Accelerated Life Testing of Thermoplastic Polyurethane Encapsulants Used in Underwater Acoustic Sensor

Eun Yeob Choi; Jung-Chae Shin; Jae Young Lee; Mu Hyeon Kim; Chang Keun Kim

Macromolecular Research, Vol.28, No.5, 510-516, May, 2020

DOI10.1007/s13233-020-8066-4 Export Citation

Accelerated Life Testing of Thermoplastic Polyurethane Encapsulants Used in Underwater Acoustic Sensor

Eun Yeob Choi, Jung-Chae Shin¹, Jae Young Lee¹, Mu Hyeon Kim, and Chang Keun Kim^†

School of Chemical Engineering & Materials Science, Chung-Ang University, 221 Huksuk-dong, Dongjak-gu, Seoul 06974, Korea
¹Hanwha corporation/defense, 264-36 Sanho-Daero, Gumi, Gyeongbuk 39370, Korea

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The lifetime of thermoplastic polyurethane (TPU) encapsulants used in underwater acoustic sensor (UAS) was predicted using accelerated life testing (ALT). The TPU specimens for tensile and tear strength tests were aged at six different temperatures, and the strengths of the aged specimens were then measured. The measured tensile and tear strengths as a function of aging temperature were used as ALT data for the analysis of the TPU encapsulant lifetime using ReliaSoft’s ALTA software. For the analysis of ALT data, the acceleration models and the life distribution models in the ALTA software were combined. The maximum likelihood estimation (MLE) method was used to find the most reliable acceleration-life distribution model when interpreting ALT data for tensile and tear strengths. It reveals that the Arrhenius-Weibull distribution model best fits the ALT data of the TPU encapsulant. The lifetime of the TPU encapsulant for tensile and tear strength was estimated using the Arrhenius-Weibull distribution model. The estimated results exhibited that the lifetime for tear stress was much shorter than that for tensile stress at 25 °C. Tensile and tear stresses are applied simultaneously to the TPU encapsulant during UAS operation. Thus, its replacement time should be determined by its tear strength lifetime.

Keywords:polyurethane;accelerated life testing;acceleration-life distribution model;maximum likelihood estimation;tensile and tear stresses

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[Referenced By]

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[1] Cayirci E, Tezcan H, Dogan Y, Coskun V, Ad Hoc Netw., 4, 431 (2006)

[2] Abraham BM, Oceans, 2006, 1 (2006)

[3] Capps RN, J. Acoust. Soc. Am., 78, 406 (1985)

[4] Chung YC, Bae CH, Kim DE, Choi JW, Chun BC, Macromol. Res., 27(6), 614 (2019)

[5] Ling L, Li J, Zhang G, Sun R, Wong CP, Macromol. Res., 26(4), 365 (2018)

[6] Campopiano S, Cutolo A, Cusano A, Giordano M, Parente G, Lanza G, Laudati A, Sensors, 9, 4446 (2009)

[7] Saldana M, Llorens CD, Felis I, Martinez-Mora JA, Ardid M, Sensors, 16, 1210 (2016)

[8] Brown M, IEEE Electr. Insul. Mag., 4, 21 (1991)

[9] Wise J, Gillen KT, Clough RL, Polymer, 38(8), 1929 (1997)

[10] Chen Y, Davalos JF, Ray I, J. Compos. Constr., 10, 279 (2006)

[11] Ngolemasango FE, Bennett M, Clarke J, J. Appl. Polym. Sci., 110(1), 348 (2008)

[12] Colin X, Audouin L, Verdu J, Rozental-Evesque M, Rabaud B, Martin F, Bourgine F, Polym. Eng. Sci., 49(8), 1642 (2009)

[13] Choudhury A, Bhowmick AK, Soddemann M, Polym. Degrad. Stabil., 95, 2555 (2010)

[14] Woo CS, Choi SS, Lee SB, Kim HS, IEEE Trans. Reliab., 59, 11 (2010)

[15] Larche JF, Bussiere PO, Gardette JL, Polym. Degrad. Stabil., 96, 1530 (2011)

[16] Mehr MY, van Driel WD, Zhang GQ, Microelectron. Reliab., 54, 1544 (2014)

[17] Nakada M, Miyano Y, J. Compos. Mater, 49, 163 (2015)

[18] Leveque D, Schieffer A, Mavel A, Maire JF, Compos. Sci. Technol., 65, 395 (2005)

[19] Reliasoft Corporation. Life Data Analysis Reference, Reliasoft Publishing, Tucson, AZ, 2014.

[20] Reliasoft Corporation. Accelerated Life Testing Reference, Reliasoft Publishing, Tucson, AZ, 2015.

[21] Modarres M, Amiri M, Jackson C, Probabilistic Physics of Failure Approach to Realiablity, John Wiley & Sons, New York, 2017.

[22] Escobar LA, Meeker WQ, Stat. Sci, 21, 552 (2006)

[23] Wang Y, Zhou B, Ge T, Feng H, Tao W, Appl. Math. Inf. Sci., 8, 1725 (2014)

[24] Nelson WB, Accelerated Testing, John Wiley & Sons, New York, 1990.