Inline Drift Detection Using Monitoring Systems and Machine Learning in Selective Laser Melting

In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography

(2020) Gunther Mohr et al.   Metals

Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning

(2019) Ikenna A. Okaro et al.   Additive Manufacturing

Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

(2019) Haneen Arafat Abu Alfeilat et al.   Big Data

Defect detection in selective laser melting technology by acoustic signals with deep belief networks

(2018) Dongsen Ye et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Porosity prediction: Supervised-learning of thermal history for direct laser deposition

(2018) Mojtaba Khanzadeh et al.   JOURNAL OF MANUFACTURING SYSTEMS

In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume

(2018) M. Grasso et al.   ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

In Situ 3D Monitoring of Geometric Signatures in the Powder-Bed-Fusion Additive Manufacturing Process via Vision Sensing Methods

(2018) Zhongwei Li et al.   SENSORS

A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification

(2018) Mohammad Peikari et al.   Scientific Reports

Process defects andin situmonitoring methods in metal powder bed fusion: a review

(2017) Marco Grasso et al.   MEASUREMENT SCIENCE and TECHNOLOGY

In situ quality control of the selective laser melting process using a high-speed, real-time melt pool monitoring system

(2014) S. Clijsters et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing

(2014) Gustavo Tapia et al.   JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME