TOLERANCE ALLOCATION USING NEURAL NETWORKS

The purpose of tolerance allocation is to find a combination of tolerances to individual components such that the assembly tolerance constraint is met with minimum production cost. There are several methods available to allocate or apportion the assembly tolerance to individual parts. Some of the most common methods use linear programming, Lagrange multipliers, exhaustive search and statistical distributions. However, all the methods have some limitations. Moreover, most of these methods cannot account for the frequently observed mean shift phenomena that occur owing to fool wear, chatter, bad coolant, etc. This paper presents a neural networks-based approach for the tolerance allocation problem considering machines' capabilities, and mean shifts. The network is trained using the backpropagation learning method and used to predict individual part tolerances.