Analysis of Photodiode Monitoring in Laser Cutting

Garcia, Sonia M.; Ramos, Joana; Arrizubieta, Jon Iñaki; Figueras, J.

doi:10.3390/app10186556

Cited by 11 publications

(5 citation statements)

References 26 publications

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“…In addition, some elaborate approaches like using a fiber Fabry-Pérot cavity microphone have been demonstrated [ 3 ]. To use such sensors as an industrial product, the sensor must be able to detect cut interruptions independent from the cutting direction, which is not feasible by below-bed sensors, as shown in [ 4 , 5 ]. For cut direction independent detection systems, in many publications, the sensor systems are integrated into the cutting head.…”

Section: Introductionmentioning

confidence: 99%

Laser Cut Interruption Detection from Small Images by Using Convolutional Neural Network

Adelmann

Schleier

Hellmann

2021

Sensors

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In this publication, we use a small convolutional neural network to detect cut interruptions during laser cutting from single images of a high-speed camera. A camera takes images without additional illumination at a resolution of 32 × 64 pixels from cutting steel sheets of varying thicknesses with different laser parameter combinations and classifies them into cuts and cut interruptions. After a short learning period of five epochs on a certain sheet thickness, the images are classified with a low error rate of 0.05%. The use of color images reveals slight advantages with lower error rates over greyscale images, since, during cut interruptions, the image color changes towards blue. A training set on all sheet thicknesses in one network results in tests error rates below 0.1%. This low error rate and the short calculation time of 120 µs on a standard CPU makes the system industrially applicable.

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Section: Introductionmentioning

confidence: 99%

Laser Cut Interruption Detection from Small Images by Using Convolutional Neural Network

Adelmann

Schleier

Hellmann

2021

Sensors

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“…In this study, we concentrated on creating IoT-based viscometers using photodiode and laser sensors designed primarily for laboratory applications. This photodiode sensor and laser technology combo offers a number of benefits, including high sensitivity, rapid reaction times, and non-invasive testing capabilities [8]. Previous studies have focused on manufacturing a fluid viscosity measuring instrument using an Arduino microcontroller with a photodiode sensor, but the data analysis and reading still relied on a serial monitor or LCD.…”

Section: Introductionmentioning

confidence: 99%

IoT-based viscometer fabrication using the falling ball method for laboratory applications

Nofriandi,

Yulkifli,

Asrizal

et al. 2024

IJEECS

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This study outlines the production procedure of internet of things (IoT)- enabled viscometers designed for laboratory use. These viscometers utilize photodiode sensors, lasers, and falling ball techniques. The system is equipped with a temperature sensor that is utilized to quantify the impact of temperature on viscosity. The temperature sensor’s characterization yielded a R-square value of 0.999. The photodiode and laser sensors are utilized to operate a timer within the system, ensuring precise time measurement. The R-square value for the sensor characterization is 0.996. A viscometer equipped with an integrated IoT module for seamless wireless transmission of data. The photodiode timer sensor has an accuracy of 95.76% and a precision of 99.96%, while the temperature sensor has an accuracy of 99.43% and a precision of 99.93%. The viscometer transmits the measured viscosity data to the server using wireless technology. This IoT viscometer has the potential to enhance the efficiency and precision of liquid viscosity measurement in laboratory settings. Additionally, it enables real-time monitoring and data collection for subsequent analysis and research purposes.

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“…In addition to low cost and easy mounting, photodiodes can detect process emissions in a wide wavelength range. Their high defect detection rate with minimal errors makes photodiodes the desired option for monitoring low-speed laser processes [18], [25]. However, photodiodes have so far not been studied enough to be employed for monitoring the laser scribing process in real-time.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Monitoring and Defect Detection of Laser Scribing Process of CIGS Solar Panels Utilizing Photodiodes

et al. 2022

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Laser scribing is developing rapidly in industrial applications as a method of material processing, especially in areas that require high levels of precision. This technology provides functionality and efficiency improvements in the manufacturing of solar panels. Due to the premium quality and speed requirements of the laser scribing technology, monitoring of this process in real-time is critical in order to promptly detect defects in the manufacturing process. However, common monitoring systems have been developed for other laser processes, like laser welding, which are noticeably slower than the laser scribing process. The goal of this research was to investigate the possibility of using photodiodes for real-time monitoring of the laser scribing process for Copper Indium Gallium Selenide (CIGS) solar panels. Various monitoring setup configurations were designed, developed, and examined to determine the viable option for implementation as a defect detection platform. Using different photodiode positions, the intensity of the light and the photodiode induced voltage for diffuse and specular reflections were tested, and the practical pros and cons of applying each configuration were analyzed. The capability of the monitoring system to distinct the different layers of the scribed CIGS cell was also examined to assess the penetration depth of the scribe. In addition, by performing several experiments with different scribe thicknesses and analyzing oscilloscope measurements, the optimal placement of the photodiode for accurate tracing of the scribing path was determined and verified. Key aspects of development of such monitoring system for solar panel applications were identified through this research.

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Analysis of Photodiode Monitoring in Laser Cutting

Cited by 11 publications

References 26 publications

Laser Cut Interruption Detection from Small Images by Using Convolutional Neural Network

Laser Cut Interruption Detection from Small Images by Using Convolutional Neural Network

IoT-based viscometer fabrication using the falling ball method for laboratory applications

Real-Time Monitoring and Defect Detection of Laser Scribing Process of CIGS Solar Panels Utilizing Photodiodes

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