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Automatic Temperature Analysis of an Image Sensor using an Application in a Semiconductor Industry
(2024-01-01)
Wongsomboon, Chanathip
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Maneerat, Noppadol
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Thudthong, Jakkrit
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Sukasem, Sutikamon
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Yajima, Kuniaki
Manufacturing process improvement is a way to increase the efficiency of production in the process even further. This research is used to change the current work method where employees download oven temperature data of the image sensor in manual CSV file format to analyze and make reports in graph form. The window application is created the data to get the data from the PLC control machine to record temperature data from a CSV file and record it in the SQL database automatically. The web application is also developed to serve as a dashboard for data analysis. It is possible to reduce operating time by 91 percents and can reduce the cost of hiring employees.
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Productivity Increasing using the Automation Machine for Arranging Wheels into the Finished Goods Rack in a Wheels Assembly Manufacturer
(2023-01-01)
Thudthong, Jakkrit
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Maneerat, Noppadol
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Wongsomboon, Chanathip
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Sukasem, Sutikamon
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Pasaya, Bundit
The automotive parts manufacturing business has continuously increased market competition. The production costs are always discussed when negotiating prices. (The price of the product is often taken into account in always negotiating with customers) Therefore, manufacturers focus on continuously reducing production costs. To maintain reasonable profits in this research, a method to reduce costs by creating an automation system to replace manual labor in the wheel packaging workstation is presented. Because human labor is one of the main costs in the cost of producing goods. This research was carried out in a wheel assembly plant in the TFD Industrial Estate and after the implementation an automation system for wheel arranging. The company can reduce production costs by reducing the number of employees in the workstation packing for 3 people and can reduce production costs by 522,000 baht per year and increase productivity by 127.84% as well.
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Adaptive cutting force control for CNC milling machine
(2020-08-31)
Maneerat, Noppadol
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Khaengsarigid, Adisak
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Pasaya, Bundit
This paper presents the adaptive cutting force control for CNC milling machines. The research scheme divided the mathematical models, consisted of two parts: model of the feed drive system. Then it's designed to the adaptive cutting force controller using Lyapunov's method. The dynamometer is used to sense the cutting force and feedback to the cutting force controller. The output signal of the controller is a feed rate volume of CNC controller to control the feed drive system during a milling process. For experimental, it's defined 3 different depths of cut(DOCs). The experimental results compared to that between the actual cutting force response and the target cutting force. As experiment result, it can control the cutting force smoothly, the tool life increased and acceptable practice.
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Adaptive Cutting Force Control for CNC Milling Machine
(2020-01-01)
Maneerat, Noppadol
;
Khaengsarigid, Adisak
;
Pasaya, Bundit
This paper presents the adaptive cutting force control for CNC milling machines. The research scheme divided the mathematical models, consisted of two parts: model of the feed drive system. Then it's designed to the adaptive cutting force controller using Lyapunov's method. The dynamometer is used to sense the cutting force and feedback to the cutting force controller. The output signal of the controller is a feed rate volume of CNC controller to control the feed drive system during a milling process. For experimental, it's defined 3 different depths of cut(DOCs). The experimental results compared to that between the actual cutting force response and the target cutting force. As experiment result, it can control the cutting force smoothly, the tool life increased and acceptable practice.
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Modified ant colony optimization with updating pheromone by leader and re-initialization pheromone for travelling salesman problem
(2018-08-13)
Ratanavilisagul, Chiabwoot
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Pasaya, Bundit
Ant Colony Optimization (ACO) algorithm is a stochastic algorithm. It is used for solving combinational optimization problem. The ant colony walks along density of pheromone from ant's nest to feeding sources. It leads to create shortest path from ant's nest to feeding sources. Normally, ACO encounters the problem of trapping in local optimum. To improve solutions, 2-Opt algorithm is applied with ACO. However, 2-Opt algorithm cannot solve trapping in local optimum of ACO and cannot improve searching performance of ACO. This paper proposed improving ACO algorithm by the results from searching of 2-Opt algorithm are applied with pheromone of ants. Moreover, when ant colony occur trapping in local optimum, the pheromone of ants is re-initialized to solve trapping in local optimum problem. The proposed technique is tested on twenty-three maps from the Traveling Salesman Problem Library (TSPLIB) and gives more satisfied search results in comparison with ACOs.
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Feature extraction from retinal fundus image for early detection of diabetic retinopathy
(2013-12-01)
Sreng, Syna
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Takada, Jun Ichi
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Maneerat, Noppadol
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Isarakorn, Don
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Varakulsiripunth, Ruttikorn
Automated detection of lesions in retinal fundus image can be aid in the detection of diabetic retinopathy. Exudates are the early sign of diabetic retinopathy so the proper detection of these lesions is an essential task in an automatic retinal screening. On the research work leading to automatic analysis of exudate detection, the knowledge of Optic Disk (OD) location is very useful. An efficient algorithm is presented to detect the OD and exudate which are the most important features for early detection of diabetic retinopathy. From a retinal fundus image, the proposed method first preprocesses and estimates the histogram of retinal background, then filters out the bright pixels in intensity image. They include OD, and non-OD (exudates and noise). Next, an OD boundary is determined and eliminated after applying blob boundary measurement and morphological reconstruction. Finally, exudates are extracted by applying the maximum entropy thresholding to filter out the bright pixels from the green component of retinal image which OD region inside is eliminated. The proposed technique has been tested first on 100 images from hospital. Experimental results show that 93% and 89% of OD and exudate were detected correctly, respectively. © 2013 IEEE.
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Automatic exudate extraction for early detection of Diabetic Retinopathy
(2013-01-01)
Sreng, Syna
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Takada, Jun Ichi
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Maneerat, Noppadol
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Isarakorn, Don
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Pasaya, Bundit
Diabetic Retinopathy (DR) is the most common cause of blindness in diabetic patients, but early detection and timely treatment can prevent this problem. Exudates have been found to be one of the signs and serious DR anomalies so the proper detection of these lesions and the treatment should be done immediately to prevent loss of vision. The aim of this study is to automatically detect these lesions in fundus images. To achieve this goal, the proposed method first preprocesses to improve the quality of fundus image, and then Optic Disc (OD) is detected and eliminated to prevent the interference to the result of exudate detection by combination of 3 methods; image binarization, Region Of Interest (ROI) based segmentation and Morphological Reconstruction (MR). Next, exudates are detected by applying the maximum entropy thresholding to filter out the bright pixels from the result of OD region eliminated. Since the result contains some noises which appear as bright light at the edge of fundus area in some images, that affect is considered and eliminated to improve the result of false positive. Finally, exudates are extracted by using MR. The proposed technique has been tested on 100 fundus images from hospital. Experimental results show that 91 % of exudate is extracted correctly with the average process of 3.92 second per image. © 2013 IEEE.
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Automatic microaneurysms detection through retinal color image analysis
(2013-01-01)
Yunuch, Preeyaporn
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Maneerat, Noppadol
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Isarakorn, Don
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Pasaya, Bundit
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Panjaphongse, Ronakorn
This paper proposes an automatic system to diagnose the diabetic retinopathy symptom, which can cause a loss of vision by analysis the abnormality in retinal image. Digital image processing system is developed for the retinal image analysis which helps ophthalmologists to identify diabetic patients. The retinal images derived from ophthalmologists are used to analysis by using HSV, area identification and eccentricity techniques to distinguish diabetic retinopathy symptoms from normal diabetic patients. First color bar is evaluated by using HSV method and then using the eccentricity technique with area of pixel to find out the abnormality of Microaneurysms (MAs). The accuracy result of experiment is around 93% when compares to the analysis of ophthalmologists. © 2013 IEEE.

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