Machine Control Research Articles

Enhancing Cloud Computing Efficiency with Crocodile Optimization Algorithm: A Novel Approach to Distributed Workload and VM Management

Cloud computing has introduced itself as a mighty mechanism for delivering customers through the service model with on-demand, scalable, and instant access to computer resources. It will conduct effective load balancing and resource management, high importance so that the cloud system works with optimized performance and resource utilization. This gives a new strategy in load balancing and virtual machine (VM) control in cloud computing applied in the field using the Crocodile Optimization Algorithm (COA) for better performance. Inspired by crocodile hunting behaviors, the COA-based strategy is adopted to balance loads and manage VMs. This approach seeks to balance the number of the workload given to VMs with respect to the processing power of VMs and also the distribution of workload. It best uses resources in such a way that tasks are dynamically distributed to VMs in such a way that response time is at its minimum, and thus overall efficiency is enhanced in cloud computing. On the other hand, COA-based load balancing incorporates VM management techniques like migration and scaling to be adjustable in relation to the changing conditions of the workload. This allows dynamically adjusting the allocation of resources with respect to current demands, in such a way that assures optimal utilization of computational resources with high performance. The proposed approach was evaluated using simulations through CloudSim, one of the most adopted tools for simulating cloud computing. The COA effectively works are divided between the VM, which in turn will lead to better response time for the user request and improve cloud resource utilization. That is to mean, subsequent research would be some type of unique attempt in the area of load balancing and VM management in cloud computing, based on the Crocodile Optimization Algorithm. This approach improves efficient cloud computing through the balancing of load distribution, maximization of resource utilization, and lowering of response time.

Optimization signal writing with machine learning assisted control

The high-precision signal writing machine, experiencing a 0.1% failure rate due to discrete fourier transform (DFT) of position error signal (PES) exceeding control limits, can be improved with an appropriate controller gain. This paper combines machine learning (ML) classification and controller optimization to determine the suitable gain for the hard disk drive (HDD) signal writing process. The result from machine classification has a high potential for position error improvement, distinguishing them from those with obvious degradation. The identified machine classes with high potential for signal write quality improvement undergo controller optimization using a genetic algorithm (GA). The objective function considers gain crossover frequency, phase margin, and PES DFT at low frequencies. Experimental results demonstrate that the new controller gain enhances signal write quality of class 0 and class 3 by 14.68% and 17.18%, respectively, leading to a reduced failure rate down to 0.05%.

Enhancing Manufacturing Precision: Leveraging Motor Currents Data of Computer Numerical Control Machines for Geometrical Accuracy Prediction Through Machine Learning.

Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time. Different machine learning algorithms, such as Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT), were used for predictive modeling. Feature extraction was performed using Tsfresh and ROCKET, which allowed us to capture the patterns in the motor current data corresponding to the geometric features of the machined parts. Our predictive models were trained and validated on a dataset that included motor current readings and corresponding geometric measurements of a mounting rail later used in an engine block. The results showed that the proposed approach enabled the prediction of three geometric features of the mounting rail with an accuracy (MAPE) below 0.61% during the learning phase and 0.64% during the testing phase. These results suggest that our method could reduce the need for post-machining inspections and measurements, thereby reducing production time and costs while maintaining required quality standards.

Ultrafine industrial aerosol as an occupational risk factor for sintering industry workers

Aim. Assessment of the physicochemical characteristics of suspended particulate matter of the ultrafine range present in the air of the working area of the sintering factory of a metallurgical plant. Materials and methods. The assessment and analysis of the physicochemical parameters of ultrafine particles were carried out in different areas of the sintering factory of the metallurgical plant. The parameters were measured using a portable scanning spectrometer Nanoscan 3910 (USA). The chemical composition of the suspended particulate matter was determined by the method of optical emission spectrometry with inductively coupled plasma (OES-IZP) using the device “Ortima 2100 DV” (PerkinElmer, USA). Results. It was estimated that the concentration of suspended particulate matter of ultrafine size in the main part of the sintering machine ranged from 3.02 × 104 to 3.12 × 104 per cm3, in the tail part of the sintering machine – from 5.09 × 104 to 7.59 × 104 per cm3, in the sintering machine control room No. 1 – from 2.06 × 104 to 2.38 × 104 per cm3, in the workers of the control group – from 1.43 × 104 to 1.73 × 104 per cm3. The quantitative concentration of suspended particulate matter by individual sizes at all workplaces of the sinter plant workers compared to workers of the control group had a significant difference. Values of the total surface area and surface volume of suspended particulate matter of the nano-sized range had their maximum in the tail part and near the head of the sintering machine, the lowest values were recorded around the control room and among the employees of the plant management department. The highest mass concentration of ultrafine particles was recorded in the tail part of the sintering machine (22.55 μg/m3 to 508.35 μg/m3), which is associated with a significant amount of suspended particulate matter of a larger size (≥115.5 nm). The chemical composition of the particles of the ultrafine range included aluminum, calcium, iron, magnesium, manganese, silicon, and phosphorus, which is explained by the specifics of the technological process, and the exceeding of the hygienic standard was observed for calcium, silicon and phosphorus. Conclusions. It was established that during the sintering of the agglomerate in the agglomeration compartment, many suspended particles of the ultrafine range are formed. They included aluminum, iron, manganese, magnesium, silicon, phosphorus, and calcium. Significantly higher values of the number, mass concentration, surface area and surface volume of suspended particulate matter were determined at the workplaces of sintering factory workers, which had a statistically significant difference compared to the workers of the control group.

Sensitivity Analysis of MTPA Control to Angle Errors for Synchronous Reluctance Machines

This study investigated the sensitivity of maximum torque per ampere (MTPA) control in synchronous reluctance machines (SynRMs) to angle errors, examining specifically how deviations in the reference control trajectory affected performance. Analytical and numerical methods were employed to analyze this sensitivity systematically, including the impact of magnetic saturation. Two MTPA control implementation schemes were evaluated, with torque and current amplitude as the reference variables, using a template SynRM from the open-source simulation tool SyR-e. The results indicated that performance sensitivity to angle errors was moderately low near the MTPA trajectory, allowing for significant angle deviations with minimal performance loss. Although magnetic saturation increased this sensitivity slightly, reducing the allowable error range by up to 25%, the maximum angle deviation for up to 1% of the performance decrease still corresponded to approximately ±3∘ around the MTPA trajectory. The findings of this study suggest potential for simplifying control implementations, reducing component costs through less precise position determination (sensor-based or sensorless), and achieving additional control objectives such as torque ripple reduction.

Design and research of numerical control simulation platform in discrete manufacturing disturbed by vibration

It is of great significance to study the deep integration of manufacturing technology and new-generation information communication technology under vibration interference of machine tools to improve the intelligence level of CNC machine tools. In this paper, a numerical control manufacturing workshop affected by vibration in discrete manufacturing is taken as the research background, and a solution for a digital workshop operation simulation platform based on the industrial internet is proposed. By constructing the simulation environment of the operation process of the digital factory, the generation and transmission of manufacturing information in the digital factory are simulated. The application architecture of the machining workshop based on a numerical control simulation platform is proposed, and the business process of the numerical control machining workshop is analyzed. Then, the key technologies of NC machine tool modeling, synchronous mapping of data and model, data integration, and fusion are studied. Through the integration and implementation of the NC machine tool simulation platform in the machining workshop, the top-down data instructions can be issued accurately, and the bottom-up feedback information can be confirmed in time. Finally, the system is applied to the electronic information and ship machining workshop to verify the effectiveness of the system framework and method proposed in this paper.

Precise Excitation of a Switched Reluctance Machine under Regenerative Mode in High-Speed Operation

The implementation of electric machines in electric and hybrid electric vehicles offers some advantages, including the ability to operate in regenerative mode. Switched reluctance (SR) machines have been alternative solutions due to their characteristics. Operating an SR machine in propulsion mode requires high torque and minimal torque ripple, while producing optimal output power in regenerative mode is required. In high-speed operation, when the back electromotive force is greater than the DC link voltage, single pulse-based methods are often used. Torque ripple problems can be neglected due to rotor inertia. In this paper, a single pulse-based control for SR machines in high-speed operation under regenerative and discontinuous conduction modes is analyzed. Excitation to magnetize the rotor and generation process to produce output power are compared to determine the effectiveness of energy saving during regenerative mode. By using a simple control strategy, precise excitation angles can be obtained to turn the machine on and off. Experimental work has been done to support the analysis. They show that by using proper excitation angles, the optimal output power can be generated.

Soil Sharing and Equipment Operations Through Digitalization of Large-Scale Earthworks

The modern construction industry is undergoing considerable changes driven by increased specialization, technological advancements, and growing complexity. The integration of smart construction technology is rapidly advancing as a solution to address the aging workforce in the sector. However, the uncertainty and risks associated with soil construction on job sites remain, leading to increased costs during project execution. Recently, construction sites have sought to enhance productivity by leveraging building information modeling (BIM) and smart construction devices. The adoption of smart equipment, such as machine control and machine guidance, is on the rise in both structural and earthwork projects, with ongoing efforts to mitigate uncertainties. This study proposes a practical approach to reduce the uncertainty in earthworks by optimizing soil sharing strategies and equipment allocation from the initial design phase. A BIM model was developed as a solid structure and then segmented using Dynamo. This model was utilized to create a construction plan using Primavera P6, while AnyLogic (8.9.2) was employed to assess the suitability of equipment combinations, ultimately demonstrating the cost-saving benefits of the proposed approach. Through repeated simulations, work efficiency was enhanced by approximately 6.2% compared to the original 2D planning approach.

Enhancing tool life through innovative process control in wood-based machining

ABSTRACT This study demonstrated a process control strategy for extending tool life in wood-based machining. A feedback control technique regulated tool spindle speed while cutting to maximize tool performance. Machining of melamine-coated particleboard was conducted on a computer numerical control router with tungsten carbide inserts. Four cutting scenarios were applied: constant low spindle speed, constant high spindle speed, step function cutting and cutting using feedback control technique. After each test, panel chipping and tool wear were assessed to determine the effect of varying the spindle speed on tool wear and panel chipping. The findings indicated that although tool wear increased, panel chipping decreased when the spindle speed remained continuously high. A constant low spindle speed increased panel chipping and decreased tool wear. Raising the spindle speed in the step function setting reduced tool wear and panel chipping, but it was not apparent when these speed adjustments should be made. The feedback control technique greatly extended the tool life, improving surface quality. The study contributes significantly to wood-based machining procedures and greatly impacts the woodworking sector. The advantages of improved tool life and increased productivity justify the need for more research in this area.

Model Predictive Control for Six-Phase Induction Machines with Insight into Past Current Errors

Multi-phase electric drives can act as a competitive solution for high-power applications. To take advantage of the multi-phase benefits, the design of high-performance control techniques is a crucial task. In this regard, the inherent features of model predictive control (MPC) have aroused the interest of the scientific community in the last decade. This control solution, based on the usage of a cost function, provides notable flexibility to the control designer to select diverse regulation goals. Although the use of some weighting factors is a common trend to prioritize control purposes, the internal behavior of each term of the cost function is commonly ignored since the cost function is only focused on minimizing the total error. With this issue in mind, this work firstly performs a detailed analysis about the behavior of each cost function term in a standard MPC. The mentioned study permits identifying a certain pattern of the internal mechanics of the cost function that can disturb the current quality. To solve this limitation, the cost function of the MPC scheme is redefined. The proposed strategy permits improving current quality without increasing the number of switching states applied per control period, as it is validated with experimental results in six-phase electric drives.

Application of IgH EtherCAT Master for Ultra-Precision Motion Control of Precision Axes.

The EtherCAT fieldbus system is widely applied in different types of computerized numerical control (CNC) machine tools due to its outstanding communication performance. In the field of ultra-precision CNC, some machine tools employ controllers that integrate EtherCAT master functionality to achieve real-time communication with other devices; however, the open-source IgH EtherCAT master has rarely been applied to the CNC systems of ultra-precision machine tools. The feasibility of using the IgH EtherCAT master to meet the communication performance requirements of ultra-precision machine tools remains uncertain; therefore, it is necessary to validate the control effect on precision axes under the application of the IgH EtherCAT master. In this work, EtherCAT applications were developed on a personal computer (PC) to alter it to a bus-type controller with the IgH EtherCAT master function. To provide the EtherCAT master with real-time and accurate motion data of the axes, an interpolation algorithm tailored for control experiments was designed, and a G-code data processing method was proposed. Moreover, precision aerostatic linear axes and servo drivers were chosen as EtherCAT slaves for single-axis motion and dual-axis linkage control experiments. The experimental results showed that the motion controller based on IgH can effectively control the precision axes to execute ultra-precision linear and circular interpolation motion.

All-fiber fast coherent LiDAR for ranging and velocimetry based on optical comb injection

Frequency-modulated continuous-wave light detection and ranging (LiDAR) is a powerful ranging technique that offers inherent resistance to ambient light and the capability to simultaneously measure both distance and velocity. However, conventional LiDAR systems often face challenges with environmental interference and achieving a balance between demodulation complexity, data-refresh rate, and precision. Here, we introduce an all-fiber LiDAR system that utilizes optical comb injection for high-precision ranging and velocimetry. Owing to the injection locking process, the beam output from slave lasers inherits the linear and fast frequency modulation features of the injected optical comb sidebands generated through an electro-optics modulator while maintaining a stable output power with little phase noise, solving a big concern in traditional external modulation schemes. The unique dual-sideband locking and common-path design enhance stability and anti-interference ability, enabling the system to possess a high data-refresh rate of over 10 kHz and a high precision of 500 μm without complex algorithms or compensation circuits. Dynamic ranging and imaging of noncooperative targets are also demonstrated. With the advantages of fast and noncontact measurement, high precision and stability, and anti-interference ability, the proposed system has potential applications in automatic driving, machine control, and intelligent robotics.

Effect of wire feed speed and arc length on weld bead geometry in synergistic controlled pulsed MIG/MAG welding

Abstract Arc welding is one of the most commonly applied methods in the welded manufacturing industry. The pulse MIG/MAG welding method (GMAW-P) was developed to prevent problems that may occur in the weld bead due to high heat input to the base metal during the feeding of the weld metal in MIG/MAG welding applications. The synergistic controlled welding machines have been developed that facilitate the selection of welding parameters and provide simultaneous feeding of pulsed welding wire by adjusting the variables to the most appropriate values relative to each other. Weld bead geometry is greatly influenced by welding process parameter, i.e., welding speed, welding current, shielding gas flow rate, voltage, arc travel speed, type of shielding gas, etc. In this study, steel welding samples in T-joint shape were produced with synergistic controlled GMAW-P. The effect of welding parameters on weld bead geometry was investigated. As a result of this study, it was determined that increases in weld wire feed speed, arc voltage, and welding current increased weld depths of penetration and weld bead thickness.

"Man vs. Machine: 3D Milling of Auricular Frameworks".

Autologous rib harvest with manual framework production is the current gold standard for microtia reconstruction. Recent clinical success with implantation of cadaveric costal cartilage grafts opens the possibility of point of care auricular framework production. This paper assesses the feasibility and efficiency of 3D milling of cadaveric costal cartilage for auricular framework production. A Nagata/Firmin style auricular framework was manually carved en bloc out of soap and 3D scanned to design a milling toolpath on a desktop 3-axis computer numerical control (CNC) machine. An en bloc framework was then milled from cadaveric costal cartilage. Time to mill a complete framework was recorded. The dimensional/volumetric analyses were performed. The main outcome measures were total time to mill a complete auricular framework out of cadaveric cartilage and dimensional/volumetric comparisons to the model ear. Total milling time for the cartilage framework was approximately 7 min. Finalizing steps took an extra 20 min. Total time to produce a final framework ready for implantation was approximately 27 min, compared to the traditional 1-2 h by manual carving. All dimensional comparisons were within 2 mm to the manually carved model. Volumetric analysis showed 71% similarity. En bloc cadaveric costal cartilage framework milling is both feasible and efficient. 3D milling significantly reduces framework production time and allows for accurate reconstruction of the complex ear geometry, which can translate to cost savings, optimized patient safety, and potential for patient-specific reconstruction. The next step toward achieving clinical application is ensuring framework sterility.

OCRClassifier: integrating statistical control chart into machine learning framework for better detecting open chromatin regions.

Open chromatin regions (OCRs) play a crucial role in transcriptional regulation and gene expression. In recent years, there has been a growing interest in using plasma cell-free DNA (cfDNA) sequencing data to detect OCRs. By analyzing the characteristics of cfDNA fragments and their sequencing coverage, researchers can differentiate OCRs from non-OCRs. However, the presence of noise and variability in cfDNA-seq data poses challenges for the training data used in the noise-tolerance learning-based OCR estimation approach, as it contains numerous noisy labels that may impact the accuracy of the results. For current methods of detecting OCRs, they rely on statistical features derived from typical open and closed chromatin regions to determine whether a region is OCR or non-OCR. However, there are some atypical regions that exhibit statistical features that fall between the two categories, making it difficult to classify them definitively as either open or closed chromatin regions (CCRs). These regions should be considered as partially open chromatin regions (pOCRs). In this paper, we present OCRClassifier, a novel framework that combines control charts and machine learning to address the impact of high-proportion noisy labels in the training set and classify the chromatin open states into three classes accurately. Our method comprises two control charts. We first design a robust Hotelling T2 control chart and create new run rules to accurately identify reliable OCRs and CCRs within the initial training set. Then, we exclusively utilize the pure training set consisting of OCRs and CCRs to create and train a sensitized T2 control chart. This sensitized T2 control chart is specifically designed to accurately differentiate between the three categories of chromatin states: open, partially open, and closed. Experimental results demonstrate that under this framework, the model exhibits not only excellent performance in terms of three-class classification, but also higher accuracy and sensitivity in binary classification compared to the state-of-the-art models currently available.

Improvement of NC Program Quality based on Shape Generation Motions and Feed Drives for Five-Axis CNC Machine Tools

Five-axis Computer Numerical Control (CNC) machine tools, integrated with Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems, are used to machine complex parts and reduce trials and errors. However, these machine tools still rely on Numerical Control (NC) programs and often lack accuracy and precision due to poor quality when implemented in the machine. This research aims to enhance the quality of NC programs for five-axis CNC machine tools by focusing on shape generation motions and a closed-loop feed drive system with Proportional-Integral-Derivative (PID) control. The individual motions were mathematically described using 4x4 transformation matrices, incorporating kinematic motion deviations, end mill geometry, machining parameters, and cutting forces derived from virtual machining. Additionally, a closed-loop feed drive system with PID control was integrated with the new position and angular data of each axis from the shape generation motions model. The new NC programs were validated by machining an S-shaped part and measuring dimensional errors at 64 points before and after using a Coordinate Measuring Machine (CMM). The results indicate a substantial reduction in the standard deviations of form and angular errors within the NC program quality, totaling approximately 80.73%. Reductions are demonstrated in the standard deviations for the X, Y, A, and B axes, with decreases of 76.83%, 95%, 82.40%, and 68.72%, respectively indicating a significant improvement in the overall quality of the NC program.

Гарантированные оценки функции распределения остаточного ресурса технологического оборудования промышленных предприятий

The article discusses the functioning processes of the process equipment at industrial enterprises, including numerical control machines (CNC), metal cutting machines, press-forging and bending equipment, welding and laser equipment, molding and casting equipment, etc. The problem of determining guaranteed estimates of the distribution function of process equipment’s residual life is formulated as a problem of finding guaranteed (lower and upper) estimates of the probabilistic functionality on the multitude of distribution functions with preset timings equal to the distribution timings determined based on the initial small sample size of trouble-free operation timings of process equipment. An approach to finding guaranteed estimates of the residual life distribution function in conditions of incomplete data on the process equipment operation time before a failure based on the results of the solution for Markov’s timing problem has been proposed. Guaranteed estimates of residual life distribution functions are based on a multitude of discrete distribution functions that meet the timing limitations. Examples of finding guaranteed estimates of process equipment residual life distribution function are provided.

State Machine and Internal Model Control-based Hybrid Controller for Improved Transient Performance of Microgrids

The transient performance of microgrids is measured by their transient stability and transient response indices. The quality of these indices depends upon the design of the microgrid’s closed-loop control scheme, which is a cascaded combination of outer power controller and inner voltage-current (VA) controllers. Generally, the power controller is realized by droop control logic (fixed droop or adaptive droop), and VA controllers by conventional proportional-integral (PI) control. Control schemes with fixed droop coefficient logic and PI-based VA controllers suffer from stability and response issues. In this scheme, when these PI-based VA controllers are replaced with internal model control (IMC) based VA controllers, the response aspect is improved, while the stability aspect shows no improvement. Therefore, when fixed droop logic is replaced with adaptive droop control logic, stability is improved. However, adaptive droop coefficient schemes with PI-based VA controllers provide a limited improvement in stability. Moreover, conventional machine learning-based adaptive droop coefficient logic suffers from severe computational difficulties. It is verified in this work that IMC-based VA controllers which cannot improve stability with fixed droop logic, will contribute to stability enhancement when a suitable adaptive droop control logic is deployed. Therefore, to enhance both stability and response aspects with reduced computational effort, this paper proposes a hybrid control scheme made of a state machine-based droop controller (SMD) supported by IMC-based VA controllers. From the simulation results, while the conventional schemes could not withstand load power factors lesser than 0.707, the proposed hybrid controller scheme maintained stability at a power factor of 0.47 with a total harmonic distortion of less than 5%.

Modelling of Harris Hawks optimization with deep learning-assisted microgrid energy management approach

Microgrid (MG) is a potential decentralized energy distribution and generation technology that is resilient, reliable, and efficient. This small-scale power system is reliable and resilient since it connects to the grid or runs independently. Renewable energy is difficult to integrate into MG due to variable load and unreliable electricity. MG operation relies on an energy management system (EMS) to balance electricity demand and supply, reduce operational costs, and maximize renewable energy use. Intelligent control systems, optimization methods, and machine learning algorithms were used for MG EMS. The Harris Hawks optimization with deep learning-assisted microgrid energy management (HHODL-MGEM) technique is developed in this work. HHODL-MGEM comprises two main stages. In the first step, the HHODL-MGEM approach uses the Harris Hawks optimization or HHO algorithm to meet load power demands at a low cost while maintaining DC bus voltage and protecting the battery from overcharging and depletion. In the second step, long short-term memory (LSTM) networks can predict power costs. The HHODL-MGEM approach is evaluated using multiple methods. The experimental results showed that HHODL-MGEM outperforms other methods.

Model Independent Dynamic Predictive Controller Design Using Differential Extreme Learning Machine for Composition Control in Binary Distillation Column

Model Independent Dynamic Predictive Controller Design Using Differential Extreme Learning Machine for Composition Control in Binary Distillation Column