Technology prediction of a 3D model using Neural Network

TL;DR

This paper presents a neural network-based method that predicts manufacturing times directly from 3D models by converting them into images, achieving high accuracy and improving scheduling in dynamic production environments.

Contribution

It introduces a novel data-driven approach that uses 2D renderings of 3D models and a neural network inspired by GQN to estimate production durations, enhancing flexibility over traditional methods.

Findings

Mean absolute error below 3 seconds

Effective across varied product types

Improves manufacturing scheduling accuracy

Abstract

Accurate estimation of production times is critical for effective manufacturing scheduling, yet traditional methods relying on expert analysis or historical data often fall short in dynamic or customized production environments. This paper introduces a data-driven approach that predicts manufacturing steps and their durations directly from 3D models of products with exposed geometries. By rendering the model into multiple 2D images and leveraging a neural network inspired by the Generative Query Network, the method learns to map geometric features into time estimates for predefined production steps with a mean absolute error below 3 seconds making planning across varied product types easier.