Sensitivity Analysis of the Laser Power Control System to Measurement Noise in SLS 3D Printers

TL;DR

This paper investigates how measurement noise affects the laser power control system in SLS 3D printers and compares traditional Monte Carlo methods with new uncertainty-tracked architectures for real-time analysis.

Contribution

It introduces a novel, low-latency uncertainty analysis method for laser power control in SLS printers, outperforming traditional Monte Carlo simulations.

Findings

Recent architectures enable sub-30 ms uncertainty analysis.

New method achieves similar accuracy to Monte Carlo with much lower latency.

Advances facilitate real-time control system uncertainty quantification.

Abstract

Uniform temperature distribution in Selective Laser Sintering (SLS) is essential for producing durable 3D prints. Achieving uniformity requires a laser power control system that minimises deviation of the printing temperatures from the target temperature. Because the estimate of the actual process temperature is an input to the laser power control, uncertainty in the estimate of the actual temperature can lead to fluctuations in laser power that affect the thermal performance of the SLS. This article investigates the sensitivity of a laser power control system to temperature measurement uncertainty. This article evaluates the effectiveness of two methods for quantifying the effect of input uncertainty on a SLS laser power control system: a recent innovation in uncertainty-tracked architecture and traditional Monte Carlo simulation. We show that recent advances in computer architecture for arithmatic on probability distributions make it possible for the first time, to perform control system uncertainty analysis with latencies under 30 ms, while achieving the same level of uncertainty analysis as Monte Carlo methods with latencies that are two orders of magnitude slower.