Development of Data-Driven In-Situ Monitoring and Diagnosis System of Fused Deposition Modeling (FDM) Process Based on Support Vector Machine Algorithm

In this article, a data-driven approach is applied to develop a health monitoring and diagnosis framework for a fused deposition modeling process based on a machine learning algorithm. For the data-driven approach, three accelerometers, an acoustic emission sensor, and three thermocouples are installed, and associated data are collected from those sensors. The collected data are processed to obtain root mean square values, and they are used for constructing health monitoring and diagnosis models for the fused deposition modeling process based on a support vector machine algorithm, which is one of machine learning algorithms. Among various root mean square values, those of acceleration data from the frame were most effective for diagnosing health states of the fused deposition modeling process with the non-linear support vector machine–based model.

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In-situ monitoring of the deformation during Fused Deposition Modeling process using CGS method

Polymer Testing ◽

10.1016/j.polymertesting.2019.03.030 ◽

2019 ◽

Vol 76 ◽

pp. 166-172 ◽

Cited By ~ 6

Author(s):

Jiaqiang Li ◽

Huimin Xie ◽

Kang Ma

Keyword(s):

Fused Deposition Modeling ◽

In Situ Monitoring ◽

Fused Deposition ◽

Modeling Process ◽

Deposition Modeling

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Automated Testing and Characterization of Additive Manufacturing (ATCAM)

Journal of Materials Engineering and Performance ◽

10.1007/s11665-021-06042-2 ◽

2021 ◽

Author(s):

Arash Alex Mazhari ◽

Randall Ticknor ◽

Sean Swei ◽

Stanley Krzesniak ◽

Mircea Teodorescu

Keyword(s):

Additive Manufacturing ◽

Fused Deposition Modeling ◽

Cell System ◽

Automated Testing ◽

Fused Deposition ◽

Deposition Modeling ◽

Machine Calibration

AbstractThe sensitivity of additive manufacturing (AM) to the variability of feedstock quality, machine calibration, and accuracy drives the need for frequent characterization of fabricated objects for a robust material process. The constant testing is fiscally and logistically intensive, often requiring coupons that are manufactured and tested in independent facilities. As a step toward integrating testing and characterization into the AM process while reducing cost, we propose the automated testing and characterization of AM (ATCAM). ATCAM is configured for fused deposition modeling (FDM) and introduces the concept of dynamic coupons to generate large quantities of basic AM samples. An in situ actuator is printed on the build surface to deploy coupons through impact, which is sensed by a load cell system utilizing machine learning (ML) to correlate AM data. We test ATCAM’s ability to distinguish the quality of three PLA feedstock at differing price points by generating and comparing 3000 dynamic coupons in 10 repetitions of 100 coupon cycles per material. ATCAM correlated the quality of each feedstock and visualized fatigue of in situ actuators over each testing cycle. Three ML algorithms were then compared, with Gradient Boost regression demonstrating a 71% correlation of dynamic coupons to their parent feedstock and provided confidence for the quality of AM data ATCAM generates.

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