Machines, Volume 11, Issue 11 (November 2023) – 55 articles

This paper assesses the energy consumption, control performance, and application-specific functional requirements of a modular drivetrain in comparison to a benchmark drivetrain. A decentralised control architecture has been developed and validated using mechanical plant models. Simscape models have been validated with data from an experimental setup including an equivalent modular and benchmark drivetrain. In addition, the control strategy has been implemented and validated on the experimental setup. The results prove the ability of the control strategy to synchronize the motion of the different sliders, resulting in crank position tracking errors below 0.032 radians on the setup. The model and experimental data show an increased performance of the modular drivetrain compared to the benchmark drivetrain in terms of energy consumption, control performance, and functional requirements. The modular drivetrain is especially advantageous for machines running highly dynamic motion profiles due to the reduced inertia. For such motion profiles, an increased position tracking of up to 84% has been measured. In addition, it is shown that the modular drivetrain root mean square (RMS) torque is reduced with 32% compared to the benchmark drivetrain. However, these mechanical energy savings are partly counteracted by the higher motor losses seen in the modular drivetrain, resulting in potential electrical energy savings of around 29%. Full article

In 4D printing, an additively manufactured component is given the ability to change its shape or function in an intended and useful manner over time. The technology of 4D printing is still in an early stage of development. Nevertheless, interesting research and initial applications exist in the literature. In this work, a novel methodical approach is presented that helps transfer existing 4D printing research results and knowledge into solving application tasks systematically. Moreover, two different smart materials are analyzed, used, and combined following the presented methodical approach to solving the given task in the form of recovering an object from a poorly accessible space. This is implemented by self-positioning, grabbing, and extracting the target object. The first smart material used to realize these tasks is a shape-memory polymer, while the second is a polymer-based magnetic composite. In addition to the presentation and detailed implementation of the methodical approach, the potentials and behavior of the two smart materials are further examined and narrowed down as a result of the investigation. The results show that the developed methodical approach contributes to moving 4D printing closer toward a viable alternative to existing technologies due to its problem-oriented nature. Full article

Machine tools are used in a wide range of applications, and they can manufacture workpieces flexibly. Furthermore, they require maintenance; the overall costs include maintenance costs, which constitute a significant portion, and the costs involved in ensuring product quality. Therefore, anomaly detection in tool conditions is required, because these tools are essential industrial elements. However, the data related to tool conditions present some challenges: data imbalances and deficiencies. Data imbalances and deficiencies can affect the performance of anomaly detection models. A model trained using data with imbalances and deficiencies may miscalculate that abnormal data are normal data, leasing to errors. To overcome these problems, the proposed method has been designed using the wavelet transform, color space conversion, color extraction, puzzle-based data augmentation, and double transfer learning. The proposed method generated image data from time-series data, effectively extracted features, and generated new image data using puzzle-based data augmentation. The color information was processed to highlight features, and the proposed puzzle-based data augmentation was applied during processing to increase the amount of data to improve the performance of the anomaly detection model. The experimental results showed that the proposed method can classify normal and abnormal data with greater accuracy. In particular, the accuracy of abnormal data classification increased from 25.00% to 91.67%. This demonstrates that the proposed method is effective and can overcome data imbalances and deficiencies. Full article

The benefits of multi-robot systems are substantial, bringing gains in efficiency, quality, and cost, and they are useful in a wide range of environments from warehouse automation, to agriculture and even extend in part to entertainment. In multi-robot system research, the main focus is on ensuring efficient coordination in the operation of the robots, both in task allocation and navigation. However, much of this research seldom strays from the theoretical bounds; there are many reasons for this, with the most-prominent and -impactful being resource limitations. This is especially true for research in areas such as multi-robot path planning (MRPP) and navigation coordination. This is a large issue in practice as many approaches are not designed with meaningful real-world implications in mind and are not scalable to large multi-robot systems. This survey aimed to look into the coordination and path-planning issues and challenges faced when working with multi-robot systems, especially those using a prioritised planning approach, and identify key areas that are not well-explored and the scope of applying existing MRPP approaches to real-world settings. Full article

In today’s manufacturing landscape, Digital Twins play a pivotal role in optimising processes and deriving actionable insights that extend beyond on-site calculations. These dynamic representations of systems demand real-time data on the actual state of machinery, rather than static images depicting idealized configurations. This paper presents a novel approach for monitoring tool and component wear in CNC milling machines by segmenting and classifying individual machining cycles. The method assumes recurring sequences, even with a batch size of 1, and considers a progressive increase in tool wear between cycles. The algorithms effectively segment and classify cycles based on path length, spindle speed and cycle duration. The tool condition index for each cycle is determined by considering all axis signals, with upper and lower thresholds established for quantifying tool conditions. The same approach is adapted to predict component wear progression in machine tools, ensuring robust condition determination. A percentage-based component state description is achieved by comparing it to the corresponding Tool Condition Codes (TCC) range. This method provides a four-class estimation of the component state. The approach has demonstrated robustness in various validation cases. Full article

The assembly process of large-scale and non-standard industrial equipment poses significant challenges due to its inherent scale-related complexity and proneness to errors, making it difficult to ensure process cost, production cycle, and assembly accuracy. In response to the limitations of traditional ineffective production models, this paper aims to explore and propose a digital twin (DT)-based technology paradigm for the intelligent assembly of large-scale and non-standard industrial equipment, focusing on both the equipment structure and assembly process levels. The paradigm incorporates key technologies that facilitate the integration of virtual and physical information, including the establishment and updating of DT models for assembly structures using actual data, the assessment of structural assemblability based on DT models, the planning and simulation of assembly processes, and the implementation of virtual commissioning technology tailored to the actual assembly process. The effectiveness of the proposed paradigm is demonstrated through a case study involving the actual assembly of a large-scale aerodynamic experimental equipment. The results confirm its ability to provide valuable technical support for the design, evaluation, and optimization of industrial equipment assembly processes. By leveraging the DT-based methodological system proposed in this paper, significant improvements in the transparency and intelligence of industrial equipment production processes can be achieved. Full article

This paper is a review of the literature about CFD modeling and analysis of journal, thrust, and aerostatic bearings; the advantages and disadvantages of each are specified, and the bearing problems that have been analyzed are discussed to improve their designs and performance. A CFD transient analysis of journal bearings was conducted using the dynamic mesh method together with movement algorithms while keeping a structured mesh of a good quality in the ANSYS Fluent software to determine the equilibrium position of the journal and calculate the dynamic coefficients. Finally, areas of opportunity for analyzing and designing fluid film bearings to improve their performance are proposed. Full article

As the field of fault diagnosis in electrical machines has significantly attracted the interest of the research community in recent years, several methods have arisen in the literature. Also, raw data signals can be acquired easily nowadays, and, thus, machine learning (ML) and deep learning (DL) are candidate tools for effective diagnosis. At the same time, a challenging task is to identify the presence and type of a bearing fault under noisy conditions, especially when relevant faults are at their incipient stage. Since, in real-world applications and especially in industrial processes, electrical machines operate in constantly noisy environments, a key to an effective approach lies in the preprocessing stage adopted. In this work, an evaluation study is conducted to find the most suitable signal preprocessing techniques and the most effective model for fault diagnosis of 16 conditions/classes, from a low-workload (computational burden) perspective using a well-known dataset. More specifically, the reliability and resiliency of conventional ML and DL models is investigated here, towards rolling bearing fault detection, simulating data that correspond to noisy industrial environments. Diverse preprocessing methods are applied in order to study the performance of different training methods from the feature extraction perspective. These feature extraction methods include statistical features in time-domain analysis (TDA); wavelet packet decomposition (WPD); continuous wavelet transform (CWT); and signal-to-image conversion (SIC), utilizing raw vibration signals acquired under varying load conditions. The noise effect is examined and thoroughly commented on. Finally, the paper provides accumulated usual practices in the sense of preferred preprocessing methods and training models under different load and noise conditions. Full article

With the facilitated development of highly automated driving functions and automated vehicles, the need for advanced testing techniques also arose. With a near-infinite number of potential traffic scenarios, vehicles have to drive an increased number of test kilometers during development, which would be very difficult to achieve with currently utilized conventional testing methods. State-of-the-Art testing technologies such as Vehicle-in-the-Loop (ViL) or Scenario-in-the-Loop (SciL) can provide a long-term solution; however, validation of these complex systems should also be addressed. ViL and SciL technologies provide real-time control and measurement with multiple participants; however, they require enormous computational capacity and low-latency communication to provide comparable results with real-world testing. 5G (fifth-generation wireless) communication and Edge computing can aid in fulfilling these needs, although appropriate implementation should also be tested. In the current paper, a realized control model based on the SciL architecture was presented that was developed with real-world testing data and validated utilizing co-simulation and digital twin techniques. The model was established in Simcenter Prescan© connected to MATLAB Simulink^® and validated using IPG CarMaker^®, which was used to feed the simulation with the necessary input data to replace the real-world testing data. The aim of the current paper was to introduce steps of the development process, to present the results of the validation procedure, and to provide an outlook of potential future implementations into the state of the art in proving ground ecosystems. Full article

The fault diagnosis of a switch machine is vital for high-speed railway operations because switch machines play an important role in the safe operation of high-speed railways, which often have faults because of their complicated working conditions. To improve the accuracy of turnout fault diagnosis for high-speed railways and prevent accidents from occurring, a combination of bi-directional long short-term memory (BiLSTM) with the multiple learning classification based on associations (MLCBA) model using the operation and maintenance text data of switch machines is proposed in this research. Due to the small probability of faults for a switch machine, it is difficult to form a diagnosis with the small amount of sample data, and more fault text features can be extracted with feedforward in a BiLSTM model. Then, the high-quality rules of the text data can be acquired by replacing the SoftMax classification with MLCBA in the output of the BiLSTM model. In this way, the identification of switch machine faults in a high-speed railway can be realized, and the experimental results show that the Accuracy and Recall of the fault diagnosis can reach 95.66% and 96.29%, respectively, as shown in the analysis of the ZYJ7 turnout fault text data of a Chinese railway bureau from five recent years. Therefore, the combined BiLSTM and MLCBA model can not only realize the accurate diagnosis of small-probability turnout faults but can also prevent high-speed railway accidents from occurring and ensure the safe operation of high-speed railways. Full article

The 3-in-1 electric drive unit (EDU) has the advantage of increasing the motor size for a larger output, and the reducer can be a compact layout designed to incorporate three key components—the drive motor, inverter, and reducer—into a single main body. This paper explores a hybrid simulation model for a 3-in-1 electromechanical drive unit (EDU) and its supporting components, consisting of the gear drive unit (GDU) mount, the motor mount, and the roll rod mounts. The synthesis of these sub-components, including the 3-in-1 EDU itself, the three supporting mount modules, and a rigid-body finite element model, is presented. The dynamics of the 3-in-1 EDU were determined through an experimental modal test. Meanwhile, the dynamic stiffness and damping coefficients of the three supporting mounts were measured using an elastomer tester across a frequency range from 10 Hz to 1000 Hz. To evaluate the sensitivity of each mount, the total spectral responses of the 3-in-1 EDU were compared under a torque input, considering rigid connections for each mount in contrast to their original dynamic stiffness. Through installing a rollrod mount, the optimal rigid connection was identified to control the dynamic response of the 3-in-1 EDU hybrid model. Furthermore, simulation results for the rigid connections in each mount were validated against experimental findings, confirming that the rigid rollrod mount configuration provided the best results. Full article

Hydrostatic bearings come with certain advantages over rolling bearings in moving large-scale structures. However, assembly errors are a serious matter on large scales. This study focuses on finding assembly error tolerances for the most common types in segmented errors of hydrostatic bearing sliders: tilt and offset. The experimental part was performed in the laboratory on a full diagnostic hydrostatic bearing testing rig. An investigation of the type of error on bearing performance was first conducted under static conditions. We identified the limiting error-to-film thickness ratio (e/h) for static offset error as 2.5 and the tilt angle as θ = 0.46° for the investigated case. Subsequently, two types of offset error were investigated under slow-speed conditions at 38 mm/s. The limiting error for the offset error considering the relative bi-directional movement of the slider and the pad was determined as e/h < 1. The results further indicate that the error tolerance would further decrease with increasing speed. The experimental results of error tolerances can be used to determine the required film thickness or vice versa. Full article

Domestic chimney cleaning is still mostly a manual procedure which can be overly complex, dangerous, and expansive. This paper describes the design of a novel robotic device for chimney cleaning that aims to provide a valuable alternative solution to the traditional manual techniques with user-friendly and low-cost features. The proposed device enables a significant reduction in operator risks, including roof falling and soot dust contact. The paper’s content describes, in detail, the design process, including a definition of the main design requirements and steps towards the manufacturing of a preliminary prototype. Moreover, a preliminary validation is described through laboratory tests to demonstrate the engineering feasibility and effectiveness of the proposed design solution for the intended semi-autonomous chimney-cleaning application. Full article

This paper presents the performance analysis of a three-phase squirrel cage induction motor (SCIM) with a double–triple-layer (DTL) stator winding configuration operating with broken rotor bar (BRB) faults. The effects of BRB faults on the performance of specific parameters are analyzed under a steady-state regime. The SCIM is modeled using the two-dimensional finite element method (FEM) to study electromagnetic performance under healthy and BRB faulty conditions. To validate the finite element analysis (FEA) results, a prototype of an SCIM with a DTL stator winding configuration is tested for performance evaluation under healthy and BRB faulty conditions. The FEA and experimental (EXP) results of the SCIM with a DTL stator winding arrangement are compared with the results of the SCIM with a conventional double-layer (CDL) stator winding configuration. FEA and EXP results evidenced that the SCIM with a DTL stator winding configuration mitigates some of the adverse effects introduced by the BRB faults compared to the SCIM with a CDL stator winding of the exact specifications. Under loaded conditions, the SCIM with a DTL stator winding configuration reduced the magnitudes of the twice slip frequency sidebands caused by BRB faults from ±1.2 Hz for the SCIM with a CDL stator winding arrangement down to ±0.2 Hz and ±0.36 Hz when operating with 3BRB and 6BRB faults, respectively. The results also indicate that the SCIM with a DTL stator winding configuration has reduced the decibel sideband magnitude by 7.5 dB and 8 dB for unloaded and loaded conditions, respectively. This premise has positioned the SCIM with a DTL stator winding configuration as a strong candidate in applications where BRB faults are frequent, and the motor may be required to continue operating with a BRB fault until scheduled maintenance is in effect. Full article

This paper addresses the issue of a road-type-adaptive control strategy aimed at enhancing suspension performance. $H_2$ synthesis is employed for modeling road irregularities as impulses or white noise, minimizing the root mean square (RMS) of performance outputs for these specific road types. It should be noted, however, that this approach may lead to suboptimal performance when applied to other road profiles. In contrast, the $H_{\infty }$ controller is employed to minimize the RMS of performance outputs under worst-case road irregularities, taking a conservative stance that ensures robustness across all road profiles. To leverage the advantages of both controllers and achieve overall improved suspension performance, automatic switching between these controllers is recommended based on the identified road type. To implement this adaptive switching mechanism, manual switching is performed, gathering input–output data from the controllers. These data are subsequently employed for training an Adaptive Neuro-Fuzzy Inference System (ANFIS) network. This elegant approach contributes significantly to the optimization of suspension performance. The simulation results employing this novel ANFIS-based controller demonstrate substantial performance enhancements compared to both the $H_2$ and $H_{\infty }$ controllers. Notably, the ANFIS-based controller exhibits a remarkable 62% improvement in vehicle body comfort and a significant 57% enhancement in ride safety compared to passive suspension, highlighting its potential for superior suspension performance across diverse road conditions. Full article

Machining CMCs under productivity conditions while limiting tool wear and material damage is a challenge for applications such as jet aircraft engines and industrial turbines. This contribution focused on developing a method to characterize the wear of abrasive tools based on fractal dimensions. This solution allows characterization of the state of the tool after each machining and identification of the type of damage to the tool (regular wear of the diamond grains, cleavage, or breakage) and its influence on the cutting forces, in addition to damage to the machined material and the quality of the machined surface. Thus, the chipped area and the maximum chipping are directly associated with the fractal dimension of the tool surface and the metal removal rate of the process. The quality of the surface (Sa, Sz, and Sq) is associated with the fractal dimension of the surface of the tool characterizing the state of the grinding wheel and the radial depth of cut ae characterizing the engagement of the tool in the CMC material. Moreover, the results also demonstrated that the use of an abrasive tool associated with cutting conditions close to milling and not grinding is a viable solution. Full article

Achieving precise dimensional accuracy and improving surface quality are the primary research and development objectives in the engineering and industrial applications of 3D printing (3DP) technologies. This experimental study investigates the pivotal role of solid model geometry tuning in enhancing the dimensional accuracy of affordable 3D printing technologies, with a specific focus on economical engineering applications. This experiment utilises low-cost Material Extrusion/Fused Filament Fabrication (FFF) and Stereolithography (SLA)/Digital Light Processing (DLP) 3D-printed patterns for the meticulous measurement of errors in the X, Y, and Z directions. These errors are then used to refine subsequent solid models, resulting in a marked improvement in dimensional accuracy (i.e., 0.15%, 0.33%, and 2.16% in the X, Y, and Z directions, respectively) in the final DLP 3D-printed parts. The study also derives and experimentally validates a novel and simple mathematical model for tuning the solid model based on the calculated linear directional errors ($e_i$, $e_j$, and $e_k$). The developed mathematical model offers a versatile approach for achieving superior dimensional accuracy in other 3D printing processes. Medium-sized (4 to 10 cm) wax-made DLP- and PLA-made patterns are used to test the ceramic mould-building capacity for rapid casting (RC), where the FFF-based 3D-printed (hollow inside) pattern favours successful RC. This work comprehensively addresses the critical challenges encountered in low-cost DLP and FFF processes and their scopes in engineering applications. It provides novel suggestions and answers to improve the effectiveness, quality, and accuracy of the FFF 3D printing process for future applications in RC. Full article

Condition-based hydraulic cylinder maintenance necessitates quantitative fault diagnostics. However, existing methods are characterized by either qualitative or limited quantitative capabilities. In this paper, a quantitative fault diagnostic method using a particle filter for hydraulic cylinders is proposed. The problem of quantitative fault diagnostics is formally formulated in a stochastic framework to assess the health/fault state, and an architecture based on joint state-parameter estimation is proposed. Through the establishment and analysis of a nonlinear dynamic model of the hydraulic cylinder, the impact of time-varying parameters on the state variables is revealed. Three fault modes of the cylinder, including friction, internal leakage, and external leakage, are theoretically identified. The proposed method allows for a simultaneous quantitative diagnosis of these three fault modes. The performance of the proposed method is evaluated using meticulously designed experiments. The results demonstrate that the mean absolute percentage errors in the parameter estimations are below 9% (accuracy exceeding 91%), thus validating its feasibility and effectiveness. Full article

This paper studies the problem of linkage-bar synthesis by means of multiple deep neural networks (DNNs), which requires the inverse solution of linkage parameters based on a desired trajectory curve. This problem is highly complex due to the fact that the solution space is nonlinear and may contain multiple solutions, while a good quality of learning cannot be obtained by a single neural network approach. Therefore, this paper proposes employing Fourier descriptors to represent trajectory curves in a systematic and normalized form, developing a multi-solution distribution evaluation by random restart local searches (MDE-RRLS) to examine a better solution-space partitioning scheme, utilizing multiple DNNs to learn subspace regions separately, and creating a multi-facet query (MFQuery) to cooperatively predict multiple solutions. The experiments demonstrate that the proposed approach can obtain better or at least competitive outcomes compared to previous work in the literature. Furthermore, to verify the effectiveness and applicability, this paper investigates the design problem of an industrial six-linkage-bar ladle mechanism used in a die-casting system, and the proposed method can obtain several superior design solutions and offer alternatives in a short period of time when faced with redesign requirements. Full article

As the applications for freeform optical surfaces continue to grow, the need for high-precision machining methods is becoming more and more of a necessity. Different toolpath strategies for the ultra-high precision turning of freeform surfaces can have a significant impact on the quality of the machined surfaces. This paper presents a novel toolpath planning method for ultra-precision slow tool servo diamond turning based on the curvature of freeform surfaces. The method analyzes the differential geometric properties of freeform surfaces by reconstructing NURBS freeform surfaces. A mathematical model is constructed based on the parameters of different positions of the freeform surface, toolpath parameters, and tool residual height. Appropriate toolpath parameters can be calculated to generate the optical freeform ultra-precision slow tool servo diamond turning toolpath. Compared with the toolpaths generated by the traditional Archimedes spiral method, the ultra-precision slow tool servo diamond turning toolpath planning method proposed in this paper can generate more uniform toolpaths on the freeform surfaces and keep the residual tool height within a small range. Full article

Individual life monitoring is crucial for ensuring aircraft flight safety. Conventional life-consumption-based monitoring methods ignore reliability, thus disjoining them from the aircraft’s reliable life determination and extension, where high confidence and reliability are required. Therefore, this paper proposes a reliable life consumption and individual life monitoring method for aircraft structure fatigue. In the paper, the P-S-N curve, i.e., the relationship between the aircraft structure’s life (N) and fatigue load (S) under a certain probability (P), is established, by which the lower confidence limit of the aircraft structure’s reliable life can be evaluated under any fatigue loads. Based on that and the aircraft’s monitored fatigue loads, the indexes of reliable life consumption and remaining reliable life percentages are proposed and assessed in real time for individual aircraft life monitoring and online life management. Case studies indicate that the proposed method can guarantee high confidence and reliability requirements in individual life monitoring, consistent with the aircraft’s life determination and extension, which are widely accepted nowadays in engineering practice. Full article

The prediction of energy-related time series for computer numerical control (CNC) machine tool axes is an essential enabler for the shift towards autonomous and intelligent production. In particular, a precise prediction of energy consumption is needed to determine the environmental impact of a product and the optimization of its production. For this purpose, a novel approach for predicting high-frequency time series of numerically controlled axes based on the program code to be executed is presented. The method involves simulative preprocessing of the input NC code to determine each axis’s acceleration, velocity, and process force. Combined with the material removal rate, these variables are input for a machine learning (ML) model that delivers axis-specific high-frequency time series predictions. Compared to common approaches, it is thus possible to make predictions for the variable energy consumption of machine tools for any tool path or target resolution in the time domain. Experiments show that this approach achieves a high precision when a robust learning data basis is available. For the X-, Y-, and Z-axis, errors of 0.2%, −1.09%, and 0.09% for aircut and of 0.15%, −3.55%, and 0.08% for material removal can be achieved. The potentials for further improvement are identified systematically. Full article

In this paper, a mathematical analysis is presented to show the effect of grid-connected inverter (GCI) parameters on its emissions in the supraharmonic range. This analysis is extended to explain the effect of asymmetry on the emissions of parallel-connected GCIs on distributed power generation systems. The switching harmonics of a GCI appear as bands around the switching frequency and its multiples. A MATLAB/Simulink model is built to perform two studies. In the first study, we use one GCI to examine the effect of the parameters on the emissions, while in the second study, we examine the effect of the asymmetry of two parallel-connected GCIs on the total emission toward the grid. An actual setup is built to verify the results of the mathematical analysis and the simulation study. It is found that the SHs of single-phase GCI amplitude are strongly dependent on the DC-link voltage and the coupling inductor, while the phases of the sideband harmonics only change with changing the injected power. The variation of the injected power does not have any tangible effect on the carrier harmonics. Full article

This paper reviews the robotic manipulation of deformable objects in caregiving scenarios. Deformable objects like clothing, food, and medical supplies are ubiquitous in care tasks, yet pose modeling, control, and sensing challenges. This paper categorises caregiving deformable objects and analyses their distinct properties influencing manipulation. Key sections examine progress in simulation, perception, planning, control, and system designs for deformable object manipulation, along with end-to-end deep learning’s potential. Hybrid analytical data-driven modeling shows promise. While laboratory successes have been achieved, real-world caregiving applications lag behind. Enhancing safety, speed, generalisation, and human compatibility is crucial for adoption. The review synthesises critical technologies, capabilities, and limitations, while also pointing to open challenges in deformable object manipulation for robotic caregiving. It provides a comprehensive reference for researchers tackling this socially valuable domain. In conclusion, multi-disciplinary innovations combining analytical and data-driven methods are needed to advance real-world robot performance and safety in deformable object manipulation for patient care. Full article

Ensuring the operational safety and reliability of rotary machinery systems, especially in oil plants, has become a focal point in both academic and industry arenas. Specifically, in terms of key rotary machinery components such as shafts, the diagnosis of these systems is paramount for achieving enhanced generalization capabilities in fault diagnosis, encompassing multiple sensor-derived variables with their respective fault patterns. This study introduces a multi-stage approach to generalize capabilities for fault diagnosis that considers multiple sensor-derived variables and their fault patterns. This method combines the Convolutional Triplet Network for feature extraction with an ensemble model for fault classification. Initially, vibration signals are processed to yield the most representative temporal and spatial features. Then, an ensemble approach is used to maximize both diversity and accuracy by balancing the contributions of the individual classifiers. The approach can detect three representative types of shaft faults more accurately than traditional single-stage machine learning models. Comprehensive experiments, detailed within, showcase the method’s efficacy in diagnosing rotary machine faults across diverse operational scenarios. Full article

Drill bits with internal cooling capabilities are still not employed in stone machining practices within shop floor environments. Therefore, a conventional industrial drill bit used in stone machining was subject to a redesign wherein an axial cooling channel was machined throughout its body. A comparison was drawn between the standard drill bit without cooling capabilities and the redesigned drill bit, which used compressed air as a cooling medium. The experiment was performed by drilling three types of stone samples varying in hardness with nine combinations of cutting speed and feed rate. During the machining process, two types of process signals were continuously measured—namely, cutting forces and vibrations. Additionally, the cutting edges of the drill bits were inspected after a specific number of drilling cycles using a vision system. The primary objective of this study was to compare the cutting forces and tool wear dynamics achieved by those two drill bits. Furthermore, the usage of vibration signals in the classification of stone hardness during machining with an internally cooled drill bit was additionally analyzed. The results of this study unveiled improvement in minimizing cutting forces, vibrations, and the intensity of tool wear when utilizing an internally cooled drill bit. Even though the machining system generally exhibited lower vibrations, vibration signals again demonstrated commendable efficacy in classifying stone hardness. Full article

The ongoing digitalization of processes and products in mechanical engineering is accompanied by an increasing demand for data. In order to provide this data, technical systems are being extended with sensory functions. To supply those sensory functions on rotating elements—such as shafts—with electrical energy, and to be able to transmit signals out of the system, sliding contacts can be used as a cost-effective and established solution. However, if electrical properties of machine elements are utilized for sensing purposes, such as condition monitoring of rolling element bearings by means of impedance measurement, sliding contacts are directly in the measurement path and can thus influence the measured impedance. The aim of this paper is to analyze the impedance of graphite and silver graphite carbon brushes under different rotational speeds, in different positions, and with different carrier frequencies. The material of the carbon brushes as well as the position have significant effects on the impedance behavior. Furthermore, carbon brushes show a significant running-in behavior. The results are discussed, and indications for use in impedance measurements are given. Silver graphite carbon brushes in axial positioning are particularly suitable for impedance measurements of sensory utilizable machine elements. Sufficient running-in time must be considered. Full article

Economy and productivity are the two most important elements of modern manufacturing systems. Economy is associated with energy-efficient operations, which results in an overall high input-to-output ratio, while productivity is related to quality and quantity. This specific work presents experimental investigations of the use of cooling conditions (dry, MQL and wet) as input variables alongside other input parameters, including depth of cut, feed and cutting speed. This research aimed to investigate the variation in output responses including tool wear, specific cutting energy, and surface roughness while machining Inconel 718, a nickel-based super alloy. For experimentation, three levels of depth of cut, feed, and cutting speed were chosen. The Taguchi method was used for the experimental design. The contribution ratio of each input parameter was ascertained through analysis of variance (ANOVA). Use of coolant showed a positive effect on process parameters, particularly MQL. By adapting the optimum machining conditions, specific cutting energy was improved by 27%, whereas surface roughness and tool wear were improved by 15% and 30%, respectively. Full article

Research on helicopter transient maneuvering flight noise is a hotspot and challenging topic in the fields of helicopter design and application. A new time-accurate free-wake (TAFW) method and the Fowcs Williams-Hawkings (FW–H) equations are applied to analyze the aerodynamic and noise responses of a rotor subjected to a ramp increase in collective pitch, in hover, and in forward flight. First, a TAFW algorithm suitable for rotor aerodynamic simulation in steady-state flight and transient maneuvers is developed using modified third-order upwind backward differentiation formulas. Then, to verify the effectiveness and accuracy of the proposed method, various parameters are calculated for two scenarios and compared with corresponding results from experiments by the University of Maryland: the Langley 2MRTS rotor and the NACA rotor with ramp increases in collective pitch. Finally, the influence of collective pitch increase rate, the total increase of collective pitch, and the start and stop azimuth of ramp increase on the aerodynamic and loading noise responses of the rotor are analyzed in hover and forward flight conditions. The results show the ramp increase in collective pitch will affect the loading noise in three timescales: short-term, medium-term, and long-term. The change of the loading noise is greater when the collective pitch increase rate is greater, and the start and stop azimuth angles of the ramp increase are also important factors affecting the aerodynamic load distribution and directionality of the noise. Full article