LAYER-WISE IN-PROCESS MONITORING-AND-FEEDBACK SYSTEM BASED ON SURFACE CHARACTERISTICS EVALUATED BY MACHINE-LEARNING-GENERATED CRITERIA

Aug 6, 2026

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Laser powder bed fusion (PBF-LB) can suffer internal lack-of-fusion (LOF) defects even when nominally optimal parameters are selected, because complex geometries, build-chamber placement, and unforeseen conditions can render those parameters ineffective. A new in-situ monitoring and feedback system was developed to suppress LOF by measuring surface properties after each laser irradiation, predicting LOF occurrence, and re-melting the same surface when needed. Evaluation thresholds combine aerial surface texture parameters learned by machine-learning from surface properties and defect outcomes. Results show improved relative density with feedback for an Inconel 718 square-pillar case.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Abstract

Introduction

Problem in PBF-LB and LOF defects

Additive manufacturing project background

In-situ monitoring and feedback concept

Online control and monitoring data acquisition

Scan-wise data acquisition with co-axial setup

Limits of direct feedback and timing considerations

LAYER-WISE IN-PROCESS MONITORING-AND-FEEDBACK SYSTEM BASED ON SURFACE CHARACTERISTICS EVALUATED BY MACHINE-LEARNING-GENERATED CRITERIA T.-T. Ikeshoji*, M. Yonehara†, K. Aoyagi‡, K. Yamanaka‡, A. Chiba‡, H. Kyogoku*, and M. Hashitani† *Research Institute of Fundamental Technology for Next Generation, Kindai University; KU.RING, Higashi Hiroshima, Hiroshima, 739-2116, Japan †Technology Research Association for Future Additive Manufacturing; TRAFAM, Chioyoda-Ku, Tokyo, 101-0044 Japan ‡Institute for Materials Research, Tohoku University, Sendai, Miyagi, 980-8577, Japan Abstract In the laser powder bed fusion (PBF-LB) process, a set of parameters that are considered optimal are selected. Still, a set of parameters cannot accommodate complex model geometries, model placement in the build chamber, and unforeseen circumstances, leading to internal defects. Therefore, a new in-situ monitoring and feedback system has been developed to suppress the occurrence of lack-of-fusion (LOF) defects in the PBFLB process. This system measures surface properties after each laser irradiation to predict whether LOF defects occur. Then, if necessary, a feedback process is performed to re-melt the same surface. Evaluation thresholds are defined by a combination of aerial surface texture parameters created in advance by machine learning of surface properties and defect occurrence. For example, a square pillar of Inconel 718 alloy built with feedback had a higher relative density than one without feedback. Introduction The laser powder bed fusion (PBF-LB) process is a manufacturing method that can create parts with complex shapes by irradiating laser beams to melt and solidify metal powder layers. However, in the PBF-LB process, even if a set of parameters (such as laser power, scanning speed, hatching pitch, and layer thickness) that are considered optimal are selected, the parameters may become inappropriate due to complex model geometries, model placement in the build chamber, and unforeseen circumstances. Moreover, spattering is challenging to suppress only by selecting process parameters. As a result, lack-of-fusion (LOF) defects may occur inside the built parts, reducing their strength and reliability. The Technology Research Association for Future Additive Manufacturing (TRAFAM) was established in 2014 to spearhead a national project in Japan. The initiative was developed in two phases, each contributing significantly to the evolution of additive manufacturing. The first phase, from FY2014 to FY2018, was dedicated to developing next-generation industrial 3D printers. The second phase (FY 2019-FY 2023), which followed, was titled "Fundamental Technology Development Project for Improving Production Efficiency through Additive Manufacturing," two academic institutions were commissioned to conduct research and development. Kindai University played a central role in developing laser beam powder bed fusion (PBF-LB) technology, while Tohoku University and JEOL played a central role in the practical application of electron beam powder bed fusion (PBF-EB) technology. These institutions did not merely supervise the project but played an integral role in its execution. The main objectives ofthe second stage were to gain a comprehensive understanding of complex melting and solidification phenomena to enable accurate defect prediction and to devise preventive measures against such defects. An in-process monitoring, and feedback system has been conceptualized and integrated into the PBF process. This strategic integration aims to achieve two outcomes: reproducible and stable production. 1201 Therefore, we developed a new in-situ monitoring and feedback system in this study to suppress LOF defect occurrence in the PBF-LB process. This system measures surface properties after each laser irradiation to predict whether LOF defects occur. Then, if necessary, a feedback process is performed to re-melt the same surface. Evaluation thresholds

FAQs

Why do LOF defects still occur in PBF-LB even with chosen optimal parameters?

Parameters can become inappropriate due to complex model geometries, model placement, and unforeseen conditions in the build chamber. Spattering is also difficult to suppress by tuning parameters alone, allowing LOF defects to form inside parts.

How does the proposed in-situ monitoring and feedback system predict LOF defects?

After each laser irradiation, it measures surface properties and uses predefined evaluation thresholds derived from machine-learning-based combinations of aerial surface texture parameters. These criteria estimate whether LOF defects will occur.

What feedback action is performed when LOF defects are predicted?

If necessary, a feedback process re-melts the same surface by performing the corrective re-melting step, aiming to suppress the onset of LOF defects.

Aug 6, 2026

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