Algorithm Development for Compression Predictions

Business Challenge

The process of tire manufacturing is the product of years of continuous research and advanced feats of engineering. The tire must achieve a balance between safety, comfort, traction, durability and efficiency. There are hundreds of components that contribute to the tire’s performance and fuel efficiency. Steel and synthetic cords make up its structural framework and provide thousands of pounds of resistance to reinforce the tire through every twist, turn and jerk.

During the manufacturing process, the cords are calendered into a composite sheet. Within these sheets the cords are subjected to compression, which if not careful, may result in the cords buckling and losing contact with each other. This creates a considerable reduction in the axial stiffness of the cord. The ability to accurately compute the compression and stiffness due to axial loads is therefore of central importance to tire wear and endurance.

 

SFL's Approach

SFL’s approach was to investigate the response of a cord subjected to the calendering process. We set up a simulated environment and worked with parameters such as the radii of a cord’s wires, pitch, composite temperature and various loads. SFL tested the axial response of a cord subjected to tensile and compressive loads with various complex cord cross-sections. We trained our model on the data in order to predict the conditions for cords buckling and found temperature to be an crucial determinant of its stability.

 

Business Value

Different temperatures provided varying benefits and risks for the cord’s structural integrity. Our model confirmed the optimal setup for the calendering process based on the aforementioned parameters. Verifying the consistency of our client’s tire manufacturing process is key to monitoring the company’s cost efficiency and safety regulations. Such understanding and modelling, can save tens of millions of dollars in costs and can be used to meet federal requirements. These types of analyses are beneficial for optimizing production and are extendable to many decisions companies can make.