Feature-Based and Process-Based Manufacturing Cost Estimation

Abstract

The demand for mass custom parts is increasing, estimating the cost of parts to a high degree of efficiency is a matter of great concern to most manufacturing companies. Under the premise of machining operations, cost estimation based on features and processes yields high estimation accuracy, but it necessitates accurately identifying a part’s machining features and establishing the relationship between the feature and the cost. Accordingly, a feature recognition method based on syntactic pattern recognition is proposed herein. The proposed method provides a more precise feature definition and easily describes complex features using constraints. To establish the relationships between geometric features, processing modes, and cost, this study proposes a method of describing the features and the processing mode using feature quantities and adopts deep learning technology to establish the relationship between feature quantities and cost. By comparing a back propagation (BP) network and a convolutional neural network (CNN) it can be concluded that a CNN using the “RMSProp” optimizer exhibits higher accuracy.