생산 공정 자료 기반 양산단계 전차 전장관리체계 환경 부하 선별 시험 방법 및 적용 개선에 관한 연구
A study on Mass production stage Tank Battle Management System Environmental Stress Screening test method and application improvement based on Production process data
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Purpose: In this study, we apply environmental stress screening (ESS) to battle management system (BMS) of a tank and use the ESS profile based on production process data, guided by MIL-HDBK-781/344/2164. Methods: To optimize ESS Profile of the BMS of a tank, we estimate ESS model parameters (e.g., defect density, screening strength) using primary production failure reporting and corrective action system (FRACAS) data of military supply contract firm. Results: First, we collect the Primary production FRACAS data of military supply contract firm. Second, we compute curve fitting approach to find patent defect density and latent defect density using FRACAS data. Third, we solve the equation of Defect Density(patent defect density + latent defect density)(DIN) and Screening Strength(SS) Using second step data. As a result of analysis according to the order, we calculate DIN(Temperature stress case : 74.02, Vibration stress : 10.252) and : SS(Temperature stress case : 0.4632, Vibration stress : 0.4142) and confirm the Condition II-D based on MIL-HDBK-344. According to Condition II-D, it is necessary to modify existing ESS profile through decreasing the DIN and increasing the SS . Conclusion: Identification of defect causes through ESS approach reduce defect densities for production. It provides feedback to a lessons-learned data base to avoid similar problems on next generation tank BMS.
Abstract
Purpose: In this study, we apply environmental stress screening (ESS) to battle management system (BMS) of a tank and use the ESS profile based on production process data, guided by MIL-HDBK-781/344/2164. Methods: To optimize ESS Profile of the BMS of a tank, we estimate ESS model parameters (e.g., defect density, screening strength) using primary production failure reporting and corrective action system (FRACAS) data of military supply contract firm. Results: First, we collect the Primary production FRACAS data of military supply contract firm. Second, we compute curve fitting approach to find patent defect density and latent defect density using FRACAS data. Third, we solve the equation of Defect Density(patent defect density + latent defect density)(DIN) and Screening Strength(SS) Using second step data. As a result of analysis according to the order, we calculate DIN(Temperature stress case : 74.02, Vibration stress : 10.252) and : SS(Temperature stress case : 0.4632, Vibration stress : 0.4142) and confirm the Condition II-D based on MIL-HDBK-344. According to Condition II-D, it is necessary to modify existing ESS profile through decreasing the DIN and increasing the SS . Conclusion: Identification of defect causes through ESS approach reduce defect densities for production. It provides feedback to a lessons-learned data base to avoid similar problems on next generation tank BMS.
참고문헌 (7)
- Jang-Eun Kim, "ESS process standardization study of Tank", Armed car battle management system, 2015, 415~
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