Abstract
In this paper, a CAD integrated method is proposed for automatic recognition of potential weld locations in large assembly structures predominantly comprised of weld joints. The intention is to reduce the total man-hours spent on manually locating, assigning, and maintaining weld-related information throughout the product life cycle. The method utilizes spatial analysis of extracted stereolithographic data in combination with available CAD functions to determine whether the accessibility surrounding a given intersection edge is sufficient for welding. To demonstrate the method, a system is developed in Siemens NX using their NXOpen Python API. The paper presents the application of the method to real-life use cases in varying complexity in cooperation with industrial partners. The system is able to correctly recognize almost all weld lines for the parts considered within a few minutes. Some exceptions are known for particular intersection lines located deep within notched joints and geometries weldable through sequential assembly, which are left as a subject to further works.
Highlights
The demand for individualized and customized products calls for flexible and intelligent development processes
Systems created with knowledge-based engineering (KBE) aim to provide the flexibility and automation to the product development processes by capturing and characterizing engineering intent behind decisions
The general idea is that cross-sections can be generated and further analyzed using a set of potential weld lines that have been derived from intersections between solid bodies inside a CAD environment
Summary
The demand for individualized and customized products calls for flexible and intelligent development processes. In such industries, a desire has risen to adopt robotic systems to automate the welding processes and further increase production efficiency. Systems created with knowledge-based engineering (KBE) aim to provide the flexibility and automation to the product development processes by capturing and characterizing engineering intent behind decisions. This captured knowledge, or logic, can be redeployed at a given time to automate decisions and design parameters, and provide smarter and more optimized solutions taking available information into account. In order to build such systems, methods for recognizing of specific features are considered a necessity [1, 2]
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