Modeling of EHD inkjet printing performance using soft computing-based approaches

Ball, Amit Kumar; Das, Raju; Roy, Shibendu Shekhar; Kisku, Dakshina Ranjan; Murmu, Naresh Chandra

doi:10.1007/s00500-019-04202-0

Cited by 31 publications

(11 citation statements)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Te repetitive patterns that are present in the data are identifed and extracted during the fnal stage of the model that has been proposed. Tis is done in order to fnally provide tree regression models for each of the subgroups [8,44].…”

Section: Hybrid Model Wavelet-m5mentioning

confidence: 99%

“…Te study of sea wave's ofshore and onshore structures develops basic knowledge in the feld of coastal engineering and the physics of the sea and waves. In coastal areas, determining the pattern of waves and coastal currents is the most important; features are proposed to identify the factors afecting the marine environment, coastal areas, and coastal structures [8]. Te beach's geometry, shape, sedimentation, erosion, and many other physical and dynamic phenomena are directly afected by waves and currents.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

Chen

Rashid

Sayed

et al. 2023

Advances in Civil Engineering

View full text Add to dashboard Cite

In coastal and port engineering, wind-generated waves have always been a crucial, fundamental, and important topic. As a result, various methods for estimating wave parameters, including field measurement and numerical methods, have been proposed over time. This study evaluates the wave height at Sri-Lanka Hambantota Port using soft computing models such as Artificial Neural Networks (ANNs) and the M5 model tree (M5MT). In order to overcome its nonstationarity, the primary wave height time series were divided into subtime series using the wavelet transform. The collected subtime series were then utilized as input data for ANN and M5MT in order to determine the wave height. For the sake of the model performance, the daily wind and wave data from the Acoustic Wave and Current (AWAC) sensor for Hambantota Port in 2020 and Sanmen Bay in 2017 were used in this study. The training state utilizes 80% of the available data, while the test state uses 20%. The Root Mean Square Error (RMSE) of the ANN, M5, WANN, and Wavelet-M5 models in the Hambantota Port for the test stage are 0.12, 0.11, 0.04, and 0.06, respectively. While in Sanmen Bay, the RMSE of the ANN, M5, WANN, and Wavelet-M5 models for the test stage are 0.14, 0.16, 0.06, and 0.08, respectively. According to the findings of this study, the accuracy of WANN and Wavelet-M5 hybrid models in evaluating wave height is superior to that of classic ANN and M5MT, and it is recommended that WANN and Wavelet-M5 hybrid models be used to estimate wave height.

show abstract

Section: Hybrid Model Wavelet-m5mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

Chen

Rashid

Sayed

et al. 2023

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…The developed model examined the effects of polymer concentration and excitation voltage dwell/rise times on droplet velocity and volume. Ball et al [ 11 ] modeled the performance of EHD inkjet printing based on different combinations of three input parameters (standoff height, applied voltage, and ink flow rate) using artificial neural networks (ANNs). Recently, Brishty et al [ 12 ] developed regression and classification models, which were informed by EHD operating parameters and ink properties, to predict drop velocity as well as radius and classify the resulting ejection into a single drop, multiple drops, and no ejection.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning‐Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Singh,

Rai,

Subramanian

2023

Adv Eng Mater

View full text Add to dashboard Cite

Electrohydrodynamic (EHD) processes are promising techniques for manufacturing nanoscopic products with different shapes (such as thin films, nanofibers, 2D/3D nanostructures, and nanoparticles) and materials at a low cost using simple equipment. A key challenge in their adoption by nonexperts is the requirement of enormous time and resources in identifying the optimum design/process parameters for the underlying material and EHD system. Machine learning (ML) has made exciting advancements in predictive modeling of different processes, provided it is trained on high‐quality datasets at appropriate volumes. This article extends the suitability of such ML‐enabled approaches to a new technological domain of EHD spraying and drop‐on‐demand printing. Different ML models like ridge regression, random forest regression, support vector regression, gradient boosting regression, and multilayer perceptron are trained and their performance using evaluation metrics like RMSE and R2_score is examined. Tree‐based algorithms like gradient boosting regression are found to be the most suitable technique for modeling EHD processes. The trained ML models show substantially higher accuracy (average error < 5%) in replicating these nonlinear processes as compared to previously reported scaling laws (average error ≈ 42%) and are well suited for predictive modeling/analysis of the underlying EHD system and process.

show abstract

“…A typical stacked bioprinting procedure involves designing a 3D computer model of the structure, slicing the model into a series of horizontally two-dimensional (2D) cross-sections, and depositng layers of biomaterials as droplets or continuous cylindrical strips under some environmental stimulus, such as sound, light, electricity, force, or heat to fabricate the model [19,20], as shown in figure 1(a). Layer-bylayer stacked bioprinting technologies are generally divided into four categories, based on the deposition and stimulus principles utilized: inkjet bioprinting [21,22], extrusion bioprinting [23,24], laser-assisted bioprinting [25,26] abioprinting [27,28].…”

Section: Introductionmentioning

confidence: 99%

Stereotactic technology for 3D bioprinting: from the perspective of robot mechanism

Tan¹,

Kuang

Li³

et al. 2021

Biofabrication

View full text Add to dashboard Cite

Three-dimensional (3D) bioprinting has been widely applied in the field of biomedical engineering because of its rapidly individualized fabrication and precisely geometric designability. The emerging demand for bioprinted tissues/organs with bio-inspired anisotropic property is stimulating new bioprinting strategies. Stereotactic bioprinting is regarded as a preferable strategy for this purpose, which can perform bioprinting at the target position from any desired orientation in 3D space. In this work, based on the motion characteristics analysis of the stacked bioprinting technologies, mechanism configurations and path planning methods for robotic stereotactic bioprinting were investigated and a prototype system based on the double parallelogram mechanism was introduced in detail. Moreover, the influence of the time dimension on stereotactic bioprinting was discussed. Finally, technical challenges and future trends of stereotactic bioprinting within the field of biomedical engineering were summarized.

show abstract

Modeling of EHD inkjet printing performance using soft computing-based approaches

Cited by 31 publications

References 86 publications

Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

Machine Learning‐Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Stereotactic technology for 3D bioprinting: from the perspective of robot mechanism

Contact Info

Product

Resources

About