An Automated Engineering Assistant: Learning Parsers for Technical Drawings

TL;DR

This paper presents an automated engineering assistant that learns parsers for technical drawings using neural and symbolic methods, enabling improved interpretation, reasoning, and design search capabilities for engineers.

Contribution

It introduces a novel approach combining neural and symbolic techniques to automatically learn parsers from technical drawings and expert knowledge.

Findings

Developed a similarity-based search algorithm for design retrieval.

Successfully integrated neural and symbolic methods for drawing interpretation.

Enhanced engineering design processes with automated parsing and reasoning.

Abstract

From a set of technical drawings and expert knowledge, we automatically learn a parser to interpret such a drawing. This enables automatic reasoning and learning on top of a large database of technical drawings. In this work, we develop a similarity based search algorithm to help engineers and designers find or complete designs more easily and flexibly. This is part of an ongoing effort to build an automated engineering assistant. The proposed methods make use of both neural methods to learn to interpret images, and symbolic methods to learn to interpret the structure in the technical drawing and incorporate expert knowledge.