How to solve it: modern heuristics
How to solve it: modern heuristics
NEURAL NETWORKS AND OPTIMIZATION ALGORITHMS APPLIED FOR CONSTRUCTION OF LOW NOISE TREAD PROFILES
Cybernetics and Systems
Intelligent reduction of tire noise
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
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Reduction of noise is a growing subject of interest in the automotive industry, especially in tire manufacturing. After construction of the basic tire design, that is design of the material and the basic building blocks called pitches, the last step in noise engineering of a tire is the determination of the pitch sequence of a tire. In this step the different types of pitches are put together regarding several constraints. Since there are a combinatorial number of valid pitch sequences, the goal is to find a valid pitch sequence with optimal noise characteristics. Due to the complexity of the problem, the globally optimal pitch sequence cannot be found by exhaustive search and intelligent algorithms such as Heuristic Optimization Algorithms, have to be used in order to find at least a locally optimal pitch sequence. Several successful approaches for this problem can be found in the literature for tires consisting out of just one pitch sequence. In this work tires consisting out of multiple pitch sequence (several tracks) are considered. We show how we can use algorithms for single track optimization and how we can combine them best for finding noise optimal tire designs for multiple pitch track tires.