 공정으로 유입되는 막오염물질 을 신속하게 확인할 수 있는 SDI 및 TOC를 포함한 LRVDIT 모니터링과 UCL 설정을 병행해야 한다.

In this paper, we investigate the requirements of QPA(Quality Process Audit), which is a process quality audit system for secondary defense contractors, compared with those of DQMS(Defense Quality Management System). And evaluate whether the deployment of QPA meets the DQMS certification requirements through the case example of Company H. The evaluation items of QPA are composed of five categories such as Material Management, Incoming Inspection, Manufacturing Process, Product Evaluation, and Packaging Management. The QPA requirements are mainly related to the chapter 7(support) and chapter 8(operation) of DQMS standards. In this view point, QPA can be expected as an effective audit for suppliers preparing for DQMS certification. In the case example, we evaluate the results and effects of improvement due to QPA and compare it with the case of DQMS. QPA can be used as appropriate quality management standards of secondary and tertiary defense contractors and can provide the basis guidelines for the preparation of implementation steps in DQMS certification.

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

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.

목적: 본 연구는 국내의 기성 돋보기가 광학적 기준에 부합여부와 조제가공 상태의 대칭성에 대하여 측정 평가하고자 하였다. 방법 : 국내에 유통 중인 한 종류의 기성 돋보기 100개(+1.00, +2.00,+3.00 와 +4.00 D)를 대상으로 측정하였다. 기성 돋보기의 광학적 품질은 렌즈 굴절력, 광학중심점간 거리, 광학중심점높이가 측정되었다. 측정된 값의 허용오차는 ISO 8980-1, ISO 16034:2002 그리고 RAL-RG-915를 기준으로 분석되었다. 인위적으로 발생한 수평수직 프리즘은 광학중심점간 거리와 남녀 평균동공간 거리의 오차로 프렌티스 공식으로 계산하였다. 추가적으로 기성 돋보기의 조제과정상 오류로 인한 비대칭성은 단안광학중심점간 거리와 두 렌즈간의 광학중심점간 높이의 오차로 측정하였다. 결과 : 100개의 기성 돋보기 중 23%가 ISO 8980-1의 렌즈굴절력 기준을 만족하지 못하였다. 본 연구의 기성 돋보기의 광학중심점간 거리는 62.06±1.41 mm이었으며, 남녀의 평균 동공간 거리와의 오차로 발생하는 수평프 리즘은 남성은 0.53±0.54 △ BI(0.18~1.06 △), 여성은 0.98±0.68 △ BI(0.37~1.78 △). 테의 중심에서 각 렌 즈광학중심점까지의 거리에서는 85%가 최소 1 mm이상의 오차를 보였다. 기성 돋보기 양쪽 렌즈 간 광학중심점 높이 차이는 1.26±0.83 mm이었으며, 이로 인한 발생되는 수직프리즘은 0.12~0.45 △이었다. 결론 : 많은 수의 기성 돋보기가 요구되는 광학적 품질에 미달되었다. 기성 돋보기는 착용자 개인별 안면형상을 고려하지 못하기 때문에, 광학중심점간 거리와 동공간거리의 오차가 발생하고 이로 인한 많은 정도의 수평프리 즘이 발생하였다. 따라서 광학적으로 잘못된 기성 돋보기의 사용은 시각적 편안함을 제공하는 것보다는 시각적 부담을 야기할 수 있을 것이며, 이런 문제를 방지하기 위하여 전문가를 통한 확인 과정이 반드시 필요하다.

Recently, due to the aging of workers and the weakening of the labor base in the automobile industry, research on quality inspection methods through ICT(Information and Communication Technology) convergence is being actively conducted. A lot of research has already been done on the development of an automated system for quality inspection in the manufacturing process using image processing. However, there is a limit to detecting defects occurring in the automotive sunroof sealer application process, which is the subject of this study, only by image processing using a general camera. To solve this problem, this paper proposes a system construction method that collects image information using a infrared thermal imaging camera for the sunroof sealer application process and detects possible product defects based on the SVM(Support Vector Machine) algorithm. The proposed system construction method was actually tested and applied to auto parts makers equipped with the sunroof sealer application process, and as a result, the superiority, reliability, and field applicability of the proposed method were proven.

This study suggests a machine learning model for predicting the production quality of free-machining 303-series stainless steel small rolling wire rods according to the manufacturing process's operation condition. The operation condition involves 37 features such as sulfur, manganese, carbon content, rolling time, and rolling temperature. The study procedure includes data preprocessing (integration and refinement), exploratory data analysis, feature selection, machine learning modeling. In the preprocessing stage, missing values and outlier are removed, and variables for the interaction between processes and quality influencing factors identified in existing studies are added. Features are selected by variable importance index of lasso regression, extreme gradient boosting (XGBoost), and random forest models. Finally, logistic regression, support vector machine, random forest, and XGBoost is developed as a classifier to predict good or defective products with new operating condition. The hyper-parameters for each model are optimized using k-fold cross validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963 and logarithmic loss of 0.0209. In this study, the quality prediction model is expected to be able to efficiently perform quality management by predicting the production quality of small rolling wire rods in advance.

Korean sliced rice cakes, or tteokguk, are conventionally dried and rehydrated during their preparation. In this study, the effects of the perforation process and various drying methods (e.g., hot-air drying, vacuum drying, low temperature drying, and freeze drying) on the quality characteristics of tteokguk (rice cake soup) were evaluated. In the experiment, the rehydration capacity and lightness increased as the pore number increased. The hardness, redness, and yellowness of tteokguk, in contrast, tended to decrease as perforations increased. The texture, taste, and overall acceptability scores of tteokguk increased as perforations increased. With respect to drying methods, the rehydration capacity was greatest for vacuum drying. The hardness of tteokguk was lowest for vacuum drying. The redness, yellowness, pH, and sensory characteristics did not differ significantly among tteokguk samples treated through various drying methods. These results suggest that high-quality ready-to-eat Korean sliced rice cakes could be created by perforation and vacuum drying.

본 연구는 인삼 잎의 이용증대를 위해 마이크로웨이브에 의한 인삼 잎의 잔류농약 추출효과와 발효 인삼 잎의 ginsenoside 유용 유도체의 전환 검토 및 품질 특성을 분석 하였다. 인삼 잎에 잔류되어 있는 tolclofos-methyl와 azoxystrobin을 microwave로 추출하기 위한 용매는 hexane이 가장 효율적 이었다. tolclofos-methyl와 azoxystrobin이 잔류되어 있는 인삼 잎에서의 microwave를 이용한 추출 최적 조건은 power 50∼95 watts, 추출용매는 hexane, 추출시간은 3분으로 나타났다. 인삼 잎 추출물의 발효에서 발효 전과 비교하여 Rg1과 Rb1은 감소한 반면 Rh1, Rg3, Rk1 및 Rh2는 발효 후 모두 증가한 것으로 나타났다. 특히 홍삼에서 대표적인 성분으로 알려져 있는 Rg3의 경우 발효전 2.77 ㎍/g에서 발효 후 균주의 종류에 따라 70.62∼77.61 ㎍/g으로 증가하였다. 7일간 발효 후 인삼 잎의 총 페놀성 화합물 및 전자공여능은 일부 균주에서는 발효전과 비교하여 감소하다가 다시 증가하는 경향을 나타내었으나, 발효가 진행됨에 따라 전반적으로 감소되는 경향을 나타내었다.