Automatic Sorting Machine Research Articles

Online sorting of surface defective walnuts based on deep learning

To address concerns regarding walnut shell damage and inadequate sorting precision during the mechanized sorting of walnuts, a walnut automatic sorting machine was designed based on deep learning and experimental research. Initially, the rationality of the design was verified through experiment. Then, three deep learning semantic segmentation algorithms, namely PSPnet, U-net, and Deeplabv3+, were selected to train walnut detection models. Results indicated that the U-net algorithm proved to be the most effective, achieving a Mean Intersection over Union of 96.71% and a Mean Pixel Accuracy value of 98.52%. Finally, performance tests were conducted on the prototype machine, yielding results with an average sorting efficiency of 51.70 kg/h, an average loss rate of 6.50%, and an average accuracy of sorting walnuts of 92.98%. The findings can provide insights for future structural improvements and operational parameter optimization of walnut automatic sorting machines.

Automatic Sorting Machine Using PLC and HMI Technique

The automatic sorter machine using PLC is a system that sorts productsbasedonspecificcriteriasuchas size, weight, or color. The system consists of a programmable logic controller (PLC), sensors, actuators, motors, conveyor system, power supply, programming software, HMI development software, and communication protocols. The sorting process is automated, with the PLC controlling them ovement of products to the sorting stations based on the selected sensors'feedback. The HMI allows the operator to monitor and control the sorting process and displays information such as the status of the machine, the number of products sorted, and any errors or alarms. The system's design and implementation require careful consideration of the sorting requirements, selection of appropriate sensors and actuators, design of the control algorithm, programming of the PLC, development of the HMI, testing and commissioning, and maintenance and optimization. The automatic sorter machine using PLC is a reliable and efficient solution for automated sorting processes in various industries such as manufacturing, packaging, and logistics.

Arduino based Automatic sorting machine based on Optical sensor

Arduino based Automatic sorting machine based on Optical sensor

Automatic Sorting Machine

The invention discloses an automatic sorting machine which comprises a first conveying platform, a second conveying platform, a general console and a plurality of connecting units; the first conveying platform and the second conveying platform are used for conveying articles thereon respectively; the general console is used for controlling the first conveying platform and the second conveying platform; a plurality of connecting units are connected with the first conveying platform and the second conveying platform; the first conveying platform is provided with a first conveyor belt; the second conveying platform is provided with a second conveyor belt; the general console is provided with a starting and stopping switch for controlling starting and stopping of the first conveyor belt and a starting and stopping switch for controlling starting and stopping of the second conveyor belt; the article on the first conveying platform is provided to the second conveying platform by a plurality of connecting units; and at least one wake-up switch is arranged on one of the first conveying platform and the second conveying platform, is connected to the general console and can also be operated to start the stopped conveyor belt on the conveying platform located by the wake-up switch. The automatic sorting machine provided by the invention saves the operation time and is convenient in operation and high in automation degree.

Design and production of an automatic solid waste sorting machine with smart digital counter

Sorting of objects is the first step of a waste management process. These objects should be sorted according to the type of material for effective waste recycling. Therefore, the focus of this work is to design and produce an automated sorting machine that can be used to sort metals and non-metals autonomously. The automatic sorting machine with a smart digital counter was designed to use a microcontroller (Arduino Mega) to control the conveyor systems which consist of the conveyor support, driving unit and the driven unit. The sorting mechanisms consist of the inductive sensor, infrared Sensor and the actuating system using servo motor. The conveyor system helps in the conveyance of objects towards the sorting chute. The actuating system receives signal from the microcontroller and the device does the sorting of the objects based on their characteristics while the digital counter counts the number of metallic objects dropping into the designated cart. The time taken for the inductive sensor to sense an object, the waiting period for the servo motor to be actuated and the time taken for the counter to count after a metallic object is sensed are 1s, 2s and 1s respectively. After its development, it was tested and observed to meet the design requirements.

Computer Vision Based Two-stage Waste Recognition-Retrieval Algorithm for Waste Classification

The main objective of this study is to classify domestic waste via computer vision and sort it automatically according to the four-category regulation. A novel two-stage Waste Recognition-Retrieval algorithm (W2R) is proposed. Its first stage was to train a Recognition Model (RegM) recognizing waste into one of thirteen subcategories. The second stage was to construct the Recognition-Retrieval Model (RevM) classifying the recognized subcategory into one of four categories. Meanwhile, a one-stage waste Classification Model (ClfM) was trained as a comparison. Both best-performing models were selected and installed respectively onto the automatic sorting machine for contrast experiment classifying a set of waste. The machine consisted of three main modules: the Computer-Vision Module, the Sorting Module, and the Customized Module. It was also a platform for data collection. Ten participants also classified and sorted the same set of waste in the experiment of Manual Sorting (MS). The experimental results show that the average accuracy of the RevM, 94.71% ± 1.69, was significantly higher than that of the ClfM-VGG, 69.66% ± 3.43, and that of the MS, 72.50% ± 11.37.

Experimental modelling and analysis of semi-automated waste black plastic materials sorter

Modern world meets a lot of challenges that includes smart waste management system. It becomes a big concern if proper disposal system is not managed. Managing of waste effectively and recycling efficiently is a major goal for every nation. There are many automatic waste material sorting facilities available, which are being used to sort metals, plastics and glass bottle wastes. Sorting plastics based on type of plastics, makes the plastics recycling process more effective. But segregation and sorting of black coloured plastics is a major problem while sorting plastics based on their types. Also, black plastics contains many harmful chemicals like bromide and other heavy metals, which may contaminate other recycling processes of plastics. Black plastics are used as food containers which leads to serious health concern. In this research work, the difficulty of sorting black plastics is done by developing an electromechanical system using microcontroller, which sorts all types of plastics. For sorting black plastic wastes, calibrated sensors and optimized algorithms have been developed and used. This sorting process makes plastics recycling process more effective. This automatic sorting machine and effective recycling of domestic wastes will make our environment more suitable for living.

Automatic sorting of fresh tea leaves using vision‐based recognition method

AbstractMechanical tea harvesting using plucking machines is highly efficient, but harvested raw fresh tea leaves (FTLs) are always low quality because they contain a mixture of old leaves and leaf debris. To address this problem, this study developed an automatic sorting machine with a vision‐based recognition method to extract high‐quality FTLs from plucked raw FTLs. First, the raw FTLs were separated one by one after passing through three sequential conveyor belts with increasing speed, and were then classified into four grades using a vision‐based recognition method. Finally, the FTLs were blown by air nozzles into collection boxes according to their specific grade. In the recognition method, the shape‐based feature of each FTL is extracted by establishing the FTL's topological structure, and the support vector machine model is used for classification. The experimental results revealed that the vision‐based recognition method performed satisfactorily with an accuracy rate of 94% and precision rate of 85%. The sorting success rate and efficiency of the automatic sorting machine were approximately 80% and 15 kg hr−1, respectively. The results indicate that the developed automatic sorting machine can effectively and efficiently sort raw FTLs, which may improve the profitability and promote the automation of tea processing.

Automatic Sorting Machine

Businesses around the world automate their outbound profiling and sortation process for an increased productivity. Sorting plays a key role in any industrial automation, particularly in the field of packaging industries. This paper aims at providing significant low-cost automation in packaging industries that calls for sorting and how the implication of pneumatics can yield a desirable solution. The prototype of the automatic sorting machine allows a systematic sorting of the carton boxes corresponding to their sizes using a pneumatic system, which is achieved by using pneumatic cylinders and direction control valves. The sorted carton boxes are shoved from the main line conveyor to the sub-line conveyor using the pneumatic cylinder, which is then collected and dispatched to the next division for the packaging purposes. Using this approach, the packaging industries can attain the maximum benefits thereby enabling a surge in their production.

Способ и устройство для автоматического распознания некондиционных клубней картофеля

The aim of the article is to improve the quality and productivity of sorting by developing a method and an intelligent device for automatic recognition and inspection of substandard potato tubers. The article describes a prototype of an automatic sorting machine designed to recognize external defects in potato tubers and automatically inspect them with a jet of compressed air. The recognition process consisted of three main modules: segmentation, tracking a potato moving in a frame along a conveyor belt, and classification using a trained artificial neural network. For the segmentation of potato tubers against the background of the transporting conveyor in real time, a method based on the calculation of the color threshold was used. The centroid tracking algorithm was used to track moving potato tubers. To train the artificial neural network, we created our own dataset consisting of images of marketable and defective potato tubers. A prototype of an automatic sorting machine has been developed, which is based on the concept of intelligent data analysis, according to which the images of potato tubers obtained from a video camera are processed and formed into images with subsequent recognition and signaling to the executive device of the automatic inspection system in the form of a single pulse signal when determining the tuber as substandard.

Deep Learning-Based Fake-Banknote Detection for the Visually Impaired People Using Visible-Light Images Captured by Smartphone Cameras

Automatic recognition of fake banknotes is an important task in practical banknote handling. Research on this task has mostly involved methods applied to automatic sorting machines with multiple imaging sensors or that use specialized sensors for capturing banknote images in various light wavelengths. These approaches can make use of the security features on banknotes for counterfeit detection. However, they require specialized devices, which are not always available for general users or visually impaired people. Meanwhile, smartphones are becoming more popular and can be useful imaging devices. Moreover, the types of fake banknotes created by imaging devices such as smartphone cameras or scanners are sometimes cannot be recognized by especially the visually impaired people. Addressing these problems, we propose a method for classifying fake and genuine banknotes using visible-light images captured by smartphone cameras based on convolutional neural networks. Experimental results on a self-collected dataset of US dollar, Euro, Korean won, and Jordanian dinar banknotes showed that our method performs better in terms of fake detection than the state-of-the-art methods.

Development of Advanced Automatic Sorting Machine using Weighing Mechanism

This paper presents, design, build, assemble and commission of an automatic Sorting machine by using Weighing mechanism. The popular measuring technique is weighing scales or weighing machines. Weighing machines are measuring instruments which are used to determine the weight or mass of an object. The electronic weight detection system is classified into two types: the strain/ force gauge load cell and the electromagnetic force system. Strain gauge load cells are the most common in industry. In this system, Weight of the object moving in the conveyor is measured .Then it is sorted based on its weight the measured. The weight of the product is feed to the controller. The standard weight of the product is set to the controller. When the weight is not equal to the preset value, the object is sorted in one side. If it meets the value, it is sorted to the other side. This is the basic mechanism used in the sorting machine by weight.

Automatic Mackerel Sorting Machine Using Global and Local Features

In Japan, blue and chub mackerels are often caught simultaneously, and their market prices are different. Humans need to sort them manually, which requires heavy labor. The demand for automatic sorting machines is increasing. The aim of this paper is to develop an automatic sorting machine of mackerels, which is a challenging task. There are two required functions. First, it needs localization of mackerels on a conveyor belt so that mackerels can be transported to destinations. Second, species classification is needed, but it is difficult due to similar appearance among the species. In this paper, we propose an automatic sorting machine using deep neural networks and a red laser light. Specifically, we irradiate red laser to the abdomen, and the shape of the laser will be circle and ellipse on the blue and chub mackerels, respectively. We take images and use neural networks to locate the whole body and irradiated regions. Then, we classify mackerels using features extracted from the whole body and irradiated regions. Using both features makes the classification accurate and robust. The experimental results show that the proposed classification is superior to the methods using either feature of irradiated or whole body regions. Moreover, we confirmed that the automatic mackerel-sorting machine performs accurately.

Design and Testing of an On-Line Omnidirectional Inspection and Sorting System for Soybean Seeds

Abstract. At present, the manual grading of soybean seeds is both time consuming and laborious, and detecting the full-surface information of soybean seeds using an existing automatic sorting machine is difficult. To solve this problem, an on-line omnidirectional inspection and sorting system for soybean seeds was developed using embedded image processing technology. According to the principles employed by the system, the surface friction properties and full-surface information such as the shape, texture and color of soybean seeds were adopted in the study. Soybean seeds were inspected and sorted using their full surface information in combination with the embedded image processing technology. Split, worm-eaten, gray-spotted, slightly cracked, moldy and normal soybeans were used to test the system. According to the test results, the optimum design parameters of the preliminary sorting device based on the friction properties were a tilting angle of 12° and a linear velocity of 0.4 m/s. Furthermore, the optimum design parameters of the directional integrated device were a tilting angle of 19° and a linear velocity of 0.45 m/s. The sorting speed was 400 soybeans per minute with 8-channel parallel transmission. The average sorting accuracies were 99.4% for split soybeans, 98.5% for worm-eaten soybeans, 98.5% for gray-spotted soybeans, 97.7% for slightly cracked soybeans, 98.6% for moldy soybeans, and 98.9% for normal soybeans. The overall results suggest that the system can potentially meet the needs of the rapid inspection and automatic sorting of soybean seeds and provide references for research on the alternating rotational motion of granules and on-line collection of full-surface information. Keywords: Embedded image processing technology, Full surface, Granules, Inspection, On-line, Sorting, Soybean seeds.

Automatic Sorting Machine Based on Vision InspectionAutomatic Sorting Machine Based on Vision Inspection

This work describes detailed an automatic vision system for quality control of small conveyed mechatronic components. The vision measurement has the advantages of non-contact measurement, high precision and high automation degree, thus it has become one of the important trends of development in modern measurement technique. The system highlighted in this paper sorts automatically the components inspected depending on the fabrication fault type detected by means of an electro mechanic system controlled through a serial port. The proposed system has been tested for a series of 608ZZ bearings which have been evaluated through a completely automated process. The proposed system can improve the control efficiency and can be integrated in to an existing fabrication line.

Model design and simulation of automatic sorting machine using proximity sensor

The automatic sorting system has been reported to be complex and a global problem. This is because of the inability of sorting machines to incorporate flexibility in their design concept. This research therefore designed and developed an automated sorting object of a conveyor belt. The developed automated sorting machine is able to incorporate flexibility and separate species of non-ferrous metal objects and at the same time move objects automatically to the basket as defined by the regulation of the Programmable Logic Controllers (PLC) with a capacitive proximity sensor to detect a value range of objects. The result obtained shows that plastic, wood, and steel were sorted into their respective and correct position with an average, sorting, time of 9.903s, 14.072s and 18.648s respectively. The proposed developed model of this research could be adopted at any institution or industries, whose practices are based on mechatronics engineering systems. This is to guide the industrial sector in sorting of object and teaching aid to institutions and hence produce the list of classified materials according to the enabled sorting program commands.

Design and Implementation of Serial Communication for Automatic Titanium Alloy Sorting System

The heat generated during Titanium alloy cutting can cause bad products, which must be eliminated by Sorting System. This paper describes the design and implementation of serial communication between the automatic sorting machine and the host computer, introduces the system framework and the software flow, and gives a detailed introduction to the communication process, parameters setting and also gives part of the MFC dialog-based serial communication program. It shows that the design and implementation completely meets the requirements of systems performance and function.

Design and Implementation of Serial Communication for Automatic Titanium Alloy Sorting System

The heat generated during Titanium alloy cutting can cause bad products, which must be eliminated by Sorting System. This paper describes the design and implementation of serial communication between the automatic sorting machine and the host computer, introduces the system framework and the software flow, and gives a detailed introduction to the communication process, parameters setting and also gives part of the MFC dialog-based serial communication program. It shows that the design and implementation completely meets the requirements of systems performance and function.

Neural Network Based Color Recognition for Bobbin Sorting Machine

Winding is a key process in the manufacturing process of textile industry. The normal and effective operation of winding process plays a very important role on the textiles’ quality and economic effects. At present, a large proportion of bobbins which collected from winder still have yarn left over. The bobbin recycling is severely limited and quick running of winder is seriously restricted, the invention of the the automatic bobbin sorting machine has solved this problem. The ability to distinguish bobbin which has yarn left over from the rest and the classification accuracy of color are the two important performance indicators for bobbin sorting machine. According to the development and application of the color recognition technology and the artificial intelligence method, this study proposes a novel color recognition method that based on BP neural networks. The result shows that the accuracy of color recognition reaches 98%. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2820 Full Text: PDF

The Content of Color Experience

According to the central thesis of Seeing, Doing, and Knowing, sensory systems are automatic sorting machines that classify distal stimuli for the purposes of physical manipulation and investigation. The results of this classificatory activity are changed associative dispositions and conscious memories. The book is a detailed working out of the implications of this thesis, with an emphasis on perception of color. The central thesis is important and the arguments for it persuasive. Moreover, the book is rilled with a host of fascinating detail. But an important feature of the account the relativity of colors, and sense features more generally, to actions or tasks leads to serious trouble. According to the account developed in Seeing, Doing, and Knowing, colors are sense features: