Photodiode Sensor Research Articles

Machine learning-based layer-wise detection of overheating anomaly in LPBF using photodiode data

Overheating anomaly detection is essential for the quality and reliability of parts produced by laser powder bed fusion (LPBF) additive manufacturing (AM). In this research, we focus on the detection of overheating anomalies which can lead to various defects in the part including geometric distortion, and poor surface roughness, among others, using photodiode sensor data. Photodiode sensors can collect high-frequency data from the melt pool, reflecting the process dynamics and thermal history. Hence, the proposed method offers a machine learning (ML) framework to utilize photodiode sensor data for layer-wise detection of overheating anomalies. In doing so, three sets of features are extracted from the raw photodiode data: MSMM (mean, standard deviation, median, maximum), MSQ (mean, standard deviation, quartiles), and MSD (mean, standard deviation, deciles). These three datasets are used to train several ML classifiers. Cost-sensitive learning is used to handle the class imbalance between the “anomalous” layers (affected by overheating) and “nominal” layers in the benchmark dataset. To boost detection accuracy, our proposed ML framework involves utilizing the majority voting ensemble (MVE) approach. First, the top three ML classifiers are identified from an initial pool of classifiers, based on their performance in k-fold cross-validation. Next, final predictions are generated using majority voting from the individual predictions of the top three classifiers. We performed 100 iterations to generate statistically reliable results. This proposed method is demonstrated using a case study including an open benchmark dataset of photodiode measurements from an LPBF specimen with deliberate overheating anomalies at some layers. The results from the case study demonstrate that the MSD features yield the best performance for all classifiers, and the MVE classifier (with a mean F1-score of 0.8654) surpasses the individual ML classifiers. Moreover, our machine learning methodology achieves superior results (9.66% improvement in mean F1-score) in detecting layer-wise overheating anomalies, surpassing the existing methods in the literature that use the same benchmark dataset. Finally, based on our results, we provide useful insights and recommendations for future research on applying machine learning techniques for defect detection in AM.

Remote Laboratory Based on the Internet of Things for E-Learning: A Development Model of Newton’s Law Experiment

Remote laboratory is a development of digital technology to support the quality of learning in this digital era. However, scientific processes often cannot be accommodated in digital spaces such as e-learning. This research highlights a remote laboratory system that can accommodate scientific process improvement in e-learning. The research objective is to develop and determine the performance of the remote laboratory system of Newton’s Law experiment based on IoT for e-learning as an experiment development model. Research methods can be classified into design and development, abbreviated as DDR. The remote laboratory system is designed and developed in six phases. This system is developed by five main components, namely, a photodiode sensor, MCU nodes, motor drivers, stepper motor, and ESP 32 CAM. The results indicate that the remote laboratory system of Newton's law experiment has demonstrated positive performance, and the accuracy and precision of measurement from the remote laboratory system are classified as high. Accordingly, the remote laboratory system of Newton's law experiment can be used as an alternative to support scientific processes in e-learning. It is expected to serve as a guide for virtual laboratory design, enlightening the audience on the potential of this system. It is used extensively for experimental teaching in modern physics education. The success in designing and developing an experimental model of Newton's law by implementing a remote laboratory based on IoT provides a good opportunity to develop various more sophisticated physics experimental systems to support the science process and e-learning.

Tailoring Laser Powder Bed Fusion Process Parameters for Standard and Off-Size Ti6Al4V Metal Powders: A Machine Learning Approach Enhanced by Photodiode-Based Melt Pool Monitoring

An integral part of laser powder bed fusion (LPBF) quality control is identifying optimal process parameters tailored to each application, often achieved through time-consuming and costly experiments. Melt pool dynamics further complicate LPBF quality control due to their influence on product quality. Using machine learning and melt pool monitoring data collected with photodiode sensors, the goal of this research was to efficiently customize LPBF process parameters. A novel aspect of this study is the application of standard and off-size powder feedstocks. Ti6Al4V (Ti64) powder was used in three size ranges of 15–53 µm, 15–106 µm, and 45–106 µm to print the samples. This facilitated the development of a process parameters tailoring system capable of handling variations in powder size ranges. Ultimately, per each part, the associated set of light intensity statistical signatures along with the powder size range and the parts’ density, surface roughness, and hardness were used as inputs for three regressors of Feed-Forward Neural Network (FFN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The laser power, laser velocity, hatch distance, and energy density of the parts were predicted by the regressors. According to the results obtained on unseen samples, RF demonstrated the best performance in the prediction of process parameters.

A novel machine learning-based approach for in-situ surface roughness prediction in laser powder-bed fusion

Controlling and optimizing surface roughness remain a significant challenge in laser powder bed fusion (LPBF). Surface roughness affects printed part quality, particularly fatigue life, leading to costly post-processing. Nevertheless, tailored roughness can be beneficial to specific fields in engineering and medicine, such as cooling or osteointegration. Consequently, the importance of quality assurance, in-situ monitoring, and automatic anomaly detection has been elevated among the users of the LPBF process. In-situ surface roughness detection remains a less explored area within all in-situ defect detection. Additionally, existing studies primarily employ camera-based methods, which come with inherent limitations, including sensitivity to ambient light conditions, compromises between resolution and field of view, and the requirement for additional equipment such as adaptive optical filters. This study pioneers using a photodiode sensor, offering faster response times for real-time surface roughness prediction. Integrating this sensor with machine learning (ML) algorithms establishes a robust framework for surface roughness prediction. The methodology involves analyzing the captured light intensity from the melt pool by an on-axial photodiode and incorporating additional process variables into ML models to predict surface roughness within each small area of the printed part (690 µm × 510 µm), even at edges and corners. Multiple ML algorithms are trained and rigorously validated with unseen data. This comprehensive analysis encompasses the impact of process parameter variations on a wide range of surface roughness values, affirming the efficiency of the proposed methodology.

Preliminary Study of Resonance Systems States in Surface Plasmon Resonance Systems

Abstract Resonance between light and collective electrons is known as Surface Plasmon Resonance or SPR. The development of SPR-based sensors can generate the use of a Kretschmann configuration prism. Otto configuration dielectric medium between prism and gold thin film. For the Kretschmann configuration, a gold thin film between the prism and dielectric medium. So, this research focuses on the use of the Kretschmann configuration in the SPR system because the setup of the half-cylindrical prism is less complicated than using the Otto configuration. In this research, we compare the reflectance of an uncoated half-cylindrical prism and a half-cylindrical prism coated with a gold thin film to see the resonance effect. The beam reflected by the back surface of the prism was detected using a photodiode sensor (OPT-101, Texas Instruments). The photodiode sensor is connected to Arduino Uno, and the voltage reading results are processed with Arduino Ide software. Adjusting the He-Ne laser light’s angle of incidence and the prism’s angle of impact 30° – 50°. The experimental results show that the TIR (total internal reflection) phenomenon occurs in half-cylindrical prisms without a gold coating. Meanwhile, in the half-cylindrical prism coated with gold, a resonance phenomenon can be seen, this case, at a particular angle of incidence, can be detected through a reduction in the beam’s intensity of the reflected light. The photodiode sensor detects a decrease in voltage value, which indicates an intensity decrease, which was originally 4.34 volts to 2.52 volts when the incident angle of the light beam was 45°.

Process identification of laser powder bed fusion anomalies based on process-monitored melt pool radiation intensity data

Laser powder bed fusion (LPBF)) has great application prospects in aerospace and other fields, but low process stability and difficult quality assurance in the process of Laser Powder Bed Fusion are the key problems that restrict its extensive development at present. One of the important ways to solve this problem is to realize online monitoring of molten pool and closed-loop quality control. In this paper, the photodiode sensor is used to collect the radiation signal of the molten pool in real time, and the characteristics of the radiation intensity signal of the molten pool in time domain and frequency domain are extracted. The most appropriate modeling features are selected by comparing and analyzing different features, and XGBoost classical integration algorithm is used to model, and the relationship between the radiation intensity signal of the molten pool and process parameters is established, which provides a new method for abnormal process identification and quality control.

Narrow bandgap silver mercury telluride alloy semiconductor nanocrystal for self-powered midwavelength-infrared photodiode

Infrared colloidal quantum dots (CQDs) have been of interest due to their low-cost fabrication and facile wavelength tunability for various infrared optoelectronic applications. Recently, the mid-wavelength infrared (MWIR) quantum dot sensor has been successfully realized by forming a photodiode via a post-chemical treatment method. Controlling the doping density of the quantum dot solid and engineering the device structure require an extremely sophisticated technique, which hinders consistent doping density and restricts further development in understanding the fundamental photophysics and manufacturing process. Here, we report an air-stable and highly reproducible MWIR CQDs photodiode by incorporating synthesized p-doped Ag-HgTe colloidal nanocrystals (NCs). The Ag-HgTe alloy NCs allow clearly defined p-doped QDs layers, leading to uniform dopant distribution and the ease of engineering device fabrication. By optimizing the doping density, we achieved an average noise equivalent temperature difference of below 10 mK at 78 K with the self-powered MWIR photodiode sensor.

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

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.

Drone: Firefighter

Abstract: This paper introduces the drone: a firefighting device that makes use of low-fee photodiode sensors to discover fires. The layout reduced size, energy consumption and value financial savings even as preserving performance. We speak the machine layout, take a look at strategies, and outcomes, focusing on the effectiveness of infrared radiation detection. The paper concludes with limitation and destiny developments, paving the way for similarity development of drone firefighting generation.

Design of an Internet of Things (IoT)-Based Photosynthetically Active Radiation (PAR) Monitoring System

Photosynthetically Active Radiation (PAR) is an important parameter in the plant photosynthesis process, which can relate to plant growth, crop water use, and leaf gas exchange. Previously, many researchers utilized commercially available sensors to monitor PAR. The high cost of the commercially available PAR sensors has limited researchers, agricultural professionals, and farmers to use and expand PAR monitoring in agricultural lands. Thus, this paper focuses on designing an affordable Internet of Things (IoT)-based PAR sensor monitoring system including 3D-printed enclosures (waterproof) for the sensors, performance evaluation of multiple light sensors, solar powering configuration, cloud setup, and cost analysis. Three sensors, including VTB8440BH photodiode, SI 1145, and LI-190R sensors, were evaluated. The 3D-printed waterproof enclosures were designed for the photodiode and SI 1145. Particle Boron was used for recording and sending the sensor data to the IoT webserver. Both the photodiode and SI 1145 were compared to LI-190R, which is the industry standard. In the calibration process, the R2 values of the photodiode and SI 1145 with LI-190R were 0.609 and 0.961, respectively. Field validation data shows that SI 1145 had a strong correlation with LI-190R. In addition, the performance evaluation data shows the photodiode had a weaker correlation with LI-190R than SI 1145. In conclusion, the study successfully developed and designed affordable and reliable IoT-based PAR sensor monitoring systems, including a 3D-printed housing, hardware, programming, and IoT website. SI 1145 with a glass filter is an alternative sensor to monitor PAR at a low cost and has the advantage of being connected to IoT microcontrollers.

DESAIN & PERANCANGAN ALAT PEMISAH KUALITAS BUAH JERUK LEMON OTOMATIS

The process of sorting fruits at this time still uses conventional methods, namely the use of human labor (manual). Therefore, a system is needed that can carry out the sorting process automatically in a short time. The purpose of this study is to determine the components of the lemon quality separator along with its automation system and the distribution of static loading on the frame of the lemon quality separator. The automation system used is a TCS3200 sensor (green / yellow color selection) and a photodiode sensor (large / small lemon size). The frame construction was designed using solidworks 2018 software. Material used in hollow iron frame (ASTM A36). Based on the results of the study showed that the maximum voltage on the frame (frame) is 2.470 MPa and the minimum is 0.100 MPa. The maximum value at displacement is 0.027 mm, and the minimum value at safety of factor is 82.582.

Hyperspectral and Photodiode Retrievals of Nighttime LED‐Induced Chlorophyll Fluorescence (LEDIF) for Tracking Photosynthetic Phenology in a Vineyard

AbstractThe magnitude of chlorophyll fluorescence emission represents both chlorophyll content and energy quenching processes enabling its application to serve as a proxy of photosynthetic activity. Thus, there is interest in advancing methods for canopy‐scale monitoring of chlorophyll fluorescence. Remotely sensed solar‐induced fluorescence (SIF) retrievals offer daytime monitoring of chlorophyll fluorescence, which can serve as an indicator of photosynthesis. However, it represents an instantaneous measurement during the day, which is strongly influenced by incoming radiation, solar angle, and sun/shade fraction—making it difficult to tease out baseline information on plant health and potential photosynthetic capacity—which could be tracked by changes in fluorescence yield (independent of sunlight). Recent advances have demonstrated the potential for inducing nighttime chlorophyll fluorescence via LED light sources at the canopy‐scale, which can be retrieved as LED‐induced chlorophyll fluorescence (LEDIF), potentially serving as a baseline indicator of plant health and photochemical capacity, independent of daytime conditions. In this study, we explored two methods of LEDIF retrievals: (a) hyperspectral sensor (1.33 nm full‐width half max) and (b) low‐cost Red‐Far‐Red photodiode sensor. LEDIF retrieved by the hyperspectral sensor demonstrated strong correlations with daytime SIF and gross primary productivity during mid to end of season phenology (R2 > 0.70). In contrast, phenological dynamics of LEDIF retrieved by the photodiode sensor was more subtle, likely due to weaker signal‐to‐noise ratio, but still demonstrated some potential. Overall, LEDIF offers a technique to monitor nighttime chlorophyll fluorescence emissions (and changes in its spectral shape with a hyperspectral sensor) to assess canopy‐scale phenology of photosynthetic potential.

HIGH-TEMPERATURE PROCESSES IN POWDER MATERIALS AT HIGH-VOLTAGE ELECTRIC PULSE CONSOLIDATION

The main features of high-voltage electric pulse consolidation (HVC) of refractory powder materials and the resulting unique capabilities of the method are considered. The electro-thermal processes of HVC at the contacts between powder particles and at the macroscale of the entire consolidated sample are analyzed. The results of experimental studies of the parameters of high-voltage electrical impulse action in the processes of consolidation of high-temperature powder compositions, high-voltage welding of dissimilar materials, as well as high-voltage discharges in liquid are presented. The results of measuring the intensity of thermal radiation of the investigated materials under high-voltage electrical impulse action, recorded by the method of pulse photometry using photodiode sensors, which, together with the Rogowski coil, are components of the measuring complex developed by the authors, are presented.

Optimalisasi Alat Monitoring Tetes Infus Kristaloid Berbasis Mikrokontroler ATMega328

In the medical world, supervision of intravenous devices is a priority and the main one to ensure proper and measurable fluid delivery to patients. This research focuses on the development of a crystalloid infusion drip monitoring device using a microcontroller. The background of this research arises from the need for a device that is able to monitor drip infusions automatically, reduce the workload of medical personnel and increase patient safety. The methods used in this study include designing, manufacturing, and testing on tools. Photodiode sensors are selected as the main component in detecting drops of crystalloid liquid. This tool is designed to optimize other supporting sensors to be integrated as instruction modules in counting infusion fluid drops. The application of microcontroller technology in affordable infusion monitoring devices has not been widely adopted in medical practice. This research is driven by the need for reliable monitoring tools in a fast-paced medical environment and requires precision. The results of this study showed that the developed tool succeeded in optimizing the function of the photodiode sensor in counting drops of 99% crystalloid liquid produced accurately. This tool is able to operate in accordance with its original purpose, namely as a monitoring tool as well as a reminder for patient safety. The conclusion is that this microcontroller-based crystalloid infusion drip monitoring device is an innovation and good practice in the medical field. This tool not only increases efficiency in infusion monitoring but also provides additional safety for patients.

Synthesis of signal formation and processing blocks of diffuse light reflection sensor using FPGA PSoC5 microcontroller

The problem of partial implementing the functionality of the electronic blocks for forming and processing the diffuse light reflection sensor's signal using an FPGA-microcontroller PSoC5 is solved. The topicality of this problem is caused by necessity of precision and high resolution measurement of the difference between, on the one hand, the reference signal of light reflection from the clear sensor's working surface and, on the other hand, the signal of diffuse light reflection from the inspected corroded surface. For this purpose, in particular, a channels' multiplexer, measuring and reference operational amplifiers of the second stage, a delta-sigma ADC with differential inputs, as well as a PWM control and synchronization scheme are implemented on the built into microcontroller FPGA consisting of universal digital blocks (UDB) together with programmed logical devices (PLD). The only components implemented outside the FPGA-microcontroller are the first stage operational amplifiers (in amount of sixteen), the current pulses generator for LED, as well as power supply block. The combination of components synthesized on FPGA with a powerful 32-bit Arm Cortex-M3 core makes it possible to ensure flexibility of settings, to minimize the influence of dark currents of sensitive elements of the sensor's photodiode linear array as well as noises, to align programmatically the sensitivity of the measuring channels and to carry out sensor calibration. Thus, noise mitigation is implemented programmatically by ADC's data collection and averaging in measurement buffer or calibration buffer (for reference signal from clear working surface). Several software routines for mode selection and settings, channels scanning (including measurement and calibration with alignment), as well as for measurement data (difference between the measurement and calibration buffers) displaying and transfer to PC (via one of onboard communication devices) have been developed. These all features can allow implementing the algorithms for deep analysis of measurement data and for calculation of the characteristics of surface corrosion defects.

CLET: Computation of Latencies in Event-related potential Triggers using photodiode on virtual reality apparatuses.

To investigate event-related activity in human brain dynamics as measured with EEG, triggers must be incorporated to indicate the onset of events in the experimental protocol. Such triggers allow for the extraction of ERP, i.e., systematic electrophysiological responses to internal or external stimuli that must be extracted from the ongoing oscillatory activity by averaging several trials containing similar events. Due to the technical setup with separate hardware sending and recording triggers, the recorded data commonly involves latency differences between the transmitted and received triggers. The computation of these latencies is critical for shifting the epochs with respect to the triggers sent. Otherwise, timing differences can lead to a misinterpretation of the resulting ERPs. This study presents a methodical approach for the CLET using a photodiode on a non-immersive VR (i.e., LED screen) and an immersive VR (i.e., HMD). Two sets of algorithms are proposed to analyze the photodiode data. The experiment designed for this study involved the synchronization of EEG, EMG, PPG, photodiode sensors, and ten 3D MoCap cameras with a VR presentation platform (Unity). The average latency computed for LED screen data for a set of white and black stimuli was 121.98 ± 8.71 ms and 121.66 ± 8.80 ms, respectively. In contrast, the average latency computed for HMD data for the white and black stimuli sets was 82.80 ± 7.63 ms and 69.82 ± 5.52 ms. The codes for CLET and analysis, along with datasets, tables, and a tutorial video for using the codes, have been made publicly available.

Determination of momentum after reflection in free fall using infrared and vibration sensors

Collision events are often encountered in daily life, such as free-falling balls. The falling ball will cause momentum, which is influenced by the speed and mass of the object. When an object falls, its speed before and after colliding with the reflecting plane will determine the coefficient of restitution (e). In this study, an apparatus for physics experiment was made using a 110 mm diameter by 125 cm height PVC cylinder pipe with two photodiode sensors attached to detect light reflection on the falling object. A vibration sensor was installed on the base to detect the bouncing ball and authenticate the time measurement. Sensor readings and measurements occur once an automated actuator triggers the sensor. The experimental results showed that the ball falling on the aluminum reflecting plane experienced the longest oscillation with a time of 4.19 Seconds, followed by ceramic at 3.59 seconds and wood at 3.29 seconds. The contact time experienced by the ball is 0.8487 seconds on aluminum, 0.8093 seconds on ceramic and 0.7830 on wood. This experimental apparatus can help students understand physics material.

Localized keyhole pore prediction during laser powder bed fusion via multimodal process monitoring and X-ray radiography

Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a long-standing objective for system manufacturers and researchers in the additive manufacturing (AM) industry. This manuscript investigates a data fusion approach for detection of keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates by concurrent recording of thermally induced optical emission measured using both off-axis and coaxial photodiode sensors, and acoustic emission. Subsurface defect formation was monitored via high-speed synchrotron X-ray imaging at 20,000 frames per second, enabling temporal registration of keyhole pore formation events to the monitoring signals at a resolution of 50 µs. We developed data fusion machine learning (ML) models for localized prediction of keyhole pore formation at various time scales ranging from 0.5 ms to 2 ms. The signal segments were featurized using two independent approaches: (1) power spectral density (PSD) and (2) highly comparative time series analysis (HCTSA) framework. The extracted features from different sensor modalities were fused together to construct a multimodal feature space and sequential feature selection was used to determine the most informative features for training the ML models. The predictive performance was evaluated for three classifying algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Naive Bayes (GNB). As a result, pore formation events were predicted with up to 0.95 F1-score, 1.0 recall and 0.94 accuracy. The most heavily weighted features indicate that model performance is chiefly governed by the acoustic monitoring signal, with a secondary contribution from the optical emission sensors.

Accuracy of Infrared Photodiode Sensors at The Flowrate Measurement in Infusion Device Analyzer with 2 Channel TFT Display

The use of infusion is crucial for patient healing. Infusion refers to a fluid that consists of drugs, nutrients, and hydration delivered continuously into the patient's bloodstream over a specific period. One of the types of infusion devices is the infusion pump and syringe pump. These devices play a vital role in accurately and precisely controlling the volume or flow rate of fluids. However, continuous usage of these devices can sometimes result in inaccurate measurements, which can affect their overall accuracy. The accuracy of these devices is crucial for proper dosage administration to patients, particularly in critical situations. Therefore, it is necessary to periodically calibrate healthcare devices, at least once a year, as specified in Ministry of Health Regulation No. 54 of 2015. Calibration is an activity performed to determine the true value of a device. The objective of this study is to develop an Infusion Device Analyzer (IDA) with a TFT LCD display that showcases graphical representations of flow rate parameters. By analyzing the calculation of flow rate values using Infrared Photodiode sensors, the stability of the flow rate graph can be observed on a 7-inch TFT LCD display. The measurement involved the use of two different brands of syringe pumps and two different brands of infusion pumps. The results were presented in real-time on the 7-inch TFT LCD display, both in graphical and numerical formats. Additionally, the data was transmitted via Bluetooth to a PC, allowing the graph to be simultaneously displayed in a Delphi program.The measurement results revealed performance errors when using the Terumo Syringe Pump with Terumo syringes in Channel 1, with values of 0.45% (10 ml/h), 0.72% (50 ml/h), and 0.40% (100 ml/h). In Channel 2, the errors were 0.32% (10 ml/h), 0.40% (50 ml/h), and 0.32% (100 ml/h). When using the B-Braun Syringe Pump with B-Braun syringes, Channel 1 exhibited errors of 0.45% (10 ml/h), 0.7% (50 ml/h), and 0.85% (100 ml/h), while Channel 2 had errors of 0.8% (10 ml/h), 0.3% (50 ml/h), and 1% (100 ml/h). In the case of the Terumo Infusion Pump with Terumo Infusion Sets, Channel 1 showed errors of 0.4% (10 ml/h), 0.5% (50 ml/h), and 0.45% (100 ml/h), and Channel 2 exhibited errors of 0.32% (10 ml/h), 0.4% (50 ml/h), and 0.72% (100 ml/h). Lastly, when using the B-Braun Infusion Pump with B-Braun Infusion Sets, Channel 1 had errors of 0.72% (10 ml/h), 1% (50 ml/h), and 1,2% (100 ml/h), while Channel 2 displayed errors of 0.8% (10 ml/h), 0.72% (50 ml/h), and 0,4% (100 ml/h).INDEX TERMS Infrared Photodiode Sensor, Calibration, Real Time, Flow Rate.

Line Tracer Robot Navigation System Using Arduino Uno Microntroller With PID Control

This Study aims to develop a line tracer using Arduino Uno the PID control method. Line tracer robot is a type of robot is a type of robot capable of following a specified path. The PID (Proportional-Integral-Derivative) control used to keep the robot on track by correcting the difference between the actual and the desired position. In this study, 6 photo diode sensors were used, the motor driver used the L293D, and the microcontroller used Arduino Uno. Testing the response of the photo diode sensor had the smallest voltage value at 1,219 Vdc and the largest voltage at 4,875 the result of the motor driver measurements all succeeded in stopping, ccw or cw. The end of testing the line tracer robot using PID control is able to navigate properly without getting of the track.
 Highlights:
 
 Precise Path Following: PID control ensures accurate tracking by correcting position deviations.
 Sensor Response Evaluation: Photo diode sensor testing reveals voltage range (1.219 Vdc to 4.875 V) for effective line detection.
 Motor Driver Proficiency: L293D motor driver successfully controls motor functions (stop, ccw, cw) for optimal movement.
 
 Keywords : Robot, Line Tracer, PID, Arduino Uno