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Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algor

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Computers, Materials & Continua 2024年第4期79卷 1727-1755页

作者： Parth Khandelwal Harshit Indranil Manna Metallurgical&Materials Engineering Department Indian Institute of TechnologyKharagpurWest Bengal721302India Computer Science&Engineering Department Indian Institute of TechnologyKharagpurWest Bengal721302India Vice Chancellor Office Birla Institute of Technology(BIT)MesraRanchiJharkhand835215India

Metallic alloys for a given application are usually designed to achieve the desired properties by devising experimentsbased on experience, thermodynamic and kinetic principles, and various modeling and simulation ***, the influence of process parameters and material properties is often non-linear and non-colligative. Inrecent years, machine learning (ML) has emerged as a promising tool to dealwith the complex interrelation betweencomposition, properties, and process parameters to facilitate accelerated discovery and development of new alloysand functionalities. In this study, we adopt an ML-based approach, coupled with genetic algorithm (GA) principles,to design novel copper alloys for achieving seemingly contradictory targets of high strength and high electricalconductivity. Initially, we establish a correlation between the alloy composition (binary to multi-component) andthe target properties, namely, electrical conductivity and mechanical strength. Catboost, an ML model coupledwith GA, was used for this task. The accuracy of the model was above 93.5%. Next, for obtaining the optimizedcompositions the outputs fromthe initial model were refined by combining the concepts of data augmentation andPareto front. Finally, the ultimate objective of predicting the target composition that would deliver the desired rangeof properties was achieved by developing an advancedMLmodel through data segregation and data *** examine the reliability of this model, results were rigorously compared and verified using several independentdata reported in the literature. This comparison substantiates that the results predicted by our model regarding thevariation of conductivity and evolution ofmicrostructure and mechanical properties with composition are in goodagreement with the reports published in the literature.

关键词： Machine learning genetic algorithm solid-solution precipitation strengthening pareto front data augmentation

Technological advancements in millet dehulling and polishing process: An insight into pretreatment methods, machineries and impact on nutritional quality

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Grain & Oil Science and technology 2024年第3期7卷 186-195页

作者： Shagolshem Mukta Singh Jayasree Joshi T P.Srinivasa Rao Agricultural and Food Engineering Department Indian Institute of Technology KharagpurKharagpurWest Bengal 721302India

Millets are widely recognized for their nutritional significance;however, the methods employed for their processing are currently lacking. This article primarily focuses on the advanced technologies and progressions in millet dehulling and polishing. These technologies operate based on the fundamental principles of compression-shearing, abrasion-friction, and centrifugal-impact forces. Processing of millets can be challenging because of the physical characteristics and tight attachment of hull and bran to the endosperm. However, several dehullers have been designed to solve this problem for different kinds of millets. In addition, the nutritional and anti-nutritional characteristics undergo alterations due to both dehulling and polishing processes. These alterations are thoroughly examined and discussed in this article. Specifically, anti-nutrients such as tannins and phytate are predominantly found in the outer pericarp of the grain and experience a reduction after undergoing dehulling and polishing. The nutritional properties are also subjected to a reduction;however, this reduction can be mitigated by subjecting the grains to certain pretreatments before dehulling and polishing. These treatments serve to enhance dehulling efficiency and nutrient digestibility while simultaneously reducing the presence of anti-nutrients. Novel thermal and non-thermal methodologies such as microwave, hydrothermal, high-pressure processing, and ohmic heating can be employed for processing millets, thereby diminishing the loss of nutrients. Additional research can be carried out to investigate their impact on the dehulling and polishing of millets.

关键词： Millets Dehulling Polishing Pretreatment Novel thermal and non-thermal techniques

Recent advances in chemically defined and tunable hydrogel platforms for organoid culture

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Bio-Design and Manufacturing 2021年第3期4卷 641-674页

作者： Tarun Agarwal Nehar Celikkin Marco Costantini Tapas K.Maiti Pooyan Makvandi Department of Biotechnology Indian Institute of Technology KharagpurKharagpurWest Bengal 721302India institute of Physical Chemistry-Polish Academy of Sciences WarsawPoland Centre for Micro-BioRobotics Istituto Italiano di TecnologiaViale Rinaldo Piaggio 3456025 PontederaPisaItaly

Recent developments in organoid culture technologies have made it possible to closely recapitulate intrinsic characteristics of different tissues under in vitro *** organoids act as a translational bridge between the traditional 2D/3D cultures and the in vivo models for studying the tissue development processes,disease modeling,and drug *** and tissue-specific extracellular matrix have been shown to support organoid development,efficiently;however,their chemically undefined nature,non-tunable properties,and associated batch-to-batch variations often limit reproducibility of the assembly *** this regard,chemically defined platforms offer wider opportunities to optimize and recreate tissue-specific *** present review delineates the current research trends in this sphere,focusing on material perspective and the target tissues(e.g.,neural,liver,pancreatic,renal,and intestinal).The review winds up with a discussion on the current limitations and future perspective to provide a basis for future research.

关键词： Organoid 3D culture Biomaterials Chemically defined hydrogels Cell-instructive microenvironment

In vitro and in vivo degradability,biocompatibility and antimicrobial characteristics of Cu added iron-manganese alloy

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Journal of Materials Science & technology 2021年第25期84卷 159-172页

作者： Santanu Mandal Vijay Kishore Madhuparna Bose Samit Kumar Nandi Mangal Ro Department of Metallurgical and Materials Engineering Indian Institute of Technology-KharagpurKharagpur 721302India Department of Veterinary Surgery&Radiology West Bengal University of Animal&Fishery SciencesKolkata 700037India Department of Biotechnology Indian Institute of TechnologyKharagpur 721302India

In recent years,iron(Fe)based degradable metal is explored as an alternative to permanent fracture fixation *** the present work,copper(Cu)is added in Fe-Mn system to enhance the degradation rate and antimicrobial ***-Mn-x Cu(x=0.9,5 and 10 wt.%)alloys are prepared by the meltingcasting-forging *** analysis confirms austenite phase stabilization due to the presence of Mn and *** predicted by Thermo-Calc calculations,Cu rich phase precipitations are noticed along the austenite grain *** behaviours of Cu added Fe-Mn alloys are investigated through static immersion and electrochemical polarization where enhanced degradation is found for higher Cu added *** challenged against *** bacteria,the Fe-Mn-Cu alloy media extract shows a significant bactericidal effect compare to the base *** vitro cytocompatibility studies,as determined using MG63 and MC3T3-E1 cell lines,indicate increased cell density as a function of time for all the *** implanted in rabbit femur,the newly developed alloy does not show any kind of tissue necrosis around the *** osteogenesis and higher new bone formation are observed with Fe-Mn-10 Cu alloy as evident from micro-computed tomography(μ-CT)and fluorochrome labelling.

关键词： Iron-manganese alloy Degradable metal In vitro Micro-CT

Pre-treatment of rocks prior to comminution--A critical review of present practices

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International Journal of Mining Science and technology 2017年第2期27卷 339-348页

作者： Somani Aditya Nandi Tapas K. Pal Samir K. Majumder Arun K. Department of Mining Engineering Indian Institute of TechnologyKharagpur 721302India Cryogenic Engineering Centre Indian Institute of TechnologyKharagpur 721302India

It has been an established fact that comminution processes, crushing and grinding, are most energy intensive processes which account for more than half of the total energy consumed in mineral *** alternative pre-treatment methods have been tried by experts around the globe. Although these methods yielded positive results in terms of reduction in energy consumption in crushing and particularly, in grinding operations at laboratory scale, their industrial application still remains an unresolved issue and challenge. Present review paper describes each one of these methods along with outcome of earlier studies and issues that need to be addressed through further rigorous experimental *** also suggests the direction in which future studies can be carried out to meet the primary objective of making comminution processes more energy efficient than today they are.

关键词： Comminution Rock breakage Microwave Thermal shock

Development of a mass model in estimating weight-wise particle size distribution using digital image processing

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International Journal of Mining Science and technology 2017年第3期27卷 435-443页

作者： Maiti Abhik Chakravarty Debashish Biswas Kousik Halder Arpan Department of Mining Engineering Indian Institute of Technology Kharag-pur 721302 India

Particle size distribution of coarse aggregates through mechanical sieving gives results in terms of cumu- lative mass percent. But digital image processing generated size distribution of particles, while being fast and accurate, is often expressed in terms of area function or number of particles. In this paper, a mass model is developed which converts the image obtained size distribution to mass-wise distribution, mak- ing it readily comparable to mechanical sieving data. The concept of weight/particle ratio is introduced for mass reconstruction from 2D images of particle aggregates. Using this mass model, the effects of several particle shape parameters （such as major axis, minor axis, and equivalent diameter） on sieve-size of the particles is studied. It is shown that the sieve-size of a particle strongly depend upon the shape param- eters, 91% of its variation being explained by major axis, minor axis, bounding box length and equivalent diameter. Furthermore, minor axis gives an overall accurate estimate of particle sieve-size, error in mean size （D-50） being just 0.4%. However, sieve-size of smaller particles （〈20 ram） strongly depends upon the length of the smaller arm of the bounding box enclosing them and sieve-sizes of larger particles （〉20 mm） are highly correlated to their equivalent diameters. Multiple linear regression analysis has been used to generate overall mass-wise particle size distribution, considering the influences of all these shape parameters on particle sieve-size. Multiple linear regression generated overall mass-wise particle size distribution shows a strong correlation with sieve generated data. The adjusted R-square value of the regression analysis is found to be 99 percent （w.r,t cumulative frequency）. The method proposed in this paper provides a time-efficient way of producing accurate （up to 99%） mass-wise PSD using digital image processing and it can be used effectively to renlace the mechanical sieving.

关键词： Particle size distribution Image analysis Particle shape parameters Weight/particle ratio Sieve analysis

Removal of CO2 in a multistage fluidized bed reactor by amine impregnated activated carbon: optimization using response surface methodology

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International Journal of Coal Science & technology 2019年第3期6卷 445-458页

作者： Dipa Das Sushanta K. Behera B. C. Meikap Department of Chemical Engineering Indian Institute of Technology (IIT) Kharagpur KharagpurWest Bengal 721302 India Department of Chemical Engineering School of Chemical Engineering Howard College Campus University of Kwazulu-Natal (UKZN) Durban 4041 South Africa Department of Chemical Engineering Indira Gandhi Institute of Technology (IGIT) Sarang Sarang Odisha 759146 India

Carbon dioxide (CO2) is the major component of greenhouse gas. Increase in concentration of CO2 in the atmosphere leads to global warming. To remove the CO2 from waste flue gas a four-stage counter-current multistage fluidized bed adsorber was developed and operated in continuous bubbling fluidization regime for the two丒phase system. This paper describes the optimum condition for CO2 removal efficiency in a multistage fluidized bed reactor using amine impregnated activated carbon. Response surface methodology with central composite design was used to determine the effect of three variables on the response. The variables are inlet concentration of CO2 in ppm (ranging from 3000 to 20,000), impregnation ratio of monoethanol amine (ranging from 0.2 to 0.6) and weir height in mm (20-60). The response was CO2 removal efficiency. The factor which was most influential has been identified from the analysis of variance. The optimum CO2 removal efficiency for the amine impregnated activated carbon (MEA-AC) was found to be 95.17%, at initial concentration of CO2 7312.85 ppm, chemical impregnation ratio of 0.31, and weir height 48.65 mm. From the experiment, the CO2 removal efficiency was found to be 95.97% at the same operating conditions. The predicted response was found to relevance with experimental data.

关键词： Activated carbon Response surface methodology Coconut shell Impregnation ratio Multistage fluidized bed Optimization

Image processing algorithms for in-field cotton boll detection in natural lighting conditions

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Artificial Intelligence in Agriculture 2021年第1期5卷 142-156页

作者： Naseeb Singh V.K.Tewari P.K.Biswas C.M.Pareek L.K.Dhruwa Department of Agricultural and Food Engineering IIT KharagpurKharagpur 721302India Department of Electronics and Electrical Communication Engineering IIT KharagpurKharagpur 721302India

In developing countries,the cotton harvesting operation is currently being performed *** to the monotonous nature of this task and the involvement of a considerable amount of labor,this operation becomes very tedious and *** harvesting robots can be a good alternative for the selective picking of cotton bolls from the *** this study,an attempt has been made to develop the image processing algorithms for in-field cotton boll detection in natural lighting conditions for the cotton harvesting *** image processing algorithms namely color difference,band ratio,YCbCr method,and chromatic aberration were proposed for the real-time segmentation of cotton bolls under natural outdoor light *** performance of developed image processing algorithms was evaluated and the experimental results revealed that the chromatic aberration method outperforms as compared to other developed *** chromatic aberration method showed the highest identification rate of 91.05%with false positive and false negative rates of 6.99%and 4.88%respectively,among all the proposed *** highest sensitivity and specificitywere found to be 81.31%and 97.53%,respectively,using the chromatic aberration ***,the chromatic aberration approach demonstrated a very promising performance for in-field cotton bolls detection under natural lighting conditions which confirms its applicability for the robotic cotton harvesters.

关键词： Cotton recognition Image segmentation Color models Color thresholding

Lightweight convolutional neural network models for semanticsegmentation of in-field cotton bolls

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Artificial Intelligence in Agriculture 2023年第2期8卷 1-19页

作者： Naseeb Singh V.K.Tewari P.K.Biswas L.K.Dhruw Department of Agricultural and Food Engineering IIT KharagpurKharagpur 721302India Department of Electronics and Electrical Communication Engineering IIT KharagpurKharagpur 721302India

Robotic harvesting of cotton bolls will incorporate the benefits of manual picking as well as mechanical harvesting. For robotic harvesting, in-field cotton segmentation with minimal errors is desirable which is a challengingtask. In the present study, three lightweight fully convolutional neural network models were developed for thesemantic segmentation of in-field cotton bolls. Model 1 does not include any residual or skip connections,while model 2 consists of residual connections to tackle the vanishing gradient problem and skip connectionsfor feature concatenation. Model 3 along with residual and skip connections, consists of filters of multiplesizes. The effects of filter size and the dropout rate were studied. All proposed models segment the cotton bollssuccessfully with the cotton-IoU (intersection-over-union) value of above 88.0%. The highest cotton-IoU of91.03% was achieved by model 2. The proposed models achieved F1-score and pixel accuracy values greaterthan 95.0% and 98.0%, respectively. The developed models were compared with existing state-of-the-art networks namely VGG19, ResNet18, EfficientNet-B1, and InceptionV3. Despite having a limited number of trainableparameters, the proposed models achieved mean-IoU (mean intersection-over-union) of 93.84%, 94.15%, and94.65% against the mean-IoU values of 95.39%, 96.54%, 96.40%, and 96.37% obtained using state-of-the-art networks. The segmentation time for the developed models was reduced up to 52.0% compared to state-of-theart networks. The developed lightweight models segmented the in-field cotton bolls comparatively faster andwith greater accuracy. Hence, developed models can be deployed to cotton harvesting robots for real-time recognition of in-field cotton bolls for harvesting.

关键词： Cotton Semantic segmentation Convolutional neural network Robotic harvesting Image segmentation Deep learning