Model design and simulation of automatic sorting machine using proximity sensor

Oladapo, Bankole I.; Balogun, Vincent A.; Adeoye, Adeyinka O.M.; Ijagbemi, Christianah Olakitan; Oluwole, Afolabi S.; Daniyan, Ilesanmi; Aghor, A. Esoso; Simeon, A.P.

doi:10.1016/j.jestch.2016.04.007

Cited by 39 publications

(20 citation statements)

References 27 publications

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“…The sorting control of sorting equipment is the responsibility of internet agent. In the past, logistics control department commonly used sorting control mode which is divided into distributed mode and aggregated mode (Oladapo et al, 2016). Internet agent combines the advantages of aggregated master-slave structure and distributed feedback mode.…”

Section: Sorting Control Based On Internet Agentmentioning

confidence: 99%

Automatic three-dimensional sorting system based on internet and database

Zhang

Cheng

2019

IJRIS

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Automatic three-dimensional sorting system is the core part of supply chain operation for logistics and warehousing department. It has some difficulty in design. The sorting distance and sorting time optimisation in traditional sorting operation is not enough. Therefore, automatic three-dimensional sorting system based on internet and database is designed. The system uses buffer zone and cargo moving area for cargo delivery through shelf area storage of goods, uses cargo sorting area to distinguish different logistics receiving area and different types of goods. This paper introduces goods sorting process in and out of storage using internet agent to realise sorting control, and designs the control, negotiation and communication principle for internet agent. At the same time SQL Server 2014 development database is selected to establish the required information entity-relation graph and feature table. Experimental results show that the sorting distance and sorting time of the system are short and it can play a great role in sorting operation.

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Section: Sorting Control Based On Internet Agentmentioning

confidence: 99%

Automatic three-dimensional sorting system based on internet and database

Zhang

Cheng

2019

IJRIS

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“…In [4], [5], an overview of the choice of industrial video cameras for machine vision systems, which are best suited for creating mineral sorting plants, is proposed. However, this paper does not propose algorithms for recognizing sorting objects.…”

Section: Introductionmentioning

confidence: 99%

Intelligent machine for sorting semi-precious minerals

Polishchuk

Tkach

Parkhomey

et al. 2021

IJEECS

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This paper proposes is devoted to solving the problem of sorting workpieces from minerals and semi-precious stones that are used in jewelry production. Intensive development of production leads to a reduction of natural resources, including semi-precious minerals. Therefore, the task of saving the use of minerals is relevant. The solution is based on the idea a new method for sorting semi-precious minerals is proposed, as well as the design of an intelligent installation for implementing this method. An increase in the accuracy of sorting semiprecious minerals has been achieved through the use of charge-coupled device (CCD) matrices, which automatically determine the volume of each mineral in 3D space. From the simulation work, it was found in comparison with manual sorting, the proposed method has reduced the amount of waste in the manufacture of jewelry by 15% ... 20%. Experimental studies were carried out using the example of such a semiprecious mineral as amber. The cost of raw amber in the amount of one kilogram on the modern market is in the range of 1200…2200 US dollars. The result confirms that the method is superior to the alternative method, with the achieved average waste saving of 20%, the proposed sorting technology makes it possible to obtain a profit of 240...440 US dollars per kilogram of amber. Ultimately, these solutions can improve the efficiency of jewelry production.

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“…The automation technology of an industrial system that uses intelligent computing systems has continued to develop rapidly recently (Kamboj et al (2019); Nikhil et al 2017; Oladapo et al (2016); Bargal et al (2016); Fluke (2015); Rosenblat et al (2014)) including the automation of sorting systems in the can recycling industry that uses object classi cation techniques based on digital images (Resti et al (2018); Resti et al (2017b)). Classi cation of cans based on digital images of cans placed on a static conveyor belt can be seen in (Resti et al (2019); Resti et al (2017a); Resti (2015); Yani et al (2009)).…”

Section: Introductionmentioning

confidence: 99%

Performance of Cans Classification System for Different Conveyor Belt Speed using Naïve Bayes

Resti

Burlian²,

Yani

2020

sci. technol. indones.

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The classification system in the sorting process in the can recycling industry can be made based on digital images by exploring the basic color pixel values of images such as R, G, and B as variable inputs. In real time, the classification of cans in the sorting process occurs when cans placed on a conveyor belt move at a certain speed. This paper discusses the performance of can classification systems using the Naïve Bayes method. This method can handle all types of variables, including when all variables are continuous. Two types of conveyor belts are designed to get different speeds, and all images of the cans are captured on both conveyor belts. Two models of Bayes naive are built on the basis of the different distribution assumptions; the original model (all Gaussian distributed) and the model based on the best distribution. Performance of the classification system is built by dividing data into the learning data and the testing data with a composition of 50:50 in which each data is designed into 50 groups with different percentages on each type of cans using sampling technique without replacement. The results obtained are, first, the speed of the conveyor belt when capturing an image affects the pixel values of red, green, and blue and ultimately affects the results of the classification of cans. Second, not all input variables are Gaussian distributed. The classification system was built using assumption the best distribution model for each input variable has the better average accuracy level than the model that assumes all input variables are Gaussian distributed, and the accuracy level of classification on the first speeds of conveyor belt with a gear ratio of 12:30 and a diameter of 35 mm has an accuracy that is better than the other speed, both on the original model and the model based on the best distribution. However, it is necessary to test more statistical distribution models to obtain significant results.

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Model design and simulation of automatic sorting machine using proximity sensor

Cited by 39 publications

References 27 publications

Automatic three-dimensional sorting system based on internet and database

Automatic three-dimensional sorting system based on internet and database

Intelligent machine for sorting semi-precious minerals

Performance of Cans Classification System for Different Conveyor Belt Speed using Naïve Bayes

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