Product and design feature-based similar process retrieval and modeling for mold manufacturing

Abstract

The design, machining, and assembly processes used to manufacture molds are knowledge-intensive, costly, and time-consuming operations requiring considerable human resources. Mold manufacturing is a typical make-to-order system. The characteristics of new products (molds or products produced using molds) determine the process and resource plan for mold manufacturing. Therefore, it is essential to store and inherit knowledge regarding products or mold design and process planning. In this study, we extract product features from images of products. We measure the similarity between features of new products and those of products previously manufactured. The clustering method is used to search for similar processes and resources among previously manufactured products. Moreover, design characteristics such as mold type, structure, and size, obtained from design data, are used to support this search for similar processes and resources. Herein, we propose a framework for a similarity retrieval model based on results of the product and design feature extraction. Convolutional neural networks and an agglomerative hierarchical clustering algorithm are used to derive similar processes. Furthermore, we propose a semiautomatic procedure to construct a collaborative process model that includes product, process, and resource information. This collaborative process modeling supports the visualization and analyses of complex processes for new products. Through this study, we endeavor to construct a collaborative process model by formulating it as an optimization problem.