Machine System Research Articles

Software Embedded Fault Tolerance Machining System (FTMS) with Service Interruption and Switching Failures

Software Embedded Fault Tolerance Machining System (FTMS) with Service Interruption and Switching Failures

CORRELATION ANALYSIS BETWEEN COMPONENTS OF FORCE AND VIBRATION IN TURNING OF 11SMN30 STEEL

Workability of material is defined as the ease of operating on it. 11SMn30 is widely used material in automobile manufacturing industries. Cutting forces have been shown to be the most effective measure for understanding metal machining processes. The forces which helps in performing machining operation also affects the cutting tool, in terms of deformation, bend, wear, which leads to the vibration in the machining system, This article aims to study the correlation between feed, components of cutting forces and components of vibration in turning of 11SMn30 steel grade using dynamometer and MPU6050 sensor.

PROGRESS AND CHALLENGES IN PLUNGE MILLING: A REVIEW OF CURRENT PRACTICES AND FUTURE DIRECTIONS

This review examines recent advancements and ongoing challenges in plunge milling. It is an increasingly utilised machining process renowned for its high material removal rates, particularly with hard-to-machine materials like hardened steels and titanium alloys. Plunge milling’s unique perpendicular tool path offers enhanced stability and reduced lateral cutting forces, making it valuable for applications that demand precision and efficiency, such as aerospace and automotive manufacturing. The paper systematically analyses and synthesises research on critical areas of plunge milling optimization, including tool geometry, material selection, coating technologies, and process parameters, highlighting strategies to mitigate common issues like rapid tool wear and chip evacuation difficulties. In this comprehensive overview, the review introduces theoretical and experimental findings on optimizing plunge milling tools and process parameters—such as cutting speed, feed rate, and coolant delivery—that are essential for improving performance and achieving desirable surface finishes. The paper also explores innovative trends, including AI-driven optimization algorithms and hybrid machining systems, which hold promise for addressing persistent limitations and enhancing plunge milling’s industrial applicability. By consolidating findings from recent studies, this review contributes to a deeper understanding of plunge milling’s role in high-precision manufacturing and identifies future research directions for advancing the process. The insights presented offer practical and strategic implications, aiming to guide ongoing developments in plunge milling technology and its adoption across various precision-oriented industries.

An Optimization Model for Production Scheduling in Parallel Machine Systems

The efficiency and quality of the manufacturing industry are greatly influenced by production scheduling, which makes it a crucial aspect. A well-designed production scheduling scheme can significantly enhance manufacturing efficiency and reduce enterprise costs. This paper presents a tailored optimization model designed to address a more complex production scheduling problem that incorporates parallel machines and preventive maintenance. The proposed solutions aim to achieve a balance between job sequence and machine reliability, considering the minimum maintenance cost rate for determining maintenance cycles of deteriorating machines in real manufacturing scenarios. Furthermore, the objective of minimizing the maximum completion time guides machine assignment and job sequence based on maintenance constraints. The innovation lies in the introduction of a greedy algorithm that utilizes a water injection model to address this NP-hard integrated problem. A pre-distribution model is constructed using the water injection model, and its solution is utilized as input for constructing the production scheduling model, which aids in determining machine assignment and job sequence. This algorithm demonstrates remarkable effectiveness and efficiency, enabling the achievement of an optimal solution. A numerical example is presented to illustrate the computational process, accompanied by an extensive discussion of the results showcasing improved performance. Furthermore, the optimization model developed in this paper can be adapted to tackle the production scheduling problem with modifications tailored for parallel machines.

Studying the Effectiveness of Using Intelligent Mining Machinery Systems on Health, Safety, and Environmental Parameters and Preventive Maintenance

Introduction: The goal of this study is to investigate how the development of technology has affected the industry (especially the mining industry). For this purpose, this paper examines the impact of intelligent mining machinery systems, including tire pressure monitoring systems (TPMS), dispatching systems, and vehicle health monitoring systems (VHMS), on health, safety, and environmental parameters and preventative maintenance. Material and Methods: This study is descriptive-analytical research that was conducted between time intervals before and after employing the intelligent mining machinery systems. Initially, parameters were identified using the Delphi method. These parameters include human accidents, equipment accidents, environmental incidents, warnings and fines in the domains of health, safety, and the environment, tire usage parameters, the shelf life of the tire, oil overfill, fuel consumption, failure rate, mean time between failures, and preventive maintenance compliance schedules in the domain of preventative maintenance. The effectiveness of using these systems was then assessed by comparing the state of the specified parameters before and after the introduction of the intelligent mining machinery systems. Results: The findings of this research indicate that using intelligent mining machinery systems will decrease equipment accidents by 33.3%, extend the useful life of tires by 7.1%, reduce fuel consumption by 14.6%, cut the mean time required to repair by 25.5%, and enhance preventive maintenance compliance schedules by 5.7%. The findings showed the effectiveness of the use of intelligent systems of mining machines was obtained as follows: reduction of equipment accidents by 33.3%, increasing the useful life of tires by 7.1%, reducing fuel consumption by 14.6%, reducing the average downtime of the car for repair by 25.5% and increasing compliance with the maintenance program by 5.7%. Conclusion: Utilizing intelligent mining machinery systems might have a positive impact on the safety of machines, reduce negative environmental effects like fuel consumption, and improve the maintenance of heavy machinery, which would lead to better mining conditions and lower costs.

Self-sensing sliding mode control of workpiece chatter based on accurate prediction of machining vibration

The previous works concerning active chatter suppression in milling systems are generally carried out based on specially designed tool holders or spindles. Due to the continuous removal of materials, however, the workpiece system cannot be treated as quasi-static or rigid. In this paper, a manufacturing system based on the digital twins (DT) model and sliding mode (SM) controller is developed to suppress the milling chatter of thin-walled workpieces. A single degree of freedom (DOF) model of the workpiece is adopted to describe the milling system. The developed SM algorithm exhibits a good performance under the variations of modal parameters, cutting parameters, unmodeled dynamics, etc. To provide accurate control feedback, a DT model of workpiece vibration is established by presenting a self-sensing predictive method. The predictive methods are implemented based on the cooperation of the combination methods of beam functions (CMOBF) and the mode superposition method (MSM). In addition, the predictive accuracy is discussed quantitatively by establishing a dataset of error coefficients. The data of error coefficients are trained by the support vector machine (SVM) model, which is introduced to the predictive methods for further error modification. Finally, a DT framework is introduced briefly to combine the machining system and the control process. Based on the developed DT-driven manufacturing system, the information interaction between the control process and the vibrating system can be realized.

Digital twin dynamic force-thermal physics sub-cell for CFRP drilling process

The digital transformation of cutting processes is anticipated to advance digital twin (DT) automated machining, particularly for difficult-to-cut materials. This paper develops a dynamic physical sub-cell for the CFRP drilling process, facilitating real-time force-thermal analysis during tool degradation and enhancing DT machining technology. A comprehensive dynamic model was developed to account for force-thermal effects as the tool wears. This model included five correction functions that consider the wear characteristics of the drill bit, allowing the improved cutting force model to track the dynamic force trajectory accurately. The finite difference method was employed to analyze drilling temperature and heat accumulation, with the heat source generated by the cutting force. The heat source intensity was determined by the heat generation efficiency function. Furthermore, the cutting force at the exit was calculated, as the exit temperature alters the material's thermal properties at the entrance. Taking experimental data from 100 holes at each feed rate as a baseline, optimization algorithms determined the values of each correction coefficient within the comprehensive model. The model's validity was verified by comparing experimental results, showing average errors of 5.14N for thrust, 0.0165N·m for torque, and 7.04°C for temperature. This model was packaged as a dynamic physical sub-cell demo, which can be integrated into a DT machining system for rapid response to multifaceted force-thermal attributes.

Efficiency and Precision in Printing: Study of the Control System of a Rotogravure Machine

Rotogravure printing is a well-liked and effective printing technique in the packaging sector of a printing cylinder. In making an automatic rotogravure machine, it is necessary to design an appropriate control system. In Indonesia, semi-automatic technology is still used to make rotogravure machines, and printing speeds are often slow. For automation machines, they are still imported. Apart from that, the machine's ability to adjust its precision is also limited. The purpose of this article is to examine the control system for a 6-cylinder automatic rotogravure machine, and discuss the rotogravure machine printing process, important aspects of rotogravure machine operation, differentiation in selecting motor types for torque and speed control, as well as advantages in efficiency and precision control over the environment. The method of using a literature review is qualitative-descriptive. The main findings on rotogravure machines include five main aspects: (1) ink flow management, (2) control of the drying system, (3) register control, (4) tension and speed control, and (5) fault detection, data logging, and analysis.

Research on the performance of frequency tracking methods for the ultrasonic machining system considering the influence of impedance matching unit

In power ultrasonic systems, impedance matching is a critical technique which enables electrical energy to be fully utilized by the transducer. However, the electrical parameters of the transducer will change when thermal-force loads are applied in the application scenario. Although the transducer can be operated at the optimum frequency point by using resonance frequency tracking (RFT), there is a certain tracking error when tracking the resonant frequency of the transducer in the loaded condition due to the impedance matching unit. To improve the RFT accuracy and the stability of the ultrasonic system, an ultrasonic system with RFT function was designed in this research. Then four commonly used matching topologies were introduced. Secondly, the electrical parameter drift patterns of the ultrasonic device were explored. Based on the patterns, the impedance characteristics of the transducer with four matching topologies after being loaded were revealed. Finally, the theory and experiments were combined to reveal the influence of matching topology and loading effect on the output characteristics of the transducer. The results showed that the Ls and LCL topologies have better resistance to load fluctuation. Meanwhile, after Ls and LCL matching, the phase-locked loop method still has good RFT performance under heavy load conditions. This study can guide the selection of the transducer’s matching topology and RFT methods.

Digital twin-driven virtual commissioning for robotic machining enhanced by machine learning

Robotic machining has been increasingly applied in intelligent manufacturing production lines. Compared with the traditional machine tools, commissioning for robotic machining system (RMS) is particularly important due to the low accuracy of industrial robots (IRs). Traditional site commissioning has large workload and is difficult to handle the multi-source errors. Since digital twin (DT) provides strategies for staying synchronized with the physical entities in whole lifecycle, a DT-driven virtual commissioning (VC) system for RMS is developed in this study to improve machining accuracy and reduce the difficulty of commissioning. Firstly, the framework of DT-driven VC system is designed including several function modules such as interaction, data pre-processing, DT model of RMS (RMSDT), and optimization service. Since RMSDT is the kernel of precise VC, a machine learning-enhanced RMSDT oriented to actual machining path prediction is then constructed based on a proposed joint error equivalent strategy, which can fully consider the coupled multi-source errors of machining robot. After that, a practical consistency retention method for RMSDT is proposed based on a stepwise updating strategy, where the model performance can be maintained with low updating costs. Finally, a visual VC system is developed for the experimental 6-degree of freedom robotic milling platform to verify the feasibility and effectiveness of the VC framework. Multiple experiments are also performed to test the performance of RMSDT and contour error compensation. This study has useful reference for the enterprises engaged in RMS and has positive significance for promoting the robotic machining.

Internal cooling system effects to heat transfer and tool wear in turning operations of titanium alloys Ti6Al4V

Internal cooling systems are essential for turning operations to improve the efficiency and quality of the machining process. In this paper, application of the virtual machining system is developed to analyze the effects of internal cooling systems on heat transfer and tool wear in turning operations of titanium alloys Ti6Al4V. The cutting forces as well as cutting temperature along machining paths are simulated in the virtual machining system to predict and analyze the effects of internal cooling system to the heat transfer and tool wear during turning operations. The coolant flow inside the internal cooling channels of a rotating cutting tool is simulated using computational fluid dynamics. Next, utilizing the modified Johnson–Cook model, the cutting temperature during turning operations is determined. The finite element method is employed to predict tool wear by applying the Takeyama–Murata analytical method. To validate the study, experimental works are implemented using turning machine tool. The cutting settings during experimental works are feed rate of 0.3 mm/rev, depth of cut of 2 mm, and cutting speed of 75 m/min. Thus, using the proposed virtual machining system, the performance of internal cooling system can be enhanced to increase cutting tool life and surface quality of machined parts in turning operations.

Development and construction example of automatic operation system for ground improvement construction machine

Development and construction example of automatic operation system for ground improvement construction machine

Influence of Winding Configurations and Stator/Rotor Pole Combinations on Field Back-EMF Ripple in Switched Flux Hybrid Excited Machines

Similar to armature back electromotive force (armature back-EMF), the back-EMF also exists in the field winding of hybrid excited machines. However, the existence of field back electromotive force (field back-EMF) is harmful to the safe and stable operation of machine systems, e.g., higher losses, lower efficiency, higher torque ripple, and reduced control performance. This paper systematically investigates the influence of armature/field winding configurations together with stator/rotor pole combinations on the field back-EMF ripple in hybrid excited machines with switched-flux stators. The two-dimensional (2D) time-stepping finite element modeling and prototyping experiments are used for the research. The investigated field and armature coil pitches equal to 1, i.e., non-overlapped windings. The influential factors that are investigated in this paper mainly include the number of layers of field/armature windings, the number of field/armature coils, and the stator/rotor pole combinations. The results show that the field back-EMF’s harmonic order and peak-to-peak value are closely associated with field/armature winding configurations and stator/rotor pole combinations under various conditions. Finally, for validation of the results predicted by the finite element method, a prototype machine is built and tested. Overall, non-overlapped double-layer armature and field windings are recommended for the hybrid excited switched flux machines with various stator/rotor pole combinations to realize relatively lower field back-EMF under different conditions.

Fault diagnosis for multi-axis carving machine systems with Gaussian mixture hidden Markov models: A data-model interactive perspective

This paper is concerned with sensor fault diagnosis problems for multi-axis carving machine systems (MACMSs) with repetitive machining tasks. A novel fault diagnosis method that combines the multi-feature fusion technology and Gaussian mixture hidden Markov models (GMHMMs) is proposed, which is inspired by a data- and model-driven collaborative perspective. With fault-sensitive features first extracted from both the time domain and time–frequency domain, the composite health index (CHI) is constructed to facilitate the understanding of the time-varying evolution. Then, GMHMMs are established to characterize the probabilistic relationship between the hidden states and CHI. To achieve high-precision fault classification, a well-designed global objective function is adopted to dynamically optimize both the CHI construction and classifier model training in a closed-loop feedback mechanism. Specifically, the fusion coefficients with range and equality constraints are integrated as part of the model parameters into the global optimization objective function, thereby reducing the search range and improving convergence speed. Besides, the well-trained GMHMMs interact with each other to capture the correlation information between different faults, and are utilized for online fault diagnosis. Finally, experiments are conducted on a self-developed multi-axis carving machine platform. The results exhibit outstanding performance in comparison with existing methods, particularly attaining a diagnostic accuracy of 95.37%.

Методы детектирования и локализации пленочных резистивных элементов для лазерной подгонки их параметров: обзор

Laser trimming (normalization) of film resistive elements (REs) by making adjustments to their configuration is one of the most frequently used and promising methods for tuning electrical parameters of circuits in microelectronics. Given that the geometric sizes of REs and the diameter of the laser beam are crucial initial data for resistor modeling, the necessity for analysis of the actual layout of the board is evident. The key aspect in the adjustment of film REs is precise detection and accurate determination of the RE’s position and defects within the coordinate system of the laser trimming machine using information from the video feed. The paper presents a systematic review of modern methods for detecting and localizing elements in images of microcircuit boards and hybrid integrated circuits. Literature analysis revealed that there are very few publications directly related to the task of precision detection of film resistors, except for a small number of studies. Therefore, the review also includes research on detecting and recognizing surface-mounted components, as well as defect localization on printed circuit boards.

Development of a Machine stereo vision-based autonomous navigation system for orchard speed sprayers

In orchards, radio frequency scintillation caused by high vegetation density can hinder the effectiveness of machinery auto guidance based on the Global Navigation Satellite System (GNSS). Signal interference not only leads to poor quality of machine operation but can also pose operational risks. In this work, we propose an alternative trajectory positioning system to supplement traditional GNSS-based navigation for machinery used in orchards. The aim of this research was to develop a deep learning machine stereo vision guidance system onboard an orchard speed sprayer. The developed system combines a collision avoidance methodology along with deep learning-driven machine vision for interrow positioning and a dead reckoning set of rules for alternating U-turns. The developed methodology was tested in 4 rows of an artificial orchard. The results show that it is possible for the embedded EfficientDet target detection algorithm to guide the equipment at 1, 1.5 and 2 km × h−1 with minimum average root-mean-square errors (RMSEs) of 0.24 m, 0.20 m, and 0.31 m, respectively. When the system navigation was performed using YOLOv7, the minimum average RMSEs for each row were 0.40 m, 0.48 m, and 0.43 m, respectively, for the abovementioned speeds. The U-turn by dead reckoning showed minimum average RMSE values of 0.56 m, 0.22 m, and 0.35 m for row navigation based on EfficientDet at 1, 1.5 and 2 km × h−1, respectively. For YOLOv7-based navigation in rows, the minimum average RMSE values at these speeds were 0.35 m, 0.81 m, and 0.44 m, respectively. This study contributes to the field by proposing an alternative navigation system for orchard machines that operates without the limitations associated with GNSS. In addition, our proposed guidance methodology introduces an RGB-D collision avoidance system with a demonstrated safety capacity for navigation under real scenario conditions.

An application oriented all-round intelligent weeding machine with enhanced YOLOv5

Nobody would contest that physical weed removal methods offer numerous advantages over biochemical alternatives. Within the domain of intelligent mechanical weed control, comprehensive research targeting the entire intelligent weeding machine system remains relatively scarce. To expedite the practical application of intelligent weeding machines, this study explored an enhanced YOLOv5 model with one colour constancy module, which aimed at achieving higher accuracy in crop seedling detection. An innovative "separating and closing" strategy, which allows the machine to precisely avoid crop seedlings while effectively weeding the areas between crop seedlings was employed to facilitate intra-row weeding. By integrating this strategy with a comprehensive design of the mobile platform, inter-row weeding actuators, and harmonious control of these key components, this research successfully developed an intelligent weeding machine capable of simultaneously performing intra-row and inter-row (all-round) weeding. Compared with previous studies, this study put the emphases on complex farm lighting conditions, both inter-row and intra-row weeding functions, and weed regrowth. Field experiments conducted in lettuce (Lactuca sativa var. ramosa Hort.) fields at four different locations on three separate dates demonstrated that this intelligent weeding machine achieved average weeding rates, crop seedling damage rates, and regrowth rates of 96.87%, 1.19%, and 0.34%, respectively. The ability to perform all-round weeding simultaneously is a significant advance in mechanical weeding control. The design and methodology employed in this study have broad implications for advancing the field of precision agriculture and addressing the growing demand for sustainable farming practices.

Forecasting and Comparative Application of PV System Electricity Generation for Sprinkler Irrigation Machines Based on Multiple Models

Currently, photovoltaic (PV) resources have been widely applied in the agricultural sector. However, due to the unreasonable configuration of multi-energy collaboration, issues such as unstable power supply and high investment costs still persist. Therefore, this study proposes a solution to reasonably determine the area and capacity of PV panels for irrigation machines, addressing the fluctuations in power generation of solar sprinkler PV systems under different regional and meteorological conditions. The aim is to more accurately predict photovoltaic power generation (PVPG) to optimize the configuration of the solar sprinkler power supply system, ensuring reliability while reducing investment costs. This paper first establishes a PVPG prediction model based on four forecasting models and conducts a comparative analysis to identify the optimal model. Next, annual, seasonal, and solar term scale models are developed and further studied in conjunction with the optimal model, using evaluation metrics to assess and compare the models. Finally, a mathematical model is established based on the optimal combination and solved to optimize the configuration of the power supply system in the irrigation machines. The results indicate that among the four PVPG prediction models, the SARIMAX model performs the best, as the R2 index reached 0.948, which was 19.4% higher than the others, while the MAE index was 10% lower than the others. The solar term scale model exhibited the highest accuracy among the three time scale models, the RMSE index was 4.8% lower than the others, and the MAE index was 1.1% lower than the others. After optimizing the configuration of the power supply system for the irrigation machine using the SARIMAX model based on the solar term scale, it is verified that the model can ensure both power supply reliability and manage energy overflow effectively.

Investigation of a Modular High-Pressure Heat Exchanger with Metal Foam Packing for a Pneumatic-Hydraulic Drive.

The results of the first stage of work aimed at improving a hybrid drive system in which the combustion engine is supported by a pneumatic-hydraulic motor are presented. The purpose of the described work was to show that a heat exchanger with a design adapted to the operating conditions of a pneumatic-hydraulic motor would allow sufficient air heating at the expense of waste heat from the combustion engine, thus increasing the efficiency of the drive system. It was assumed that the key component of the heat exchanger would be copper foam in order to increase the heat exchange surface. A prototype modular heat exchanger was designed and tested. An open-cell copper foam with a porosity of 0.9 and a pore density of 40PPI was placed in the heat exchanger. Experimental and numerical air heating studies were carried out under various heat exchanger operating conditions. The tests were conducted at initial air temperatures of -123 °C, -71 °C, and 22 °C and air pressures of 2.5 × 106 and 7.0 × 106 Pa. The air mass flux was in the range of 3.6-1644 kg/(m2s). It was found that the tested heat exchanger allows a reduction in air consumption in the drive system of 11% to 58% and increases the efficiency of the air expansion system by 16% to 30%. The maximum efficiency of the heat exchanger is 96%. The results of the work carried out will help to improve the pneumatic-hydraulic drive systems of work machines and vehicles.

Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine Using WOA-XGBoost Model

Solar energy can mitigate the power supply shortage in remote regions for portable irrigation systems. The accurate prediction of solar irradiance is crucial for determining the power capacity of photovoltaic power generation (PVPG) systems for mobile sprinkler machines. In this study, a prediction method is proposed to estimate the solar irradiance of typical irrigation areas. The relation between meteorological parameters and solar irradiance is studied, and four different parameter combinations are formed and considered as inputs to the prediction model. Based on meteorological data provided by ten typical radiation stations uniformly distributed nationwide, an Extreme Gradient Boosting (XGBoost) model optimized using the Whale Optimization Algorithm (WOA) is developed to predict solar radiation. The prediction accuracy and stability of the proposed method are then evaluated for different input parameters through training and testing. The differences between the prediction performances of models trained based on single-station data and mixed data from multiple stations are also compared. The obtained results show that the proposed model achieves the highest prediction accuracy when the maximum temperature, minimum temperature, sunshine hours ratio, relative humidity, wind speed, and extraterrestrial radiation are used as input parameters. In the model testing, the RMSE and MAE of WOA-XGBoost are 2.142 MJ·m−2·d−1 and 1.531 MJ·m−2·d−1, respectively, while those of XGBoost are 2.298 MJ·m−2·d−1 and 1.598 MJ·m−2·d−1. The prediction effectiveness is also verified based on measured data. The WOA-XGBoost model has higher prediction accuracy than the XGBoost model. The model developed in this study can be applied to forecast solar irradiance in different regions. By inputting the meteorological parameter data specific to a given area, this model can effectively produce accurate solar irradiance predictions for that region. This study provides a foundation for the optimization of the configuration of PVPG systems for mobile sprinkler machines.