Characterization and Dataset Compilation of Torque–Angle Curve Behavior for M2/M3 Screws

Iván Juan Carlos Pérez-Olguín, Consuelo Catalina Fernández-Gaxiola (), Luis Alberto Rodríguez-Picón and Luis Carlos Méndez-González
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Iván Juan Carlos Pérez-Olguín: Department of Industrial Engineering and Manufacturing, Autonomous University of Ciudad Juarez, Av. Plutarco Elías Calles 1210, Fovissste Chamizal, Ciudad Juárez 32310, Mexico
Consuelo Catalina Fernández-Gaxiola: Department of International Logistics Engineering, Technological University of Ciudad Juarez, Av. Universidad Tecnológica 3051, Col, Lote Bravo, Ciudad Juárez 32695, Mexico
Luis Alberto Rodríguez-Picón: Department of Industrial Engineering and Manufacturing, Autonomous University of Ciudad Juarez, Av. Plutarco Elías Calles 1210, Fovissste Chamizal, Ciudad Juárez 32310, Mexico
Luis Carlos Méndez-González: Department of Industrial Engineering and Manufacturing, Autonomous University of Ciudad Juarez, Av. Plutarco Elías Calles 1210, Fovissste Chamizal, Ciudad Juárez 32310, Mexico

Data, 2024, vol. 9, issue 10, 1-37

Abstract: This research explores the torque–angle behavior of M2/M3 screws in automotive applications, focusing on ensuring component reliability and manufacturing precision within the recommended assembly specification limits. M2/M3 screws, often used in tight spaces, are susceptible to issues like stripped threads and inconsistent torque, which can compromise safety and performance. The study’s primary objective is to develop a comprehensive dataset of torque–angle measurements for these screws, facilitating the analysis of key parameters such as torque-to-seat, torque-to-fail, and process windows. By applying Gaussian curve fitting and Gaussian process regression, the research models and simulates torque behavior to understand torque dynamics in small fasteners and remarks on the potential of statistical methods in torque analysis, offering insights for improving manufacturing practices. As a result, it can be concluded that the proposed stochastics methodologies offer the benefit of fail-to-seat ratio improvement, allow inference, reduce the sample size needed in incoming test studies, and minimize the number of destructive test samples needed.

Keywords: torque study; torque–angle curve; process window; machine learning; Gaussian fitting model (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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