The role of artificial neural network and machine learning in utilizing spatial information

Goel, Akash; Goel, Amit; Kumar, Adesh

doi:10.1007/s41324-022-00494-x

Cited by 80 publications

(26 citation statements)

References 51 publications

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“…The approach presented here is based on the use of machine learning techniques to get linear or nonlinear internal relationships from empirical data. The introduction of this computational model in the industry could be useful for the reduction of manufacturing costs of materials, not only for the design of new alloys but also for the material selection, the definition of cold forming process conditions, the improvement of the quality of stainless steel and the prevention of serious damage to the environment or public safety, all of this thanks to the capability of the model of the present work to predict the mechanical behaviour of multiple varieties of stainless steel under strain hardening condition, advantages which have been already expressed by multiple authors such as Thike et al [ 37 ], Goel et al [ 38 ], Huang et al [ 39 ] or Mouellefe et al [ 40 ].…”

Section: Introductionmentioning

confidence: 83%

A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels

Contreras-Fortes,

Rodríguez-García,

Sales

et al. 2023

Materials

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Stainless steel is a cold-work-hardened material. The degree and mechanism of hardening depend on the grade and family of the steel. This characteristic has a direct effect on the mechanical behaviour of stainless steel when it is cold-formed. Since cold rolling is one of the most widespread processes for manufacturing flat stainless steel products, the prediction of their strain-hardening mechanical properties is of great importance to materials engineering. This work uses artificial neural networks (ANNs) to forecast the mechanical properties of the stainless steel as a function of the chemical composition and the applied cold thickness reduction. Multiple linear regression (MLR) is also used as a benchmark model. To achieve this, both traditional and new-generation austenitic, ferritic, and duplex stainless steel sheets are cold-rolled at a laboratory scale with different thickness reductions after the industrial intermediate annealing stage. Subsequently, the mechanical properties of the cold-rolled sheets are determined by tensile tests, and the experimental cold-rolling curves are drawn based on those results. A database is created from these curves to generate a model applying machine learning techniques to predict the values of the tensile strength (Rm), yield strength (Rp), hardness (H), and elongation (A) based on the chemical composition and the applied cold thickness reduction. These models can be used as supporting tools for designing and developing new stainless steel grades and/or adjusting cold-forming processes.

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Section: Introductionmentioning

confidence: 83%

A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels

Contreras-Fortes,

Rodríguez-García,

Sales

et al. 2023

Materials

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“…(2) Deep learning algorithms, a subset of machine learning, can be used for tasks such as hazard prediction, risk assessment, disaster detection, and damage assessment [62]. (3) Artificial neural networks, including recurrent neural networks and convolutional neural networks, are effective in processing spatial information and can be used for weather forecasting, healthcare, and flood detection [61]. There exist several ethical considerations and potential biases associated with using AI and machine learning for predicting natural disasters on civil engineering infrastructure.…”

Section: Forecasting the Likelihood And Severity Of Natural Disastersmentioning

confidence: 99%

Machine learning methods in civil engineering: a systematic review

Kamolov

2024

AMCS

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“…[11] Due to the recent surge in computational capabilities, artificial neural networks have become widely prevalent in modern machine learning, marking a significant shift towards their extensive adoption across various domains. [12] Few areas that uses artificial neural network are: Medical diagnosis and health care, facial recognition, behaviour of social media users is analysed using ANN, stock market forecasting, weather forecasting, robotics and dynamics, etc.…”

Section: A Logistic Regression (Lr)mentioning

confidence: 99%

Making a Better Logistic Regression Model Using Ensemble Learning

Verma

2024

IJRASET

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Machine learning is a hot research topic in today's society and an important direction in the field of image classification research. Image classification is a supervised learning method used to classify images. There are many image classification algorithms that perform differently in different conditions. Paper focuses on improving the performance of image classification algorithms by using ensemble learning. Paper analyses the performance of logistic regression model and support vector machine model and then uses ensemble learning to increase the performance of logistic regression model. In the research work, both theoretical and empirical approaches were followed. For the theoretical approach a review of both secondary data as well as data based on results obtained by application on the tools is studied. Secondary data was acquired from the research articles, text books, journals, technical reports, published thesis, websites, e-journals, software tool manuals, conference proceedings and any other research articles published in the related domain. The empirical study was carried out on the set of experiments, using software tools. The results obtained from the experiments were analyzed for the finding of the research. The paper compares the results of three algorithms when tested on same dataset, in same environment and on same system. The final results of the research prove that ensemble techniques give best results and this type of learning is effective.

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The role of artificial neural network and machine learning in utilizing spatial information

Abstract: processing, healthcare, and weather forecasting with greater than 90% accuracy.

Cited by 80 publications

References 51 publications

A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels

A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels

Machine learning methods in civil engineering: a systematic review

Making a Better Logistic Regression Model Using Ensemble Learning

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