Machine Control System Research Articles

Path Planning Strategy for Implementing a Machine Control System in Grader Operations

Path Planning Strategy for Implementing a Machine Control System in Grader Operations

MODERNIZATION OF THE WATER PURIFICATION SYSTEM FOR SEWAGE TREATMENT PLANTS USING SCHNEIDER ELECTRIC EQUIPMENT

Currently, the issue of modernization of outdated automatic control systems at strategically important infrastructure facilities of the city, such as water supply and water treatment, is urgent. Using modern automation equipment allows for improving the environmental situation and increasing the reliability of technological processes. This article discusses the development of a new automated system for the biological treatment facility of the Almaty Su sewage treatment plant to replace the outdated current automation. A control system for air conditioning machines, based on Schneider Electric equipment, has been developed using EcoStructure Control Expert software to manage the Modicon M340 controller. Additionally, using EcoStructure Machine Expert, a control system has been created for air injection machines during the biological cleaning phase, which uses the Modicon M241 controller.A process control mnemonic circuit was designed to switch between Modicon M340 and M241 controllers. Remote I/O architectures were developed using the Architecture Builder to create network configurations. An emergency protection system was implemented on Honeywell equipment at Kazakh-British Technical University (KBTU) JSC, using Safety Controller and Manager tools. Security levels at SIL enterprises were calculated.

Steam Pressure Control of Marine Boiler Based on AFSA-PSO-LSSVM

Abstract To solve the problem that the main steam pressure fluctuates greatly when the load of the combustion control system of the marine main boiler changes suddenly, present a combined inverse control system of Support vector machine and PID to control the steam pressure intelligently. The inverse model of the nonlinear system identified by LSSVM is used as the feedforward controller and the PID controller is used as the feedback controller. The hybrid intelligent algorithm of artificial fish swarm Algorithm and particle swarm optimization is used to optimize the parameters of the control system. The AFSA algorithm is used to search the initial values of the parameters and the PSO algorithm is locally updated. Finally, the mechanism of the steam pressure control system of the marine boiler is modeled and simulated, and the algorithm models of AFSA-PSO-LSSVM-PID, AFSA-LSSVM-PID, PSO-LSSVM-PID and LSSVM-PID are established. The experimental results show that the AFSA-PSO-LSSVM-PID model has a good dynamic, stability, anti-interference ability and robustness.

Design of Integrated System for the Comprehensive Utilization of Yellow River Sediment-Sealing Machine Structure and Control System Design

With the development of national industrialization, the control of yellow-river sediment needs to develop a complete automatic treatment system. In this paper, the structure and control system of the sealing machine in the integrated system design of the yellow river sediment comprehensive utilization are designed. Firstly, the overall design scheme of the sealing machine is determined, then, the structure and working principle of the sealing machine are introduced in detail. The design part of the actuator is defined. Secondly, finite element analysis is carried out for some of the stressed parts. The results show that the sealing machine system runs normally, the structure is not deformed and damaged, and it can work continuously for 12~16 hours, and the woven bag with sand can be successfully sealed. The sealing machine can not only save manpower and material resources, but also has the characteristics of simple operation and high efficiency. However, affected by the size of the equipment, it can only work in the factory or a large flat site.

Polishing Machine Control System Based on B-Spline Curve Trajectory

In this study, a B-spline trajectory control algorithm is presented, showcasing its ability to achieve smooth trajectory control within an embedded motion control system. Traditional surface machining methods involve converting interpolated trajectories into G-code using numerical control (NC) software, which is then downloaded and executed on the numerical control system. This research introduces a specialized third-degree B-spline interpolation method tailored for curved surface trajectories, aiming to enhance machining efficiency. A significant strength of this project lies in the holistic design of an embedded system, encompassing both hardware circuitry and software logic. Utilizing the cost-effective STM32 architecture, a B-spline discrete velocity interpolation algorithm is implemented, validated through experiments conducted on a polishing machine system. The project involves MATLAB software for data simulations and experiments on a polishing machine using B-spline curve interpolation, confirming the practicality and effectiveness of the third-degree B-spline algorithm within an embedded system.

Multi-attribute optimization of eco-manufacturing based criteria in CNC manufacturing systems using an evolutionary algorithm

Manufacturing technology has evolved over the years with the development of CNC manufacturing systems, flexible manufacturing, rapid prototyping, smart manufacturing etc Simultaneously, the development of new and exotic materials to match specific requirements has shaped new problems in manufacturing. The materials developed thus far require special tools, lubricating agents, and extra-care in machining without compromising the quality. Further the study of carbon emissions in manufacturing sector has also gained unusual spotlight in view of their deleterious effect on the ecological balance. The manufacturing systems have to be consequently designed and developed, such that they generate minimal quantity of emissions without forfeiting the prime objectives of quality and tool morphology. The present work is principally intended to analyse the effect of cutting parameters on the emission rate of greenhouse gases, tool wear and work-piece temperature concurrently. These studies are accomplished in both dry and wet conditions on computer numerical control machining system. The machining process involved plain facing of a Ti-6Al-4V hardened material. The experimental studies are realized using both single point and multi-point cutting tools and are supplemented with the application of Multi-Objective Genetic Algorithm (MOGA). The MOGA generated set of pareto-fronts for the four machining conditions were subjected to VIKOR, TOPSIS and LINMAP decision making approaches to arrive at the optimum values of decision variables. The optimum cutting parameters obtained in single point cutting tool machining in dry conditions are speed (873.2 rpm), feed (0.199 mm rev−1) and depth of cut (0.25 mm), while the corresponding values of responses are tool wear (67.19 μm), work-piece temperature (39.36 °C) and carbon emission (0.138 Kg-CO2). The equivalent values for multi-point cutting were determined as 899.8 rpm, 0.195 mm rev−1 and 0.25 mm, while the responses for these optimal conditions are 69.92 μm, 39.48 °C and 0.137 Kg-CO2 in that order.

Coupled fluid-structure-interaction simulation approach for correction of the thermal tool elongation to improve the milling precision

During milling operation, the position of the tool centre point (TCP) is affected by structural displacements, which are caused by force loads and heat input inside the machine as well as machining induced thermal loads. These thermal loads result in considerable thermal deformations of tool holder, tool, and accordingly the TCP position. They can be summed up to about tens of microns and compromise the dimensional accuracy. The objective was to develop an integrated, numerical simulation-based approach for future TCP correction for a milling process using characteristic diagrams. For this, a complex Fluid-Structure-Interaction (FSI) simulation model predicts the uni-axial displacement in the longitudinal direction of the TCP due to a specified process heat source at the tool tip. As a partial result, the simulation has reached its performance limits, under the restriction of a reasonable simulation time in order to produce characteristic diagrams for application on a milling machine. The calculated thermally induced displacement can be further processed with the aid of Design of Experiments (DoE) and response surface methodology (RSM), depending on the thermal process load and coolant volume flow rate. That results in characteristic diagrams for the displacement as a function of process parameters. In this study the calculated value for thermally induced TCP displacement covers a span from 10 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}m to 80 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}m, with a strongly nonlinear behaviour. Subsequently, this forms the basis for a future implementation of characteristic diagrams in the machine control system for online correction of thermal tool errors.

Research and Simulation on the Development of a Hydraulic Prop Support System of Powered Roof Support to Increase Work Safety.

The underground mining environment is currently based on technology that uses mainly analogue sensors in machine and equipment control systems. The primary machine performing the most important functions in a mining system is the powered roof support. In order for it to work properly, it is important that it achieves the required power. To ensure this, it is necessary to continuously and precisely monitor the pressure in the under-piston space of the prop. Due to the extreme environmental conditions, pressure sensors should have high sensitivity, large transmission capacity, small size and light weight. To achieve these requirements, the authors of the article propose to implement a monitoring system based on photonics technology. To achieve this goal, several studies were carried out. The range of these studies included simulations, bench tests and tests under real conditions. The obtained test results showed the possibility of developing the control system for the powered roof support, the additional function to supercharge power. Based on the analysis of the obtained test results, assumptions were developed for the development of a power charging system with monitoring sensors. Based on the guidelines obtained from the research results, thedevelopment of the above prototype based on photonics technology is proposed.

Time series prediction for production quality in a machining system using spatial-temporal multi-task graph learning

The quality of multiple-machining-feature parts in batch computer numerical control machining systems is affected by complex spatial-temporal relationships, making accurate prediction difficult. Traditional quality prediction methods are difficult to cope with the challenges posed by the time impact effects generated by batch production sequences and the spatial impact effects generated by the adjacency of machining features of thin-walled parts. Hence, the time series prediction for production quality in a machining system using spatial-temporal multi-task graph learning is proposed to analyze the spatial-temporal impact relationship of machining quality and achieve accurate quality prediction. Specifically, a graph learning-based framework to capture the coupled spatial-temporal effects is designed to represent the spatial topological and temporal relationship of the machining features of batch parts. A novel global-local multi-task spatial-temporal graph learning framework is constructed to simultaneously predict the time series quality of each machining feature at different spatial positions. The global multi-task model aims to extract spatial-temporal coupling data features (i.e., input parameters for machining features at different positions of batch parts) for machining feature clusters based on gated networks and spatial-temporal graph convolution with multi-head attention. Then, the local multi-task model for each feature cluster (round holes, rectangular grooves, etc.) performs individual time series quality prediction regarding multiple quality indicators. Experimental results on a production dataset of a computer numerical control machining system for thin-walled parts show that the proposed method is superior to the conventional models such as a simple spatial-temporal graph convolution method (3.0%, 18.3% and 11.0% improvement in R2, MAE and RMSE) or the model without graph approach (6.5%, 29.8% and 19.8% improvement in R2, MAE and RMSE).

ADVANCES IN ULTRAPRECISION DIAMOND TURNING: TECHNIQUES, APPLICATIONS, AND FUTURE TRENDS

Advances in ultraprecision diamond turning have revolutionized manufacturing processes across various industries, offering unparalleled precision and surface quality in the fabrication of optical components, microfluidic devices, and advanced mechanical parts. This review delves into the techniques, applications, and future trends in ultraprecision diamond turning, highlighting recent advancements and potential trajectories. Techniques in ultraprecision diamond turning have evolved significantly, driven by innovations in machine design, tooling materials, and control systems. Diamond turning machines equipped with ultra-stiff structures, high-speed spindles, and advanced feedback mechanisms enable sub-nanometer level accuracy and surface finishes down to Angstrom levels. Additionally, advancements in single-point diamond turning (SPDT), fast tool servo (FTS), and deterministic microgrinding (DMG) techniques further enhance the versatility and precision of the process. Applications of ultraprecision diamond turning span a wide range of industries, including aerospace, automotive, biomedical, and telecommunications. In optics manufacturing, diamond turning facilitates the production of aspheric lenses, freeform optics, and diffractive optical elements with unprecedented accuracy, contributing to the development of high-performance imaging systems and laser applications. Moreover, in the biomedical field, diamond-turned microfluidic devices enable precise control over fluid flow and particle manipulation, empowering advancements in drug delivery systems and lab-on-a-chip technologies. Future trends in ultraprecision diamond turning are poised to address challenges related to scalability, multi-material processing, and in-situ metrology. Integration of adaptive control algorithms and machine learning techniques promises enhanced process stability and predictive maintenance, optimizing productivity and reducing downtime. Furthermore, the development of hybrid manufacturing approaches, combining diamond turning with additive manufacturing or laser processing, offers novel avenues for fabricating complex, multi-functional components with improved efficiency and cost-effectiveness. The ongoing advancements in ultraprecision diamond turning techniques, coupled with diverse applications across industries, underscore its pivotal role in advancing manufacturing capabilities. Anticipated future trends hold promise for further expanding the scope and impact of this technology, driving innovation and pushing the boundaries of precision engineering.
 Keywords: Ultraprecision, Diamond, Turning, Technique, Review.

Robots for Forest Maintenance

Forest fires are becoming increasingly common, and they are devastating, fueled by the effects of global warming, such as a dryer climate, dryer vegetation, and higher temperatures. Vegetation management through selective removal is a preventive measure which creates discontinuities that will facilitate fire containment and reduce its intensity and rate of spread. However, such a method requires vast amounts of biomass fuels to be removed, over large areas, which can only be achieved through mechanized means, such as through using forestry mulching machines. This dangerous job is also highly dependent on skilled workers, making it an ideal case for novel autonomous robotic systems. This article presents the development of a universal perception, control, and navigation system for forestry machines. The selection of hardware (sensors and controllers) and data-integration and -navigation algorithms are central components of this integrated system development. Sensor fusion methods, operating using ROS, allow the distributed interconnection of all sensors and actuators. The results highlight the system’s robustness when applied to the mulching machine, ensuring navigational and operational accuracy in forestry operations. This novel technological solution enhances the efficiency of forest maintenance while reducing the risk exposure to forestry workers.

Rotor-Flux Vector Based Observer of Interior Permanent Synchronous Machine

The sensorless control system of the interior permanent magnet machine (IPMSM) is considered in this paper. The control system is based on classical linear controllers. In IPMSM, there occurs non-sinusoidal distribution of rotor flux together with the slot harmonics, these are treated as control system disturbances. In this case, the classical observer structure in the (d-q) is unstable for the low range of rotor speed resulting in disturbances. This negative effect can be minimized by using the observer structure which is based on the rotor flux vector in (α-β) stationary frame together with the classical rotor speed and position law of estimation. The performance of the observer structure is validated by simulation and experimental results in the sensorless control system with field-oriented control.

Intelligent residual film recovery machine monitoring and control system

Domestic residue recovery machines in China still follow the traditional towed mechanical model, lacking automation and intelligent features. Under the key R&D project of Xinjiang Autonomous Region, our team has developed integrated intelligent agricultural machinery for cotton straw crushing and residue recovery. This research covers the monitoring and control system, including PLC control and communication, working parameter monitoring and adjustment, algorithmic control of electro-hydraulic actuators, and human-machine interface display. Compared to traditional models, this machine replaces the conventional chain-driven mechanical structure and diesel tractor traction power with a fully hydraulic integrated suspension drive and power system. Sensors, PLC, and a touch screen are utilized for real-time monitoring and display of parameters. Hardware components like electromagnetic switch valves, electro-hydraulic proportional valves, hydraulic cylinders, and software elements such as PLC programs and intelligent control algorithms are employed for electro-hydraulic switching and proportional control of certain working parameters and mechanisms. All parameter information is consolidated in the PLC host and expansion modules and displayed and controlled on the human-machine interface. The system was installed on the first-generation machine for indoor testing, revealing that the monitoring and control system used on the first-generation machine achieves parameter detection and adjustment of the actuating mechanisms. It incorporates more advanced functions compared to traditional residue recovery machines. However, there is still ample room for improvement in the overall intelligence of the machine. The control system’s structure requires adjustment, and certain functionalities of the overall mechanical structure and working components need optimization or upgrading. Detailed enhancements to the entire system will be made in the next-generation machine.

STUDY OF THE CURRENT STATE OF DEVELOPMENT OF CONTROL AND AUTOMATION SYSTEMS OF HYDRAULIC MACHINES

Future hydraulic systems will heavily incorporate AI and ML, enhancing control precision and enabling predictive maintenance. AI-driven algorithms will increasingly be used for predictive maintenance, improving the reliability and lifespan of hydraulic systems. This integration will lead to smarter, more efficient operation, and extended equipment lifespans. The integration of IoT technology is crucial for real-time data collection and analysis, facilitating remote monitoring and control that leads to improved operational efficiency and maintenance management. The use of cloud platforms for data analysis and storage is likely to increase, enabling more sophisticated data processing and accessibility. Advanced sensors and predictive maintenance contribute significantly to safety by anticipating system failures. Reliability is also enhanced through reduced downtime and smarter maintenance schedules. The growing convergence of advanced robotics with hydraulic systems is steering these systems towards increased autonomy. Enhanced safety features will be a priority, driven by smarter sensors and control algorithms. The growing complexity of hydraulic systems underscores the need for specialized training and education in system design, maintenance, and operation. Future hydraulic machines are expected to be more customizable and flexible, with modular designs that cater to specific industry needs. A significant challenge is the adaptability of AI and ML models in dynamic, real-world conditions. Continuous learning methods are being developed to address this. In summary, the future of hydraulic machine control systems and automation looks towards greater intelligence, connectivity, efficiency, and adaptability. These trends will be underpinned by ongoing technological advancements, particularly in AI, ML, IoT, and sustainable practices.

Energy function and complex dynamics from a jerk system

Jerk, as a mathematical concept, is used in mechanics to describe the rate of change of acceleration and plays a crucial role in the design of control systems for machines and vehicles. Therefore, it is important to master the various states and the energy released during the change of acceleration. This is why a new simple jerk function introduced afterward, energy released, is derived from a Hamilton function using the Helmholtz theorem. The condition of having a stable or unstable rate of change of acceleration is established using Hopf bifurcation theory. Some two-parameter stability charts are then computed for a suitable selection region of the study. Using some nonlinear analysis metrics, in the unstable region of the study, the occurrence of phenomena is found, such as reverse period doubling bifurcation, antimonotonicity, and hysteresis involving the coexistence of the states in the considered jerk system. An electronic circuit is built and used to implement the mathematical expression of the jerk equation and validate the result of the theoretical investigation.

Structural Design and Analysis of Battery Cell Winding Machine

This article aims to address the issues currently faced by domestic battery cell winding machines, including small size, low production efficiency, poor winding accuracy, and low product yield. To overcome these challenges, we have taken a step further by optimizing the key structure and automatic control system of a small semi-automatic battery cell winding machine. As a result, we have successfully developed a fully automatic battery cell winding equipment capable of producing large-sized battery cells.By constructing a comprehensive machine experimental platform and conducting thorough experimental verification of battery winding, we have achieved full automation of the battery cell winding process. This significant progress has led to improvements in efficiency, accuracy, and product yield. Consequently, our study offers a practical solution for the optimization design of battery cell winding machines.

Experimental study of automated soil density control system of garlic planting machine

An automated system for adaptive regulation of soil density has been developed, which makes it possible to improve the quality of garlic incorporation when planting within the range of soil density regulation in the range of 1.2 -1.4 g/cm?. A methodology has been developed for conducting experimental studies of a machine for planting garlic with an installed automatic monitoring and control system for regulating soil density during planting. The results of experimental studies of the uniformity of soil compaction using a compaction roller with an automatic density control system and a closing device represented by a skid-type harrow indicate that after passing through a skid-type harrow, the value of the studied parameter increased by 18% relative to the initial state of the soil. The results of experimental studies of the quality indicators of the planting machine without an automatic monitoring and control system indicate a deterioration in the quality of the process of planting garlic bulbs with increasing forward speed in the range from 0.8 to 1.2 m/s at values of the coefficients of variation of the depth of planting of garlic bulbs ?_Hl from 32% to 42%. The results of experimental studies of the influence of a digital system for automatic control and management of garlic planting indicate the prospects of conducting research in this direction, which is confirmed by an increase in the quality indicators of the planting machine by 15% in comparison with the closing organ in the form of a skid-shaped harrow.

IoT based enabling home automation system for individuals with diverse disabilities

This paper addresses the challenges faced by individuals with impairments, focusing on their specific needs. Moreover, the insights from this study can be applied to individuals who have encountered accidents or various similar circumstances. The primary objective of this endeavor is to mitigate their discomfort. The project demonstrates the creation of an affordable machine control system utilizing IoT technology. This system incorporates functionalities such as speech control and hand gestures, making it beneficial for people with impairments. The Internet of Things (IoT) pertains to the interconnectedness of electronic devices, software, sensors, actuators, and network connectivity among machines, vehicles, structures, and other entities. This connectivity enables the gathering and sharing of data.The application of IoT technology in this undertaking facilitates wireless control of devices via internet connectivity. The project employs a Node MCU development board as the central computing module. Furthermore, the project envisions an interface that empowers users through voice commands and hand gestures, accessible through a dedicated app. The MIT Software Inventor employs an online speech-to-text infrastructure to enable speech control. The system listens for the user's vocal commands and recognizes hand angular positions. Upon detecting specific predefined phrases, corresponding actions are triggered to activate or deactivate the machines. The innovative aspect of this concept lies in its ability to empower differently-abled individuals to effortlessly control machines through voice commands and hand gestures. The proposed system extends its applications to the industrial sector, streamlining machinery management and enhancing personnel control. This app-based interface can be accessed from any global location, further enhancing its accessibility and usability.

Performance Metrics of an Intrusion Detection System Through Window-Based Deep Learning Models

Intrusion and prevention technologies perform reliably in harsh conditions by fortifying many of the world's highest security sites with few defects in high performance. This paper aims to contribute by designing an intrusion/preventive system using a window-based convolutional neural network (CNN), an integrated recurrent neural network (RNN), and autoencoders (AutoE) to detect and test the performance of the intrusion detection system. The data packets were converted to images where the pixels were used as input. The CNN architecture shows a three-layer model with high predictive performance. The result shows high performance on CNN as compared to both RNN and AutoE; CNN seems to resist overfitting more than the rest of the models. The future perspective would be to test the model on other standard methods such as support vector machine (SVM) and dynamic control systems.

AI-powered Electronic Control Systems for Software-defined Agricultural Machines

AI-powered Electronic Control Systems for Software-defined Agricultural Machines