Work Item
ASTM WK76983

New Practice for Additive Manufacturing -- Powder Bed Fusion -- Best Practice for In-situ Defect Detection and Analysis

1. Scope

This standard provides guidelines and best practices for in-situ defect detection during laser beam powder beam diffusion (LB-PBF), when photodiode(s) are used for process monitoring. This standard defines methods and procedures for collecting datasets, calibration/correction factors, de-sampling, data analytic best practices/algorithms, verification and validation through experimental analysis. The standard provides acceptance criteria for validating the defect prediction carried through the best practices used for data analysis.

Keywords

in-situ monitoring; quality assurance; am

Rationale

In the last decades, additive manufacturing has been promoted from a prototyping to a series and mass production platform. Like all conventional techniques, quality assurance procedures/tools are of the utmost importance in aiding manufacturers in quality management and certification. For this purpose, in-line melt pool monitoring devices, installed in laser-based AM systems, provide vital real-time information about process characteristics, implicitly or explicitly leading toward understanding the quality of printed parts. This standard covers the required steps and approaches to effectively analyze signals collected from photodiodes mounted in laser beam powder bed fusion systems and correlate them with defects induced in metallic parts. The standard provides best practices for defect-detection platforms using in-situ monitoring of light intensity emitted from the melt pool of laser beam powder bed fusion to detect pores initiated during the process. Guidelines driven by correlating disturbances in the light intensity emitted from the melt pool to actual pores identified through a post-processing micro-computed tomography (CT) scanning are crucial and will be provided by this standard.

The title and scope are in draft form and are under development within this ASTM Committee.

Details

Developed by Subcommittee: F42.05

Committee: F42

Staff Manager: Pat Picariello

Work Item Status

Date Initiated: 05-19-2021

Technical Contact: Ehsan Toyserkani

Item: 000

Ballot:

Status: