Multi-Modal SeNSor Fusion with Machine Learning for Data-Driven Process Monitoring for Additive Manufacturing

2021 ◽  
pp. 102364
Author(s):  
Jan Petrich ◽  
Zack Snow ◽  
David Corbin ◽  
Edward W. Reutzel
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Sensor Fusion ◽  
Process Monitoring ◽  
Data Driven

Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm

2022 ◽  
Vol 62 ◽  
pp. 145-163
Author(s):  
Shenghan Guo ◽  
Mohit Agarwal ◽  
Clayton Cooper ◽  
Qi Tian ◽  
Robert X. Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Data Driven ◽  
Metal Additive Manufacturing ◽  
Metal Additive

In-Situ Laser-Based Process Monitoring and In-Plane Surface Anomaly Identification for Additive Manufacturing Using Point Cloud and Machine Learning

2021 ◽  
Author(s):  
Jiaqi Lyu ◽  
Javid Akhavan Taheri Boroujeni ◽  
Souran Manoochehri
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Data Processing ◽  
Process Monitoring ◽  
Point Cloud ◽  
Plane Surface ◽  
Laser Scanner ◽  
Classification Model ◽  
Depth Image

Abstract Additive Manufacturing (AM) is a trending technology with great potential in manufacturing. In-situ process monitoring is a critical part of quality assurance for AM process. Anomalies need to be identified early to avoid further deterioration of the part quality. This paper presents an in-situ laser-based process monitoring and anomaly identification system to assure fabrication quality of Fused Filament Fabrication (FFF) machine. The proposed data processing and communication architecture of the monitoring system establishes the data transformation between workstation, FFF machine, and laser scanner control system. The data processing performs calibration, filtering, and segmentation for the point cloud of each layer acquired from a 3D laser scanner during the fabrication process. The point cloud dataset with in-plane surface depth information is converted into a 2D depth image. Each depth image is discretized into 100 equal regions of interest and then labeled accordingly. Using the image dataset, four Machine Learning (ML) classification models are trained and compared, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN), and Hybrid Convolution AutoEncoder (HCAE). The HCAE classification model shows the best performance based on F-scores to effectively classify the in-plane anomalies into four categories, namely empty region, normal region, bulge region, and dent region.

Feature extraction and process monitoring of multi-channel data in a forging process via sensor fusion

2021 ◽  
Vol 34 (1) ◽  
pp. 95-109
Author(s):  
Feng Ye ◽  
Yiming Guo ◽  
Zhijie Xia ◽  
Zhisheng Zhang ◽  
Yifan Zhou
Keyword(s):  
Feature Extraction ◽  
Sensor Fusion ◽  
Process Monitoring ◽  
Forging Process

scholarly journals Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

2021 ◽  
Author(s):  
Ekaterina Kochmar ◽  
Dung Do Vu ◽  
Robert Belfer ◽  
Varun Gupta ◽  
Iulian Vlad Serban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Performance Outcomes ◽  
Data Driven ◽  
Personalized Feedback ◽  
Tutoring Systems

AbstractIntelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

scholarly journals A Data-Driven Method for the Temporal Estimation of Soil Water Potential and Its Application for Shallow Landslides Prediction

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1208
Author(s):  
Massimiliano Bordoni ◽  
Fabrizio Inzaghi ◽  
Valerio Vivaldi ◽  
Roberto Valentino ◽  
Marco Bittelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Potential ◽  
Soil Water ◽  
Temporal Trends ◽  
Soil Water Potential ◽  
Shallow Landslides ◽  
Water Dynamics ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Physically Based

Soil water potential is a key factor to study water dynamics in soil and for estimating the occurrence of natural hazards, as landslides. This parameter can be measured in field or estimated through physically-based models, limited by the availability of effective input soil properties and preliminary calibrations. Data-driven models, based on machine learning techniques, could overcome these gaps. The aim of this paper is then to develop an innovative machine learning methodology to assess soil water potential trends and to implement them in models to predict shallow landslides. Monitoring data since 2012 from test-sites slopes in Oltrepò Pavese (northern Italy) were used to build the models. Within the tested techniques, Random Forest models allowed an outstanding reconstruction of measured soil water potential temporal trends. Each model is sensitive to meteorological and hydrological characteristics according to soil depths and features. Reliability of the proposed models was confirmed by correct estimation of days when shallow landslides were triggered in the study areas in December 2020, after implementing the modeled trends on a slope stability model, and by the correct choice of physically-based rainfall thresholds. These results confirm the potential application of the developed methodology to estimate hydrological scenarios that could be used for decision-making purposes.

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