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Understanding the Stick Slip Behavior of Plastics and Target Setting: An OEM Perspective
Technical Paper
2019-01-1465
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Automotive OEMs are aggressively using different materials for interiors due to value proposition and variety of options available for customers in market. Excessive usage of different grade plastics with zero gap philosophy can cause stick slip effect leading to squeak noise. Even though systems and subsystems are designed using best practices of structural design and manufacturing tolerances, extreme environmental conditions can induce contacts leading to squeak noise. Appropriate selection of interface material pairs can minimize the possibilities of squeak conditions. Stick-slip behavior of different plastics is discussed in the present study, along with critical parameters during material compatibility testing in a tribological test stand. Friction coefficient of different material pairs for a defined normal load and sliding velocity are analyzed for patterns to recognize squeaks versus time. An OEM perspective is presented with focus on material selection using objective metrics like coefficient of friction and set targets for raw material suppliers regarding compatible materials. In the next step, an algorithm based on machine learning approach has been developed for estimating stick-slip performance.
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Authors
Citation
Somasundharam, S., Kumar, A., Mohammed, R., and Raghavendran, P., "Understanding the Stick Slip Behavior of Plastics and Target Setting: An OEM Perspective," SAE Technical Paper 2019-01-1465, 2019, https://doi.org/10.4271/2019-01-1465.Data Sets - Support Documents
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References
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- Chowdhury , M.A. , Nuruzzaman , D.M. , Mia , A.H. , and Rahaman , M.L. Friction Coefficient of Different Material Pairs under Different Normal Loads and Sliding Velocities International Journal of Mechanical and Mechatronics Engineering 11 53 57 2011
- Zuleeg , J. How to Measure, Prevent, and Eliminate Stick-Slip and Noise Generation with Lubricants SAE Technical Paper 2015-01-2259 2015 10.4271/2015-01-2259
- see.stanford.edu https://see.stanford.edu/materials/aimlcs229/transcripts/MachineLearning-Lecture