Statistical Analysis and Probabilistic Characterization of the Mechanical Behavior of ABS: Application of Student’s t-Distribution and Weibull Distribution

This study, presents a detailed of a statistical study of how acrylonitrile butadiene styrene (ABS) behaves mechanically when subjected to uniaxial tensile testing. By combining Student’s t-distribution with the Weibull distribution, we capture the variability in mechanical properties and build robust reliability models. Our analysis covers both flawless specimens and those containing geometric flaws, such as notches and holes. From the data, we identified crucial statistical parameters, established 90% confidence intervals, and set design guidelines to ensure a 99% probability of survival. Importantly, the study highlights how these geometric defects significantly weaken the material’s load capacity. This probabilistic framework equips engineers with valuable insights to optimize ABS component design and reliably predict their performance in real-world applications.

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Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-10-27
DOI
https://doi.org/10.1051/matecconf/202541501010/pdf
Primary Topic
Probabilistic and Robust Engineering Design
Type
article
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article

Statistical Analysis and Probabilistic Characterization of the Mechanical Behavior of ABS: Application of Student’s t-Distribution and Weibull Distribution

Amal Lamarti, Jamila Bouchgl, Abdelilah Hachim
Springer Link (Chiba Institute of Technology)
Probabilistic and Robust Engineering Design
article

Statistical Analysis and Probabilistic Characterization of the Mechanical Behavior of ABS: Application of Student’s t-Distribution and Weibull Distribution

Amal Lamarti, Jamila Bouchgl, Abdelilah Hachim
article en

Abstract

This study, presents a detailed of a statistical study of how acrylonitrile butadiene styrene (ABS) behaves mechanically when subjected to uniaxial tensile testing. By combining Student’s t-distribution with the Weibull distribution, we capture the variability in mechanical properties and build robust reliability models. Our analysis covers both flawless specimens and those containing geometric flaws, such as notches and holes. From the data, we identified crucial statistical parameters, established 90% confidence intervals, and set design guidelines to ensure a 99% probability of survival. Importantly, the study highlights how these geometric defects significantly weaken the material’s load capacity. This probabilistic framework equips engineers with valuable insights to optimize ABS component design and reliably predict their performance in real-world applications.

Springer Link (Chiba Institute of Technology)
Openalex Percentile: Top 24%
Probabilistic and Robust Engineering Design
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