Intelligent use of data to optimize compressive strength of cellulose-derived composites

Vafaeenezhad, H.; Asadolahpour, Seyed Reza; Nayebpashaee, Nasim; Seyedein, S.H.; Aboutalebi, M.R.

doi:10.1016/j.asoc.2015.12.029

Cited by 6 publications

(7 citation statements)

References 46 publications

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“…Recently, soft computing techniques are being utilized by researchers in almost any field. This can be attributed to their unique capabilities in handling complex, nonlinear, categorical, and multi-dimensional prediction and classification problems, where analytical solutions are complicated and time consuming, if not impossible [8,29]. Thus, these tools have gained considerable attention in the materials engineering society.…”

Section: Discussionmentioning

confidence: 99%

“…Polymer processing is in general difficult to predict and model, especially because of the interdependencies between processing conditions, polymeric behaviors, and geometries. Process modelling typically involves significant amount of experimentation along with analytical modeling and/or numerical simulation; such procedures are also cyclical with several trial-and-error runs, costly and time-consuming tests, and material losses [29,30]. With the rapid advancement in soft computing, machine learning algorithms (MLA) have been advocated to solve complex modeling and optimization problems in various engineering fields [16,31].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Models for Predicting and Classifying the Tensile Strength of Polymeric Films Fabricated via Different Production Processes

2019

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In this study, machine learning algorithms (MLA) were employed to predict and classify the tensile strength of polymeric films of different compositions as a function of processing conditions. Two film production techniques were investigated, namely compression molding and extrusion-blow molding. Multi-factor experiments were designed with corresponding parameters. A tensile test was conducted on samples and the tensile strength was recorded. Predictive and classification models from nine MLA were developed. Performance analysis demonstrated the superior predictive ability of the support vector machine (SVM) algorithm, in which a coefficient of determination and mean absolute percentage error of 96% and 4%, respectively were obtained for the extrusion-blow molded films. The classification performance of the MLA was also evaluated, with several algorithms exhibiting excellent performance.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Predicting and Classifying the Tensile Strength of Polymeric Films Fabricated via Different Production Processes

2019

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“…Film composition, its component properties, ratios, pre-processing, environmental factors, and a variety of other material-based parameters have also been found to have a significant effect on film properties. , Polymer manufacturing is difficult to forecast and model in general owing to the interdependence of processing environments, polymer structures, and geometries. Process modeling usually entails large amounts of testing as well as computational modeling and/or numerical simulation, and such techniques are often cyclical, with many trial-and-error runs, expensive and time-consuming experiments, and material losses. , …”

Section: Introductionmentioning

confidence: 99%

The Effect of Machine Learning Algorithms on the Prediction of Layer-by-Layer Coating Properties

et al. 2023

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Layer-by-layer film (LbL) coatings made of polyelectrolytes are a powerful tool for surface modification, including the applications in the biomedical field, for food packaging, and in many electrochemical systems. However, despite the number of publications related to LbL assembly, predicting LbL coating properties represents quite a challenge, can take a long time, and be very costly. Machine learning (ML) methodologies that are now emerging can accelerate and improve new coating development and potentially revolutionize the field. Recently, we have demonstrated a preliminary ML-based model for coating thickness prediction. In this paper, we compared several ML algorithms for optimizing a methodology for coating thickness prediction, namely, linear regression, Support Vector Regressor, Random Forest Regressor, and Extra Tree Regressor. The current research has shown that learning algorithms are effective in predicting the coating output value, with the Extra Tree Regressor algorithm demonstrating superior predictive performance, when used in combination with optimized hyperparameters and with missing data imputation. The best predictors of the coating thickness were determined, and they can be later used to accurately predict coating thickness, avoiding measurement of multiple parameters. The development of optimized methodologies will ensure different reliable predictive models for coating property/function relations. As a continuation, the methodology can be adapted and used for predicting the outputs connected to antimicrobial, anti-inflammatory, and antiviral properties in order to be able to respond to actual biomedical problems such as antibiotic resistance, implant rejection, or COVID-19 outbreak.

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“…With the rise of silicon research, the determination method of silicon content has also been updated. At present, the commonly used method is silicomolybdic blue spectrophotometry (Tong et al, 2005; Zhang et al, 2013). However, this method still needs to prepare a variety of solutions, such as nitric acid, ammonium molybdate, oxalic acid, ammonium ferrous sulfate solution, and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…At present, the detection of the removal of silicon structures on the epidermis of straw has the problems of low efficiency, harsh detection conditions, obvious experimental error, and low accuracy, which have brought trouble to the detection of silicon structures, and limit the researchers' judgment on the removal efficiency of silicon structures on wheat straw epidermis after pretreatment (Vafaeenezhad et al, 2016). Therefore, how to detect the silicon structures accurately and conveniently on wheat straw epidermis has attracted increasing attention.…”

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confidence: 99%

Using an improved YOLOv5s network for the automatic detection of silicon on wheat straw epidermis of micrographs

Zhang

Wang

Song

et al. 2022

Journal of Field Robotics

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The silicon on wheat straw epidermis is an obstacle to its resource utilization, and pretreated methods should be applied to remove this structure. Due to the difficulties in detecting the silicon on wheat straw epidermis, judging the efficiency of pretreatment is still a challenging task. In this study, an automatic detection method based on you only look once (YOLO) v5s was proposed to detect the silicon on wheat straw epidermis of micrographs. To improve the efficiency of the network, the Input was modified, the inverted residual module, the pointwise convolution, and the attention mechanism were added, while the focus module was cut off. A total of 4690 micrographs of wheat straw epidermis were collected for training and testing. The training results showed that the proposed model can efficiently detect silicon on wheat straw epidermis of micrographs, and had the highest mean Average Precision of 98.88% among five state‐of‐the‐art comparison models, including RetinaNet, Single Shot MultiBox Detector, YOLOv4tiny, YOLOv4, and YOLOv5s. The weight of the proposed model was 11.7 M, indicating that it can be transplanted to mobile devices. The proposed model showed good robustness under different imaging conditions. All the results indicated that the proposed model could detect the silicon on wheat straw epidermis of micrographs accurately and efficiently.

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Intelligent use of data to optimize compressive strength of cellulose-derived composites

Cited by 6 publications

References 46 publications

Machine Learning Models for Predicting and Classifying the Tensile Strength of Polymeric Films Fabricated via Different Production Processes

Machine Learning Models for Predicting and Classifying the Tensile Strength of Polymeric Films Fabricated via Different Production Processes

The Effect of Machine Learning Algorithms on the Prediction of Layer-by-Layer Coating Properties

Using an improved YOLOv5s network for the automatic detection of silicon on wheat straw epidermis of micrographs

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