Machine Learning: Supervised Algorithms to Determine the Defect in High‐Precision Foundry Operation

BramahHazela,; Hymavathi, J.; Kumar, T. Rajasanthosh; Kavitha, S.; Deepa, D.; Lalar, Sachin; Karunakaran, Prabakaran

doi:10.1155/2022/1732441

Cited by 28 publications

(4 citation statements)

References 26 publications

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“…Since the advent of mass media, the advertising industry has relied heavily on personalization to reach its target audience [12,13,14]. Machine learning-based methods have now been included in ad personalization development.…”

Section: Literature Reviewmentioning

confidence: 99%

“…By eliminating the need for customers to wait in lengthy checkout lines, SST has been shown to improve customer happiness and convenience. [1,2] SST, which uses barcode scanning and self-service payment equipment, is mainly deployed in major retail centres [12]. Indeed, the checkout capacity is theoretically increased at peak moments [1] when customers take their baskets to self-checkout terminals to pay and get their purchases.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, the checkout capacity is theoretically increased at peak moments [1] when customers take their baskets to self-checkout terminals to pay and get their purchases. Current self-checkout technologies are already widely used in ordinary grocery shops, making them a good fit for the digital transformation of these businesses [12]. However, they are not relevant in a quick-service restaurant setting.…”

Section: Introductionmentioning

confidence: 99%

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Personalized recognition system in online shopping by using deep learning

Devarakonda Venkata,

Donda,

Madhavi

et al. 2024

EAI Endorsed Trans IoT

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This study presents an effective monitoring system to watch the Buying Experience across multiple shop interactions based on the refinement of the information derived from physiological data and facial expressions. The system's efficacy in recognizing consumers' emotions and avoiding bias based on age, race, and evaluation gender in a pilot study. The system's data has been compared to the outcomes of conventional video analysis. The study's conclusions indicate that the suggested approach can aid in the analysis of consumer experience in a store setting.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Personalized recognition system in online shopping by using deep learning

Devarakonda Venkata,

Donda,

Madhavi

et al. 2024

EAI Endorsed Trans IoT

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“…The foundry process converts liquid metal into the desired shape, that is., metal casting [1]. At present, the pouring task of large castings still relies mainly on manual participation.…”

Section: Introductionmentioning

confidence: 99%

Design, simulation, control of a hybrid pouring robot: enhancing automation level in the foundry industry

Chengjun,

Hao,

Long

2024

Robotica

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Currently, workers in sand casting face harsh environments and the operation safety is poor. Existing pouring robots have insufficient stability and load-bearing capacity and cannot perform intelligent pouring according to the demand of pouring process. In this paper, a hybrid pouring robot is proposed to solve these limitations, and a vision-based hardware-in-the-loop (HIL) control technology is designed to achieve the real-time control problems of simulated pouring and pouring process. Firstly, based on the pouring mechanism and the motion demand of ladle, a hybrid pouring robot with a 2UPR-2RPU parallel mechanism as the main body is designed. And the equivalent hybrid kinematic model was established by using Eulerian method and differential motion. Subsequently, a motion control strategy based on HIL simulation technique was designed and presented. The working space of the robot was obtained through simulation experiments to meet the usage requirements. And the stability of the robot was tested through the key motion parameters of the robot joints. Based on the analysis of pouring quality and trajectory, optimal dynamic parameters for the experimental prototype are obtained through water simulation experiments, the pouring liquid height area is 35–40 cm, the average flow rate of pouring liquid is 112 cm3/s, and the ladle tilting speed is 0.0182 rad/s. Experimental results validate the reasonableness of the designed pouring robot structure. Its control system realizes the coordinated movement of each branch chain to complete the pouring tasks with different variable parameters. Consequently, the designed pouring robot will significantly enhance the automation level of the casting industry.

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On the potential of using ensemble learning algorithm to approach the partitioning coefficient (k) value in Scheil–Gulliver equation

Li,

Tan,

Jarfors

et al. 2024

Materials Genome Engineering Advances

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The Scheil–Gulliver equation is essential for assessing solid fractions during alloy solidification in materials science. Despite the prevalent use of the Calculation of Phase Diagrams (CALPHAD) method, its computational intensity and time are limiting the simulation efficiency. Recently, Artificial Intelligence has emerged as a potent tool in materials science, offering robust and reliable predictive modeling capabilities. This study introduces an ensemble‐based method that has the potential to enhance the prediction of the partitioning coefficient (k) in the Scheil equation by inputting various alloy compositions. The findings demonstrate that this approach can predict the temperature and solid fraction at the eutectic temperature with an accuracy exceeding 90%, while the accuracy for k prediction surpasses 70%. Additionally, a case study on a commercial alloy revealed that the model's predictions are within a 5°C deviation from experimental results, and the predicted solid fraction at the eutectic temperature is within a 15% difference of the values obtained from the CALPHAD model.

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Machine Learning: Supervised Algorithms to Determine the Defect in High‐Precision Foundry Operation

Cited by 28 publications

References 26 publications

Personalized recognition system in online shopping by using deep learning

Personalized recognition system in online shopping by using deep learning

Design, simulation, control of a hybrid pouring robot: enhancing automation level in the foundry industry

On the potential of using ensemble learning algorithm to approach the partitioning coefficient (k) value in Scheil–Gulliver equation

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