Hybrid additive manufacturing of highly sustainable Polylactic acid -Carbon Fiber-Polylactic acid sandwiched composite structures: Optimization and machine learning

Thakur, Vishal; Kumar, Ranvijay; Singh, Rupinder; Kumar, Vinay

doi:10.1177/08927057231180186

Cited by 18 publications

(2 citation statements)

References 44 publications

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“…In the evolving landscape of additive manufacturing, the work by Thakur et al [106] epitomizes a breakthrough in utilizing ML for material optimization, specifically in the context of carbon fiber (CF) reinforced PLA composites. This study harnesses FFF to create PLA-CF-PLA sandwiched structures, focusing on optimizing various manufacturing parameters like fiber deposition angles and temperatures.…”

Section: Machine Learning For Materials Selection or Optimizationmentioning

confidence: 99%

Advancing 3D Printing through Integration of Machine Learning with Algae‐Based Biopolymers

Bin Abu Sofian,

Lim,

Chew

et al. 2024

ChemBioEng Reviews

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The integration of machine learning (ML) with algae‐derived biopolymers in 3D printing is a burgeoning area with the potential to revolutionize various industries. This review article delves into the challenges and advancements in this field, starting with the critical problem it addresses the need for sustainable and efficient additive manufacturing processes. Algae‐based biopolymers, such as alginate and carrageenan, are explored for their viability in 3D printing, highlighting their environmental benefits and technical challenges. The role of ML in enhancing material selection, predictive modeling, and quality control is examined, showcasing how this synergy leads to significant improvements in 3D printing processes. Key findings include the enhanced mechanical properties of algae‐based biopolymers and the optimization of printing parameters through ML algorithms. Examples like the use of Spirulina in creating a range of materials and the application of carrageenan in bone tissue engineering are discussed. The conclusion underscores the transformative impact of combining ML with algae‐based biopolymers in 3D printing, paving the way for innovative, sustainable solutions in additive manufacturing. Despite existing challenges, this integration holds promise for a future of advanced, eco‐friendly manufacturing techniques.

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Section: Machine Learning For Materials Selection or Optimizationmentioning

confidence: 99%

Advancing 3D Printing through Integration of Machine Learning with Algae‐Based Biopolymers

Bin Abu Sofian,

Lim,

Chew

et al. 2024

ChemBioEng Reviews

View full text Add to dashboard Cite

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“…CART, a non-parametric decision tree induction method, recursively selects rules based on variable values to identify the best split. The splitting process stops when further gain is unattainable or specific predetermined criteria are met [30].…”

Section: (B) Decision Treementioning

confidence: 99%

Player Performance Predictive Analysis in Cricket Using Machine Learning

Bharadwaj,

Saxena,

Kumar

et al. 2024

RIA

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Player performance is the most critical parameter for a match's outcome. The selection of a certain set of players according to various parameters, including consistency, Form, performance against the particular opponent, performance in the specific venue, the tournament in which the match is being played, the pressure of the type of match, etc., elevates the probability of a team winning the game. The following research aims to analyze and predict the player's performance based on the player's performance parameters. The problem is segmented into two parts, i.e., batting performance and bowling performance. The problem is presumed to be a classification problem. Runs scored, and wickets taken are classified in distinct ranges. Naïve Bayes', Decision Tree, Random Forest, and Support Vector Machine (SVM) are the algorithms used in the research. Random Forest and Decision Tree were almost identical and, hence, the most accurate for the result.

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Material extrusion‐based additive manufacturing of sandwiched acrylonitrile butadiene styrene/glass fibers composites: Machine learning approach to model tensile stress

Kumar,

Kumar

2024

Polymer Composites

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Additive manufacturing (AM), also recognized as 3D printing, has gained significant attention in various industries for its potential to revolutionize production processes. One critical aspect of AM is ensuring the quality and performance of printed parts, particularly concerning mechanical properties like tensile stress. In the present work, the effect of process variables on 3D‐printed acrylonitrile butadiene styrene (ABS)/Glass fiber composite materials was explored. The machine learning approach, classification and regression trees (CART) algorithm, was used to predict tensile stress in ABS/Glass fiber composite materials based on predictor variables such as layer thickness, nozzle temperature, bed temperature, and infill density. The objective is to develop an accurate and interpretable model that captures the relationships between these variables and tensile stress. The model is evaluated using performance metrics such as R‐squared, mean absolute deviation (MAD), and root mean squared error (RMSE) on both training and test datasets. From results the highest tensile stress of 39 MPa was achieved at nozzle temperature of 250°C, bed temperature of 80°C and infill density at 60%. The CART model predicts the most influencing parameter as infill density followed by nozzle temperature and bed temperature.Highlights Novel material is manufactured by sandwiching glass fiber in ABS layers. FDM process for novel material is optimized based upon process parameters. SEM analysis reported good interlayer adhesion with few micro‐porosities. Modeling of tensile stress by classification and regression tree algorithm. CART model predicts significant impact of infill density on tensile stress.

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Hybrid additive manufacturing of highly sustainable Polylactic acid -Carbon Fiber-Polylactic acid sandwiched composite structures: Optimization and machine learning

Cited by 18 publications

References 44 publications

Advancing 3D Printing through Integration of Machine Learning with Algae‐Based Biopolymers

Advancing 3D Printing through Integration of Machine Learning with Algae‐Based Biopolymers

Player Performance Predictive Analysis in Cricket Using Machine Learning

Material extrusion‐based additive manufacturing of sandwiched acrylonitrile butadiene styrene/glass fibers composites: Machine learning approach to model tensile stress

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