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Author

Jiaqi Lyu

Bio: Jiaqi Lyu is an academic researcher from Stevens Institute of Technology. The author has contributed to research in topics: Materials science & Fused filament fabrication. The author has an hindex of 3, co-authored 5 publications receiving 21 citations.

Papers
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Journal ArticleDOI
TL;DR: Two developed PID-based online closed-loop control systems are implemented which can significantly reduce the height deviation errors between the fabricated part measurements and design values, and correct the in-plane surface anomalies.
Abstract: Additive Manufacturing (AM) technologies are experiencing rapid growth in the past decades. Critical objectives for the AM processes are how to ensure product quality and process consistency. The d...

27 citations

Journal ArticleDOI
TL;DR: Through comparing the dimensional deviations of parts fabricated before and after optimization, the effectiveness of the integrated error model is analyzed and demonstrated for the three FDM machines.
Abstract: The dimensional accuracy of fused deposition modeling (FDM) machines is dependent on errors caused by processing parameters and machine motions. In this study, an integrated error model combining these effects is developed. Extruder temperature, layer thickness, and infill density are selected as parameters of this study for three FDM machines, namely, Flashforge Finder, Ultimaker 2 go, and XYZ da Vinci 2.0 Duo. Experiments have been conducted using Taguchi method and the interactions between processing parameters are analyzed. Based on the dimensional deviations between fabricated parts and the computer aided design (CAD) geometry, a set of coefficients for the integrated error model are calculated to characterize each machine. Based on the results of the integrated error model, the original CAD geometry is optimized for fabrication accuracy on each machine. New parts are fabricated using the optimized CAD geometries. Through comparing the dimensional deviations of parts fabricated before and after optimization, the effectiveness of the integrated error model is analyzed and demonstrated for the three FDM machines.

10 citations

Journal ArticleDOI
TL;DR: In this paper, an integrated error model and compensation methods are developed to improve the accuracy of fused deposition modeling (FDM) machines, which can be used to obtain the compensated values for any given part models.
Abstract: The purpose of this paper is to improve the accuracy of fused deposition modeling (FDM) machines.,An integrated error model and compensation methods are developed to improve the accuracy of FDM machines. The effects of machine-dependent and process-dependent errors are included in this integrated model. The error model is then used to obtain compensated values for the printed object. A three-dimensional artifact is designed for the FDM machine characterization. This process takes place only once and an error model for the machine is then developed. An artifact is designed that is feature rich and its coordinates are measured by the coordinate measuring machine (CMM). The CMM digitized values for the three-dimensional artifact are used to calculate the coefficients of the model. The integrated error model of the machine can be used to obtain the compensated values for any given part models. The coefficients of the integrated error model are machine-dependent and represent machine error estimation. To demonstrate this, two test examples are used and modified based on the machine model to verify the effectiveness of the proposed method.,The errors from machine mechanical structure and process are evaluated. The variation trend of each error is analyzed. The uncompensated and compensated models are compared, and the effectiveness of the integrated error model and compensation method is analyzed and validated.,An effective integrated error model with compensation is developed, which can be used to improve the FDM machines accuracy.

7 citations

Journal ArticleDOI
TL;DR: In this paper , a real-time monitoring system based on acoustic emission (AE) and laser scanning technology to monitor the warpage defect in fabricated parts throughout the printing process is presented.

6 citations

Proceedings ArticleDOI
25 Nov 2019
TL;DR: The present study highlights the predictive model for dimensional accuracy in the FDM process, and finds that the ANN model performs better than the multivariate linear regression and SVR models.
Abstract: With the development of Fused Deposition Modeling (FDM) technology, the quality of fabricated parts is getting more attention. The present study highlights the predictive model for dimensional accuracy in the FDM process. Three process parameters, namely extruder temperature, layer thickness, and infill density, are considered in the model. To achieve better prediction accuracy, three models are studied, namely multivariate linear regression, Artificial Neural Network (ANN), and Support Vector Regression (SVR). The models are used to characterize the complex relationship between the input variables and dimensions of fabricated parts. Based on the experimental data set, it is found that the ANN model performs better than the multivariate linear regression and SVR models. The ANN model is able to study more quality characteristics of fabricated parts with more process parameters of FDM.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article , a cause-and-effect diagram exhibits the process parameters' categorization with the surface quality (SQ) and dimensional accuracy (DA) of the manufactured parts.
Abstract: ABSTRACT Fused filament fabrication (FFF) is an additive manufacturing process, which constructs physical items by fused melt material, selectively deposited layer-by-layer through a heated extrusion mechanism. Parameters’ selection and control in FFF are of utmost importance since they significantly affect the surface quality (SQ) and dimensional accuracy (DA) of the FFF printed parts. In the present paper, initially, the FFF process is briefly presented. Next, a cause-and-effect diagram exhibits the process parameters’ categorization with the SQ and DA of the manufactured parts. Then, according to the robust design theory, the process parameters are divided into three classes, i.e., the signal, the control, and the noise. This classification supports the selection of appropriate FFF printers, according to the control parameters, whereas it facilitates the optimization of the SQ and DA of printed parts, concerning the signal parameters. Finally, the impact of each parameter on SQ and DA is presented, supported by an extensive literature review. Overall, the process parameters’ optimization is critical for the SQ and DA. Therefore, they should be adjusted to achieve higher quality and less post-processing work.

57 citations

Journal ArticleDOI
TL;DR: Several approaches are presented, namely response surface methodology, particle swarm optimization, and symbiotic organism search, to find the optimal parameter settings for better surface quality, i.e., surface roughness of the FDM printed part.
Abstract: Additive manufacturing (AM) technologies such as fused deposition modeling (FDM) have been widely used in various fields of today’s manufacturing industries such as transportation, aerospace, and medical because of its ability to produce parts of complex designs with less manufacturing time and cost. However, a proper selection of input process parameters is a vital aspect in order to obtain the best quality of the printed part. This paper presents several approaches, namely response surface methodology, particle swarm optimization, and symbiotic organism search, to find the optimal parameter settings for better surface quality, i.e., surface roughness of the FDM printed part. Layer height, print speed, print temperature, and outer shell speed were considered as the input parameters and surface roughness as the output response. The experimental design was carried out using response surface methodology (RSM) method. Then, the relationship between the input parameters and the surface roughness was established using regression model. Once the accuracy of the model had been validated, the model was then coupled with particle swarm optimization (PSO) and symbiotic organism search (SOS) to optimize the input parameters that would lead to minimum surface roughness. Experimental results showed that the surface roughness obtained using PSO and SOS have improved about 8.5% and 8.8%, respectively, compared with the conventional method, i.e., RSM. A good agreement between the predicted surface roughness and the experimental values was also observed.

56 citations

Journal ArticleDOI
TL;DR: In this paper, a data-driven framework for process parameters optimization using physics-informed computer simulation models is presented to identify a robust manufacturing condition that allows us to constantly obtain equiaxed materials microstructures under uncertainty.
Abstract: The presence of various uncertainty sources in metal-based additive manufacturing (AM) process prevents producing AM products with consistently high quality. Using electron beam melting (EBM) of Ti-6Al-4V as an example, this paper presents a data-driven framework for process parameters optimization using physics-informed computer simulation models. The goal is to identify a robust manufacturing condition that allows us to constantly obtain equiaxed materials microstructures under uncertainty. To overcome the computational challenge in the robust design optimization under uncertainty, a two-level data-driven surrogate model is constructed based on the simulation data of a validated high-fidelity multiphysics AM simulation model. The robust design result, indicating a combination of low preheating temperature, low beam power, and intermediate scanning speed, was acquired enabling the repetitive production of equiaxed structure products as demonstrated by physics-based simulations. Global sensitivity analysis at the optimal design point indicates that among the studied six noise factors, specific heat capacity and grain growth activation energy have the largest impact on the microstructure variation. Through this exemplar process optimization, the current study also demonstrates the promising potential of the presented approach in facilitating other complicate AM process optimizations, such as robust designs in terms of porosity control or direct mechanical property control.

53 citations

Journal ArticleDOI
TL;DR: The use of short carbon fibre as reinforcement did not affect the dimensional accuracy of the PLA-CF samples, and even improved the surface roughness in certain cases, particularly in Flat and On-edge orientations.
Abstract: In this work, the effect of short carbon fibre (CF) on the mechanical and geometric properties of 3D printed polylactic acid (PLA) composite parts processed using the Fused Filament Fabrication (FFF) technique have been analysed. Tensile, flexural and interlaminar shear strength (ILSS) tests were performed to obtain the mechanical performance of the different samples. The surface quality and geometric accuracy of the printed specimens were also evaluated. Finally, Scanning Electron Microscope (SEM) images of the printed samples are analysed. The results revealed that the addition of carbon fibres effectively improved all assessed mechanical properties of PLA-CF composites as compared to the neat PLA. In particular, Flat PLA-CF samples showed an average increase in tensile performance of 47.1% for the tensile strength and 179.9% for the tensile stiffness in comparison to the neat PLA. From the flexural behaviour point of view, Flat PLA-CF samples revealed an increase in average flexural strength and stiffness of 89.75% and 230.95%, respectively in comparison to the neat PLA. Furthermore, PLA-CF samples depicted the best ILSS performance. In general, the use of short carbon fibre as reinforcement did not affect the dimensional accuracy of the PLA-CF samples, and even improved the surface roughness in certain cases, particularly in Flat and On-edge orientations.

35 citations