Geometry Optimization to Improve Helical Springs Reliability: An Approach based on Material Variabilities
Optimisation Géométrique pour améliorer la fiabilité de ressorts hélicoïdaux : une approche basée sur les variabilités matériau
Résumé
Helical compression springs are known for their linear force-length relation and are used in all industries. Standards equations enable to design the spring according to geometric and operating specifications. However, it is often observed that operators have difficulties to produce springs respecting simultaneously all the customer constraints. This phenomenon is mainly due to the errors in the standards analytical equation to describe the stiffness and to the material variabilities of the supplied wire. Operators must experimentally retrieve the desired stiffness by modifying coiling machine inputs, leading to the significant loss of both tuning time and raw materials. This study presents a robust algorithm aimed at improving the reliability of helical compression springs by systematically compensating for material variabilities through geometric optimization and machine tuning. Two representative cases are considered: a spring fabricated from the stiffest possible wire imposed by supplier variabilities (diameter and material properties) and another from the softest wire . By identifying geometric configurations that ensure both springs meet strict mechanical constraints and deliver a consistent load-length response centered around a target behavior, the method ensures high reliability despite inherent variabilities. Furthermore, if valid designs are found for these extreme cases, then reliable solutions exist for all intermediate wire configurations, guaranteeing for any wire characterization a reliable spring design. The algorithm has been successfully confronted with industrial application. The proposed methodology provides a powerful tool for designers seeking to optimize spring specifications respect under uncertainty and for operators seeking to tune their coiling machines more efficiently.
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