Development of an automatic monitoring and control system for the objects on the conveyor belt

Vardhan, D. Sree Vishnu; Narayan, Yogendra

doi:10.1109/mami.2015.7456594

Cited by 12 publications

(2 citation statements)

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“…D.S.V. Siva vardhan, Y.Shivraj Narayan [5]:-This paper takes the light on the development of monitoring and controlling the objects on the conveyor system. Again, the monitoring is done through the software called SCADA.…”

Section: Literature Surveymentioning

confidence: 99%

A Review: Development of Testing Device for the Conveyor System Using Automation

M.Waghamare¹

2016

IJRET

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This Paper Focused on the testing device i.e. conveyor system for the quality control department using automation (PLC & SCADA). The paper belongs to the different sections such as how the conveyor system works on PLC. Second thing how the system handles the section of motors i.e. D.C. motors, solenoid etc. Third thing is the most important how it will handles the faults detected in the conveyor system and gives us the fault free output. The main goal of the project is to reduce the errors in the product size. The conveyor system also deals with the monitoring structure i.e. SCADA part of the system. SCADA gives you the visualisation structure that's makes easy to handle the system and error free output. The solenoid connects in the project will ejects the faulty material and gives you the suitable material or product.

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Section: Literature Surveymentioning

confidence: 99%

A Review: Development of Testing Device for the Conveyor System Using Automation

M.Waghamare¹

2016

IJRET

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“…Take, for instance, a programmable logic controller (PLC) designated for overseeing the operations of a conveyor belt system using a master-slave (M-S) architecture that only needs to power the motor of the conveyor belt system on and/or off based on some measured metrics such as read sensor values when the conveyed item reaches a designated point or landmark along the production line as exemplified in [15]. In a sense, every other data such as the remaining useful life (RUL) of the motor and quality of the product (the item being conveyed in this case such as the bottles and trays in [15]) that are also associated with the process may not really impact on the modi operandi of the PLC-based M-S control architecture. However, if such data are made available and analyzed, inferences drawn from such analysis can go a long way in improving the overall efficiency of the process.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments

Akinsolu

Zribi

2023

Inventions

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In this paper, the growing significance of data analysis in manufacturing environments is exemplified through a review of relevant literature and a generic framework to aid the ease of adoption of regression-based supervised learning in manufacturing environments. To validate the practicality of the framework, several regression learning techniques are applied to an open-source multi-stage continuous-flow manufacturing process data set to typify inference-driven decision-making that informs the selection of regression learning methods for adoption in real-world manufacturing environments. The investigated regression learning techniques are evaluated in terms of their training time, prediction speed, predictive accuracy (R-squared value), and mean squared error. In terms of training time (TT), k-NN20 (k-Nearest Neighbour with 20 neighbors) ranks first with average and median values of 4.8 ms and 4.9 ms, and 4.2 ms and 4.3 ms, respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of prediction speed (PS), DTR (decision tree regressor) ranks first with average and median values of 5.6784×106 observations per second (ob/s) and 4.8691×106 observations per second (ob/s), and 4.9929×106 observations per second (ob/s) and 5.8806×106 observations per second (ob/s), respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of R-squared value (R2), BR (bagging regressor) ranks first with average and median values of 0.728 and 0.728, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 0.746 and 0.746, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. In terms of mean squared error (MSE), BR (bagging regressor) ranks first with average and median values of 2.7 and 2.7, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 3.5 and 3.5, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. All methods are further ranked inferentially using the statistics of their performance metrics to identify the best method(s) for the first and second stages of the predictive modeling of the multi-stage continuous-flow manufacturing process. A Wilcoxon rank sum test is then used to statistically verify the inference-based rankings. DTR and k-NN20 have been identified as the most suitable regression learning techniques given the multi-stage continuous-flow manufacturing process data used for experimentation.

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Mathematical Modeling of the Work of the Flow-Meter Flowmeter-Doser

Vasilyev

Shestov

Shilin

et al. 2018

Advances in Intelligent Systems and Computing

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Development of an automatic monitoring and control system for the objects on the conveyor belt

Cited by 12 publications

References 3 publications

A Review: Development of Testing Device for the Conveyor System Using Automation

A Review: Development of Testing Device for the Conveyor System Using Automation

A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments

Mathematical Modeling of the Work of the Flow-Meter Flowmeter-Doser

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