scholarly journals Development of Data-Driven Machine Learning Models for the Prediction of Casting Surface Defects

Metals ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Shikun Chen ◽  
Tim Kaufmann
Keyword(s):  
Machine Learning ◽  
Cast Iron ◽  
Surface Defects ◽  
Casting Process ◽  
Machine Learning Algorithms ◽  
Casting Surface ◽  
Iron And Steel ◽  
Related Data ◽  
Iron Foundry ◽  
Related Defect

This paper presents an approach for the application of machine learning in the prediction and understanding of casting surface related defects. The manner by which production data from a steel and cast iron foundry can be used to create models for predicting casting surface related defect is demonstrated. The data used for the model creation were collected from a medium-sized steel and cast iron foundry in which components ranging from 1 to 100 kg in weight are produced from wear and heat resistant cast iron and steel materials. This includes all production-relevant data from the melting and casting process, as well as from the mold production, the sand preparation and component quality related data from the quality management department. The data are tethered together with each other, the information regarding the identity and number of components that resulted in scrap due to the casting surface defect metal penetrations was added to the dataset. Six different machine learning algorithms were trained and an interpretation of the models outputs was accomplished with the application of the SHAP framework.

scholarly journals Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data

Metals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 557 ◽  
Author(s):  
Cristiano Fragassa ◽  
Matej Babic ◽  
Carlos Perez Bergmann ◽  
Giangiacomo Minak
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Mechanical Properties ◽  
Pattern Recognition ◽  
Cast Iron ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Tensile Behaviour ◽  
Cast Alloys ◽  
Pattern Recognition Analysis

The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to offer acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 different machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results offer high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed.

scholarly journals AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study

2021 ◽  
Author(s):  
Naser Zaeri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Ct Scan ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Related Data ◽  
Recent Developments ◽  
Intelligence Modeling ◽  
Health Organization ◽  
Imaging Algorithms

The coronavirus disease 2019 (COVID-19) outbreak has been designated as a worldwide pandemic by World Health Organization (WHO) and raised an international call for global health emergency. In this regard, recent advancements of technologies in the field of artificial intelligence and machine learning provide opportunities for researchers and scientists to step in this battlefield and convert the related data into a meaningful knowledge through computational-based models, for the task of containment the virus, diagnosis and providing treatment. In this study, we will provide recent developments and practical implementations of artificial intelligence modeling and machine learning algorithms proposed by researchers and practitioners during the pandemic period which suggest serious potential in compliant solutions for investigating diagnosis and decision making using computerized tomography (CT) scan imaging. We will review the modern algorithms in CT scan imaging modeling that may be used for detection, quantification, and tracking of Coronavirus and study how they can differentiate Coronavirus patients from those who do not have the disease.

Wearable Devices Data for Activity Prediction Using Machine Learning Algorithms

2019 ◽  
Vol 4 (1) ◽  
pp. 32-46
Author(s):  
Lakshmi Prayaga ◽  
Krishna Devulapalli ◽  
Chandra Prayaga
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Wearable Devices ◽  
Machine Learning Algorithms ◽  
Embedded Sensors ◽  
Data Sets ◽  
Activity Prediction ◽  
Related Data ◽  
Recent Trends

Wearable devices are contributing heavily towards the proliferation of data and creating a rich minefield for data analytics. Recent trends in the design of wearable devices include several embedded sensors which also provide useful data for many applications. This research presents results obtained from studying human-activity related data, collected from wearable devices. The activities considered for this study were working at the computer, standing and walking, standing, walking, walking up and down the stairs, and talking while walking. The research entails the use of a portion of the data to train machine learning algorithms and build a model. The rest of the data is used as test data for predicting the activity of an individual. Details of data collection, processing, and presentation are also discussed. After studying the literature and the data sets, a Random Forest machine learning algorithm was determined to be best applicable algorithm for analyzing data from wearable devices. The software used in this research includes the R statistical package and the SensorLog app.

scholarly journals Machine Learning for Conservation Planning in a Changing Climate

2020 ◽  
Vol 12 (18) ◽  
pp. 7657
Author(s):  
Ana Cristina Mosebo Fernandes ◽  
Rebeca Quintero Gonzalez ◽  
Marie Ann Lenihan-Clarke ◽  
Ezra Francis Leslie Trotter ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Climate Projections ◽  
Sage Grouse ◽  
Climate Conditions ◽  
Fragmented Habitat ◽  
Related Data ◽  
Temperature And Precipitation ◽  
Methodical Approach

Wildlife species’ habitats throughout North America are subject to direct and indirect consequences of climate change. Vulnerability assessments for the Intermountain West regard wildlife and vegetation and their disturbance as two key resource areas in terms of ecosystems when considering climate change issues. Despite the adaptability potential of certain wildlife, increased temperature estimates of 1.67–2 °C by 2050 increase the likelihood and severity of droughts, floods, heatwaves and wildfires in Utah. As a consequence, resilient flora and fauna could be displaced. The aim of this study was to locate areas of habitat for an exemplary species, i.e., sage-grouse, based on current climate conditions and pinpoint areas of future habitat based on climate projections. The locations of wildlife were collected from Volunteered Geographic Information (VGI) observations in addition to normal temperature and precipitation, vegetation cover and other ecosystem-related data. Four machine learning algorithms were then used to locate the current sites of wildlife habitats and predict suitable future sites where wildlife would likely relocate to, dependent on the effects of climate change and based on a timeframe of scientifically backed temperature-increase estimates. Our findings show that Random Forest outperforms other competing models, with an accuracy of 0.897, and a sensitivity and specificity of 0.917 and 0.885, respectively, and has great potential in Species Distribution Modeling (SDM), which can provide useful insights into habitat predictions. Based on this model, our predictions show that sage-grouse habitats in Utah will continue to decrease over the coming years due to climate change, producing a highly fragmented habitat and causing a loss of close to 70% of their current habitat. Priority Areas of Conservation (PACs) and protected areas might be deemed insufficient to halt this habitat loss, and more effort should be put into maintaining connectivity between patches to ensure the movement and genetic diversity within the sage-grouse population. The underlying data-driven methodical approach of this study could be useful for environmentalists, researchers, decision-makers, and policymakers, among others.

scholarly journals A Tree Based Machine Learning Approach for PTB Diagnostic Dataset

2021 ◽  
Vol 2115 (1) ◽  
pp. 012042
Author(s):  
S Premanand ◽  
Sathiya Narayanan
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Primary Objective ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Related Data ◽  
Machine Learning Approach ◽  
Health Related

Abstract The primary objective of this particular paper is to classify the health-related data without feature extraction in Machine Learning, which hinder the performance and reliability. The assumption of our work will be like, can we able to get better result for health-related data with the help of Tree based Machine Learning algorithms without extracting features like in Deep Learning. This study performs better classification with Tree based Machine Learning approach for the health-related medical data. After doing pre-processing, without feature extraction, i.e., from raw data signal with the help of Machine Learning algorithms we are able to get better results. The presented paper which has better result even when compared to some of the advanced Deep Learning architecture models. The results demonstrate that overall classification accuracy of Random Forest, XGBoost, LightGBM and CatBoost, Tree-based Machine Learning algorithms for normal and abnormal condition of the datasets was found to be 97.88%, 98.23%, 98.03% and 95.57% respectively.

scholarly journals The Sources of Surface Defects in Castings Produced in Automated Process Lines

2015 ◽  
Vol 15 (4) ◽  
pp. 91-94 ◽  
Author(s):  
R. Wron ◽  
M. Brzeziński ◽  
E. Ziółkowski
Keyword(s):  
Surface Defects ◽  
Casting Process ◽  
Quality Parameters ◽  
Molten Metals ◽  
Moisture Contents ◽  
Slag Inclusions ◽  
Iron Foundry ◽  
Metallic Compounds ◽  
Process Line ◽  
Automated Process

Abstract This study summarises the research efforts undertaken in iron foundry plants in which the process are mostly automated and mechanised. The research program was limited in scope, focusing on causes of surface defects in castings products that are attributable to the bentonite-containing sand and the mould system. One of the potential roots of surface defects is heterogeneity of sand grains, containing lumped ball-shaped grains and irregular pellets with a layered-structure. The moisture contents of those lumped grains is different than the moisture level required in the process, besides these grains may contain various elements and metallic compounds which, when cast into moulds, may react with molten metals in an uncontrolled manner. As a result, surface defects are produced, such as surface blowholes, burst penetration, sand holes, slag inclusions, pinhole porosity. This study investigated the efficiency of key sand preparation and moulding machines and installations integrated into the casting process line. The efficiency of machines and installations is defined in terms of quality parameters of sand mix and moulds, which are associated with the emergence of surface defects on castings.

Comparative Study of Machine Learning Algorithms on Prediction of Diabetes Mellitus Disease

2020 ◽  
Vol 17 (1) ◽  
pp. 201-205
Author(s):  
Gina George ◽  
Anisha M. Lal ◽  
P. Gayathri ◽  
Niveditha Mahendran
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Ensemble Methods ◽  
Classification Problem ◽  
Disease Diagnosis ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Related Data

Diabetes Mellitus disease is said to occur when there is not proper generation of insulin in the body which is needed for proper regulation of glucose in the body. This health disorder leads to whole degradation of several organs including the heart, kidneys, eyes, nerves. Hence diabetes disease diagnosis by means of accurate prediction is vital. When such disease related data is given as input to several machine learning techniques it becomes an important classification problem. The purpose of the work done in this paper is to compare several classic machine learning algorithms including decision tree, logistic regression and ensemble methods to identify the more accurate classification algorithm for better prediction of the diabetes mellitus disease. This in turn would help for better and effective treatment.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

Sign in / Sign up

Export Citation Format

Share Document