3D Printed Integrated Sensors: From Fabrication to Applications—A Review

Hassan, Md Sahid; Zaman, Saqlain; Dantzler, Joshua Z. R.; Leyva, Diana Hazel; Mahmud, Md Shahjahan; Ramirez, Jean Montes; Gomez, Sofia Gabriela; Lin, Yirong

doi:10.3390/nano13243148

Cited by 12 publications

(6 citation statements)

References 137 publications

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“…During direct energy deposition (DED), a stream of metallic powder or wire is fed into a melt pool that is created by a laser beam and melts as it is deposited [88]. This varies from powder bed fusion technologies (PBF) where thermal energy is used to selectively fuse regions of a powder bed.…”

Section: Ded-direct Energy Deposition Processesmentioning

confidence: 99%

Effects of Shot Peening and Electropolishing Treatment on the Properties of Additively and Conventionally Manufactured Ti6Al4V Alloy: A Review

Okuniewski,

Walczak,

Szala

2024

Materials

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This literature review indicates that the basic microstructure of Ti6Al4V is bimodal, consisting of two phases, namely α + β, and it occurs after fabrication using conventional methods such as casting, plastic forming or machining processes. The fabrication of components via an additive manufacturing process significantly changes the microstructure and properties of Ti6Al4V. Due to the rapid heat exchange during heat treatment, the bimodal microstructure transforms into a lamellar microstructure, which consists of two phases: α′ + β. Despite the application of optimum printing parameters, 3D printed products exhibit typical surface defects and discontinuities, and in turn, surface finishing using shot peening is recommended. A literature review signalizes that shot peening and electropolishing processes positively impact the corrosion behavior, the mechanical properties and the condition of the surface layer of conventionally manufactured titanium alloy. On the other hand, there is a lack of studies combining shot peening and electropolishing in one hybrid process for additively manufactured titanium alloys, which could synthesize the benefits of both processes. Therefore, this review paper clarifies the effects of shot peening and electropolishing treatment on the properties of both additively and conventionally manufactured Ti6Al4V alloys and shows the effect process on the microstructure and properties of Ti6Al4V titanium alloy.

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Section: Ded-direct Energy Deposition Processesmentioning

confidence: 99%

Effects of Shot Peening and Electropolishing Treatment on the Properties of Additively and Conventionally Manufactured Ti6Al4V Alloy: A Review

Okuniewski,

Walczak,

Szala

2024

Materials

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“…The 3D printing of electrodes can be categorized as solid material printing and paste material printing [18][19][20][21][22][23]. Electrodes can be printed using conductive filament materials such as carbon black/acrylonitrile butadiene styrene, carbon nanofiber/graphite/polystyrene, graphene/polylactic acid (PLA), and carbon black/PLA.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fabrication of Electrochemical Chips with a Paste-Dispensing 3D Printer

Wong,

Ng,

Lin

et al. 2024

Sensors

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Electrochemical (EC) detection is a powerful tool supporting simple, low-cost, and rapid analysis. Although screen printing is commonly used to mass fabricate disposable EC chips, its mask is relatively expensive. In this research, we demonstrated a method for fabricating three-electrode EC chips using 3D printing of relatively high-viscosity paste. The electrodes consisted of two layers, with carbon paste printed over silver/silver chloride paste, and the printed EC chips were baked at 70∘C for 1 h. Engineering challenges such as bulging of the tubing, clogging of the nozzle, dripping, and local accumulation of paste were solved by material selection for the tube and nozzle, and process optimization in 3D printing. The EC chips demonstrated good reversibility in redox reactions through cyclic voltammetry tests, and reliably detected heavy metal ions Pb(II) and Cd(II) in solutions using differential pulse anodic stripping voltammetry measurements. The results indicate that by optimizing the 3D printing of paste, EC chips can be obtained by maskless and flexible 3D printing techniques in lieu of screen printing.

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“…AM allows for the direct the integration of sensors into the fabrication process, enabling the creation of complex and customized components with embedded sensing capabilities. Sensor-integrated parts fabricated by AM have several applications [8]. In particular, with its AM applications, it can constitute key elements by being applied in the biomedical, electronics, and aviation industries within the framework of Industry 4.0 [9,10].…”

Section: Introductionmentioning

confidence: 99%

Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm

Ulkir,

Bayraklılar,

Kuncan

2024

Applied Sciences

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As additive manufacturing (AM) processes become integrated with artificial intelligence systems, the time and cost of the fabrication process decrease. In this study, the raster angle, an important parameter in the manufacturing process, was examined using fused deposition modeling (FDM), an AM method. The optimal value of this parameter varies depending on the designed product geometry. By changing the raster angle, the distribution of stresses and strains within the printed object can be modified, potentially influencing the mechanical behavior of the object. Thus, the correct estimation of the raster angle is essential for obtaining parts with high mechanical properties. The focus of this study is to reduce the fabrication time and cost of products by intertwining machine learning (ML) systems with mechanical systems. Its novelty is that ML has never been applied for FDM raster angle estimation. The estimation and modeling of the raster angle were performed using five different ML algorithms. These algorithms include a support vector machine (SVM), Gaussian process regression (GPR), an artificial neural network (ANN), decision tree regression (DTR), and random forest regression (RFR). Data for training were generated using various shapes and geometries, then trained in the MATLAB software, and a prediction model between the input parameters and the raster angle was created. The predicted model was evaluated using five performance criteria. The RFR model predicts the raster angle in the FDM test data with R-squared (R2) = 0.92, an explained variance score (EVS) = 0.92, a mean absolute error (MAE) = 0.012, a root mean square error (RMSE) = 0.056, and a mean squared error (MSE) = 0.0032. These values are R2 = 0.93, EVS = 0.93, MAE = 0.010, RMSE = 0.051, and MSE0.0025 for the training data. RFR is significantly superior to the other prediction algorithms. The proposed model predicts the optimum raster angle for any geometry.

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3D Printed Integrated Sensors: From Fabrication to Applications—A Review

Cited by 12 publications

References 137 publications

Effects of Shot Peening and Electropolishing Treatment on the Properties of Additively and Conventionally Manufactured Ti6Al4V Alloy: A Review

Effects of Shot Peening and Electropolishing Treatment on the Properties of Additively and Conventionally Manufactured Ti6Al4V Alloy: A Review

Adaptive Fabrication of Electrochemical Chips with a Paste-Dispensing 3D Printer

Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm

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