Stepper Motor Research Articles

Speed control of stepper motors for use in laboratory animal devices

Speed control of stepper motors for use in laboratory animal devices

Design and control of an automatic cable-cutting machine with the implementation of a stepper control program using PLC

Proper control design will reduce energy losses and production costs for the industry. Control system design also reduces the role of humans in production. An automatic cable-cutting control system can reduce cable manufacturing time so that a company's human resources do not need to carry out the cable-cutting process manually. This cable cutter uses a PLC for its central control, which will convert the length value into the number of pulses and calculate the number of cables to be cut. The HMI inputs the length of the cable to be cut and the number of cables to be missed. The stepper motor is used to run the cable-pulling mechanic, which will pull according to the size stated on the HMI. This system can cut AWG20 and AWG16 cables in various lengths from 50 mm to 3000 mm with the number or number of wires that can be set from 1 cut to 10 cuts for each size.

Development and Evaluation of Bluetooth based Remote Controlled Battery Powered Drum Seeder

A Bluetooth based battery-powered remote-controlled direct rice seeder was developed to overcome the drudgery and operational difficulties involved in direct seeding by manual drum seeder. The developed seeder consists of micro-controller unit, stepper motor, DC geared motor, motor drivers, battery, wheels and frame. The micro-controller unit is the heart of the developed mechatronic system. The micro-controller unit was designed and developed by Programming. A mobile application was designed and developed to connect the seeder to mobile through Bluetooth connectivity and operated range of 47.6 m which is sufficient for hilly region topography. The developed seeder was evaluated in laboratory conditions at different forward speeds varying from 0.5 to 3.0 km.h−1 and hopper fill levels varying from 25% to 75%. Forward speed of 0.7 km.h−1 and 75% hopper fill level were found optimum with corresponding seed rate of 20.2 kg. ha−1, missing hill index of 0%, hill spacing uniformity of 93.7%, coefficient of variance of number of seeds per hill of 0.25 and hill span of 9.4 cm. The developed seeder was evaluated in the field in puddled soil conditions in Sajong village, East Sikkim, India and the performance was compared with manual drum seeder. The field capacity and field efficiency of the developed seeder was found to be 0.017 ha.h−1 and 62% respectively as compared to 0.023 ha.h−1and 82% in case of manual drum seeder respectively. The developed seeder has reduced drudgery/cumbersome of 64% over manual drum seeder as evident from operator's heart rate and energy expenditure rate. In addition to that the operator can able to sit in a safe and comfortable environment outside the field to operate the developed drum seeder. Moreover, the developed seeder reduces the operational cost of 230 ₹. ha−1 over manual drum seeder.

Development of a Low-Cost Artificial Vision System as an Alternative for the Automatic Classification of Persian Lemon: Prototype Test Simulation.

In the present research work, an algorithm of artificial neural network (ANN) has been developed based on the processing of digital images of Persian lemons with the aim of optimizing the quality control of the product. For this purpose, the physical properties (weight, thickness of the peel, diameter, length, and color) of 90 lemons selected from the company Esperanza de San José Ornelas SPR de RL (Jalisco, Mexico) were studied, which were divided into three groups (Category "extra", Category I, and Category II) according to their characteristics. The parameters of weight (26.50 ± 3.00 g), diameter/length (0.92 ± 0.08) and thickness of the peel (1.50 ± 0.29 mm) did not present significant differences between groups. On the other hand, the color (determined by the RGB and HSV models) presents statistically significant changes between groups. Due to the above, the proposed ANN correctly classifies 96.60% of the data obtained for each of the groups studied. Once the ANN was trained, its application was tested in an automatic classification process. For this purpose, a prototype based on the operation of a stepper motor was simulated using Simulink from Matlab, which is connected to three ideal switches powered by three variable pulse generators that receive the information from an ANN and provide the corresponding signal for the motor to turn to a specific position. Manual classification is a process that requires expert personnel and is prone to human error. The scientific development presented shows an alternative for the automation of the process using low-cost computational tools as a potential alternative.

A Universal Volumetric Haptic Actuation Platform

In this paper, we report a method of implementing a universal volumetric haptic actuation platform which can be adapted to fit a wide variety of visual displays with flat surfaces. This platform aims to enable the simulation of the 3D features of input interfaces. This goal is achieved using four readily available stepper motors in a diagonal cross configuration with which we can quickly change the position of a surface in a manner that can render these volumetric features. In our research, we use a Microsoft Surface Go tablet placed on the haptic enhancement actuation platform to replicate the exploratory features of virtual keyboard keycaps displayed on the touchscreen. We ask seven participants to explore the surface of a virtual keypad comprised of 12 keycaps. As a second task, random key positions are announced one at a time, which the participant is expected to locate. These experiments are used to understand how and with what fidelity the volumetric feedback could improve performance (detection time, track length, and error rate) of detecting the specific keycaps location with haptic feedback and in the absence of visual feedback. Participants complete the tasks with great success (p < 0.05). In addition, their ability to feel convex keycaps is confirmed within the subjective comments.

Real-Time Optical Detection of Artificial Coating Defects in PBF-LB/P Using a Low-Cost Camera Solution and Convolutional Neural Networks

Additive manufacturing plays a decisive role in the field of industrial manufacturing in a wide range of application areas today. However, process monitoring, and especially the real-time detection of defects, is still an area where there is a lot of potential for improvement. High defect rates should be avoided in order to save costs and shorten product development times. Most of the time, effective process controls fail because of the given process parameters, such as high process temperatures in a laser-based powder bed fusion, or simply because of the very cost-intensive measuring equipment. This paper proposes a novel approach for the real-time and high-efficiency detection of coating defects on the powder bed surface during the powder bed fusion of polyamide (PBF-LB/P/PA12) by using a low-cost RGB camera system and image recognition via convolutional neural networks (CNN). The use of a CNN enables the automated detection and segmentation of objects by learning the spatial hierarchies of features from low to high-level patterns. Artificial coating defects were successfully induced in a reproducible and sustainable way via an experimental mechanical setup mounted on the coating blade, allowing the in-process simulation of particle drag, part shifting, and powder contamination. The intensity of the defect could be continuously varied using stepper motors. A low-cost camera was used to record several build processes with different part geometries. Installing the camera inside the machine allows the entire powder bed to be captured without distortion at the best possible angle for evaluation using CNN. After several training and tuning iterations of the custom CNN architecture, the accuracy, precision, and recall consistently reached >99%. Even defects that resembled the geometry of components were correctly classified. Subsequent gradient-weighted class activation mapping (Grad-CAM) analysis confirmed the classification results.

Actively Minimize Rocket Roll-Rate During the Coast Phase

In the 2021-2022 Collegiate Rocket Competition, participating teams were tasked with the development and implementation of an active roll-control system onboard their rocket. To reduce the rotation of an object, the principal of conservation of momentum was applied by adjusting the angular momentum of a mass inside the object. This principle was implemented in the Phantastic Phoenom’s 2022 rocket to minimize the roll-rate during the coast phase of flight. The components utilized to accomplish this include a three-inch steel tube, Raven4 altimeter, six-axis accelerometer, Arduino Nano microcontroller, and a stepper motor. The mass housed inside the rocket was reduced by 40% to achieve the weight needed to reach the predicted apogee. Despite several unforeseen challenges the day of the competition launch, the rocket completed a successful launch. The Phantastic Phoenoms are proud to be able to win third place in this competition.

ANALYZING A THREE HUNDRED TEETH BI-PHASE HYBRID STEPPER MOTOR WITH DIFFERENT NUMBERS OF POLE PAIRS

The paper presents numerical models of a bi-phase, 0.3° full step hybrid stepper motor (HSM), with 16, 24, 32, 40, and, respectively, 48 poles in the stator. For the motor to move with a constant step angle, the stator and the rotor teeth must be shifted against each other with an angle of zero, 90, 180, and 270 electric degrees for each four consecutive stator poles. Due to this, bi-phase steppers with the number of poles in the stator different than eight are hardly seen. Therefore, to allow the construction of HSMs with different numbers of poles, some of the stator poles must be shifted from their symmetrical position. The five topologies' HSM detent and holding torque characteristics and their geometrical and constructive differences are presented. The Finite Element Method (FEM) based models are developed using the professional software COMSOL Multiphysics.

Realization of combined dynamic motion mode for roller forming unit

The combined dynamic mode of the reciprocating motion for a forming trolley is calculated to increase the reliability and durability of the roller forming unit. The criterion action is used as a criterion of the motion mode, which is an integral over time with the sub-integral function that expresses the acceleration «energy» when calculating the combined dynamic motion mode of the forming trolley in the acceleration and braking areas. The change functions of the kinematic characteristics of the forming trolley during its motion from one extreme position to another, which correspond to the combined dynamic mode of the reciprocating motion, are calculated. The displacement and speed of the forming trolley in the acceleration and braking areas change smoothly with this motion mode, without creating significant dynamic loads in the unit, which in turn has a positive effect on its durability.
 The variation law of the compaction rollers angular velocity is calculated by taking into account the change functions of the forming trolley speed. The roller forming unit design with a drive from the high-torque stepper motor is proposed, which is mounted in the compaction rollers of the forming trolley and provides the combined dynamic mode of reciprocating motion for the forming trolley. The surface quality of the processed concrete mixture is increased, dynamic loads in the drive mechanism elements are reduced, unnecessary destructive loads on the frame construction are disappeared and, accordingly, the reliability and durability of the unit as a whole are increased, when we use the such drive in the unit.

The effect of model uncertainties in the reinforcement learning based regulation problem: An experimental case study with inverted pendulum

AbstractThis work aims to improve the classical white‐box model of an inverted pendulum in order to reach a more accurate representation of an actual pendulum on a cart system. The purpose of the model is to train different controllers based on machine learning algorithms. In the context of this paper, the inverted pendulum system is driven by a belt drive that is controlled by a stepper motor. Due to the nature of the controller, the input to the stepper motor is in the form of a non‐smooth bang‐bang‐like signal that moves the cart to the left, right, or terminates its movement. One of the main challenges, in this case, is to find a proper function to model the stepper motor as its dynamics cannot be captured with a constant gain. It has been shown that the transient behavior of the stepper motor when changing direction or stopping is not negligible in the closed‐loop control performance. Accordingly, a grey‐box scheme, which accounts for the uncertainties that are not included in the vanilla white‐box model, is utilized to achieve a lower model mismatch compared to the actual pendulum. Initially, the equation of motion was derived using the Euler‐Lagrange equation with force on the cart as the control input. But in the real‐time experiment, the interface is realized by the stepper motor's frequency modulator, hence a transfer function representing the relationship between the frequency and the force applied on the cart (in the model) is calculated as a black‐box model. To improve the accuracy of the transfer function, an experimental data‐driven design of this function is performed based on modern schemes in system identification. For this purpose, the applied frequency to the stepper motor and the states from the actual system are recorded. Then, the applied force on the cart is calculated using the equation of motion and the recorded states. It is also shown that the frequency‐force transfer function uncertainty due to exogenous disturbances is non‐negligible and with the aim of having a more accurate model, an artificial neural network is introduced. Finally, the effectiveness of this grey‐box model is shown by training and implementing a deep Q‐network based controller to swing up and balance the inverted pendulum.

Development of a user‐friendly automatic ground‐based imaging platform for precise estimation of plant phenotypes in field crops

AbstractPlant phenotyping is the science to quantify the quality, photosynthesis, development, growth, and biomass productivity of different crop plants. In the past, plant phenotyping employed methods such as grid count and regression models. However, the grid count method proved to be labor‐intensive and time‐consuming, while the regression model lacked accuracy in calculating leaf area. To address these challenges, a portable automatic platform was developed for precise ground‐based imaging of field plots. This platform consisted of a frame, an RGB camera, a stepper motor, a control board, and a battery. The RGB camera captured images, which were then processed using MATLAB software. Statistical analysis was performed to compare the results obtained from the grid count, regression model, and image processing techniques. The correlation coefficient (r) between the image processing technique and the regression model for sunflower was found to be 0.98 and 0.97, respectively, whereas for kidney bean it was 0.99 and 0.96, respectively. The minimum and maximum values for leaf area density (LAD) of all selected sunflower leaves were determined to be 0.132 and 0.714 m²/m³, respectively. For kidney bean leaves, the minimum and maximum mean LAD values were found to be 0.081 and 0.239 m²/m³, respectively. Ergonomic aspects of the developed automatic system were studied. The developed system had lower physiological parameters, such as working heart rate of 99 beats/min, work pulse of 18 beats/min, oxygen consumption of 786 mL/min, and energy consumption of 11.5 kJ/min compared to the grid count method. Thus, developed automatic ground‐based imaging system would significantly reduce physiological workload and associated hazards. Therefore, the developed method proved satisfactory in comparison to other techniques, offering a quick, efficient, and user‐friendly approach for determining plant phenotypes.

H2-gas diffusion in porous media as observed by NMR

Not only is hydrogen (H2) the simplest and most abundant gas in our universe, it also plays a pivotal role in the decarbonization of our energy. As such hydrogen will be one of the most important critical enablers for industry in the global energy transition. Hydrogen’s low density hinders the wide-scale deployment and therefore, it is important to come to large scale low-cost H2 storage capacity as a source of hydrogen gas. In order, to store the energy in the TW h/GW h-range subsurface storage of hydrogen in depleted hydrocarbon reservoir and/or deep saline aquifers is needed. Concerning the hydrogen storage in depleted gas/oil reservoirs there are still many scientific questions open, which have to be addressed in order to come to reliable, efficient and cost-effective storage [1, 2]. For example, the injected hydrogen will displace the pore residual oil/gas and spread out in a reservoir; a trap structure is needed to prevent the hydrogen from escaping and allowing reproduction of the hydrogen, i.e., leakage is a major concern. Accurate predictions are needed of multi-phase fluid displacement in porous media, e.g., the interaction/displacement of cushion gas and the interactions with the solid matrix.
 In this study we have used NMR to image the diffusion and interaction in porous media. NMR is one the few methods by which H2-gas can be directly imaged in porous media. In order to do so, we have constructed a special NMR setup to measure the diffusion in porous media up to 60 bar. As the relaxation times measured for hydrogen are too short for 3D imaging this setup is limited to 1D measurement [3]. As, to be able to measure also on real porous media containing magnetic impurities, it was chosen to keep the main field at 0.7 T. The present setup is made up of an electromagnet generating a main magnetic field of 0.7 T with a gap of 70 mm and a picture is given in Fig 1. The NMR setup itself is equipped with Anderson gradient coils which can generate up to 0.3 T/m. An RF insert was made which is equipped with a special Faraday-shield, to be able to perform quantitative measurements and as such this setup is directed towards quantitative H2 profile measurements [4].
 For the NMR measurements a special PEEK reactor was purchased, which can be operated on up to 60 bars. The PEEK reactor has an outer diameter of 50 mm and inner diameter of 30 mm with a length of 500 mm. The reactor can be moved through the RF-coil with the help of a stepper motor, hence giving the possibility to measure the H2 content profile over the length of the reactor.
 In the initial tests we looked at the quantitative measurement of H2 as a function of the gas pressure. These initial measurements show that the H2 gas has a T1 in the order of 0.5 ms. Hence, as the signal of the H2 gas is quite low in comparison to the background noise, it was decided to measure the H2 gas at a high repetition rate, i.e., TR=2 ms. The results as function of the gas pressure up in the initial test upto 15 bars are given in Fig 2. As can be seen a clear linear behavior is found and only a minor contribution of the background is observed. In the next experiment the reactor was filled with 0.06 mm glass beads to mimic an ideal porous material and the results are also given in Fig 2. Again, a clear linear behavior can be observed, indicating measurements can also be performed in porous media.
 After this preliminary study in which we have tested the setup, we now intend to conduct measurements on porous glass beads up to 2 mm under various prewetting conditions, e.g., water, NaCl solution, oil and at various pressures. Here we want to measure the exchange of H2 gas by other gases such as N2 up to 60 bar and see the influence of the various prewetting conditions.

Design of a Vision Based Autonomous Turret

This article describes the hardware and software design of a vision-based autonomous turret system. A two degree of freedom (2 DOF) turret platform is designed to carry a canon equipped with an embedded camera and actuated by stepper motors or direct current motors. The turret system includes a central calculator running a visual detection and tracking solution, and a microcontroller, responsible for actuators control. The Tracking-Learning-Detection (TLD) algorithm is implemented for target detection and tracking. Furthermore, a Kalman filter algorithm is implemented to continue the tracking in case of occlusion. The performances of the designed turret, regarding response time, accuracy and the execution time of its main tasks, are evaluated. In addition, an experimental scenario was performed for real-time autonomous detection and tracking of a moving target.

An Optimization Scheme and Circuit for Driving a Stepper Motor with the Index Control Mode

An optimized control scheme for driving a stepper motor with the index control mode is proposed and analyzed in this paper to control the stepper motor more accurately and effectively. Based on the built-in five-digit digital-to-analog signal converter (DAC), six stepper motor operating modes, namely 1/32 micro steps, 1/16 micro steps, 1/8 micro steps, 1/4 step, 1/2 step, and full step, can be selected. Only four control ports are required for switching of operating mode and motor steering. The simulation results show the optimized circuit can output accurate current signals to control the stepper motor.

Simulation of active vibration control of a moving stage

Active vibration control (AVC) has gained considerable interest due to its inherent adaptability and cost-effectiveness, with its ability to suppress vibrations in the controlled system across various applications. Existing studies focus on the AVC of non-moving systems and usually assume that the vibration signal to be controlled is known and can serve as the ideal reference signal in feedforward control systems. However, in many practical applications, the ideal reference signal is not accessible, and a sensor must be used to detect the reference signal. This can degrade the AVC performance, especially for a moving system. This study, therefore, aims to explore the potential application of piezoelectric stack actuators (PSA) to control the vibration of a moving stage driven by a stepper motor. The secondary path was estimated offline, and vibration data were collected at different moving speeds. The effects of the location of the reference sensor were initially investigated. Then, extensive simulations were conducted to evaluate the performance of different adaptive control algorithms regarding vibration reduction, convergence speed, computational complexities, etc. This research underlines the potential of integrating PSA within a moving system to effectively control its vibration.

Design of Charge Pump Circuit for Stepper Motor Drive Chip

A charge pump circuit structure that can be applied to high-voltage drive circuits such as motor driver chips is proposed. The pump mode of the traditional circuit is changed based on the traditional charge pump, the output frequency of the oscillator is increased, and the capacitance area of the pump is reduced. The design and simulation of the proposed charge pump circuit are carried out based on HHG 0.35 um BCD technology. The simulation results show that when the simulation temperature is 27 °C and the clock cycle is set to 1 us, the voltageVin+5V can be stably output after 923 us, and the on-load current can reach 0.2 mA. The results meet the design requirements and provide a new way to solve this kind of problem.

Data-Driven Inverse Kinematics Approximation of a Delta Robot with Stepper Motors

The Delta robot is a parallel robot that is over-actuated and has a highly nonlinear dynamic model, which poses a significant challenge to its control design. The inverse kinematics that maps the motor angles to the position of the end effector is highly nonlinear and extremely important for the control design of the Delta robot. It has been experimentally shown that geometry-based inverse kinematics is not accurate enough to capture the dynamics of the Delta robot due to manufacturing component errors, measurement errors, joint flexibility, backlash, friction, etc. To address this issue, we propose a neural network model to approximate the inverse kinematics of the Delta robot with stepper motors. The neural network model is trained with randomly sampled experimental data and implemented on the hardware in an open-loop control for trajectory tracking. Extensive experimental results show that the neural network model achieves excellent performance in terms of the trajectory tracking of the Delta robot under different operation conditions, and outperforms the geometry-based inverse kinematics model. A critical numerical observation indicates that neural networks trained with the specific trajectory data fall short of anticipated performance due to a lack of data. Conversely, neural networks trained on random experimental data capture the rich dynamics of the Delta robot and are quite robust to model uncertainties compared to geometry-based inverse kinematics.

Hot wire thermopol cutting using CNC machine

Recently, the Hot Wire CNC Technology is use for the purpose of cutting of the materials like thermopol and foam for the packaging of products. This technology saves labor costs and increases production. The main objective of the research is to determine the influence of wire temperature on the quality of cuts achieved through hot wire cutting technology and explore the optimal material combination for low power consumption in wire cutting technology. The technique utilizes controlling the current regulation of the Nichrome wire, to reduce the power consumption. The Hot Wire CNC Machine is controlled by a micro controller which sends a special code called G-Code to the stepper motors and the current regulation is controlled by PWM (Pulse Width Modulation) through Power transistor. The results of the study show a sufficient reduction in power consumption when using the combination of Nickel-chromium materials.

Self-Adjusting Optical Systems Based on Reinforcement Learning

Progress in the field of machine learning has enhanced the development of self-adjusting optical systems capable of autonomously adapting to changing environmental conditions. This study demonstrates the concept of self-adjusting optical systems and presents a new approach based on reinforcement learning methods. We integrated reinforcement learning algorithms into the setup for tuning the laser radiation into the fiber, as well as into the complex for controlling the laser-plasma source. That reduced the dispersion of the generated X-ray signal by 2–3 times through automatic adjustment of the position of the rotating copper target and completely eliminated the linear trend arising from the ablation of the target surface. The adjustment of the system was performed based on feedback signals obtained from the spectrometer, and the movement of the target was achieved using a neural network-controlled stepper motor. As feedback, the second harmonic of femtosecond laser radiation was used, the intensity of which has a square root dependence on the X-ray yield. The developed machine learning methodology allows the considered systems to optimize their performance and adapt in real time, leading to increased efficiency, accuracy, and reliability.

ALAT PROYEK MIKRO KONTROL PENGHAPUS PAPAN TULIS OTOMATIS MENGGUNAKAN REMOTE BERBASIS ARDUINO

The development of automation technology on blackboard erasers can increase the level of hygiene and health, because with an automation system the activity of erasing the blackboard can be carried out automatically without inhaling marker ink which has an impact on health and does not dirty hands. The purpose and working mechanism of making an automatic blackboard eraser using a remote and Arduino. The method of designing an automatic whiteboard eraser is made using an arduino uno as a microcontroller and an infrared remote that controls a stepper motor to drive the eraser. The assembly of the blackboard eraser mechanism is done by preparing a blackboard prototype that has been mounted on an iron frame. Then, install the top rail and bottom rail. Followed by installing the upper and lower wheels on the upper and lower rails that have been glued to the eraser clamp. The results of the design of an automatic whiteboard eraser based on Arduino Uno, can facilitate teachers and students in the learning process. The conclusion of this design is that the automatic whiteboard eraser can work according to the program that has been entered into the arduino uno and is controlled by an infrared remote and the whiteboard eraser working mechanism functions properly to erase the marker ink on the blackboard with a power of 26.67 watts