Machines, Volume 12, Issue 2 (February 2024) – 63 articles

In tolerancing activities focusing on the allocation of geometrical tolerances, many critical issues originate from the non-optimal assignment of responsibilities among the organization units involved. This paper aims to depict relations between different tolerancing activities and relevant specifications, assigning them to the proper actor and, therefore, expanding the ISO 8015:2011 “responsibility principle”. A classification among tolerancing activities, specifications, and media is proposed; a horizontal hierarchical framework among functional, manufacturing, and verification specifications and a vertical hierarchical framework along the supply chain are discussed. Examples of both hierarchical structures are presented. Full article

In this research, an 8 × 8 scaled electric combat vehicle (SECV) is built. The scaled vehicle is evaluated in both experimental and simulated methods to analyze its performance. The scaled vehicle is developed to apply the Ackermann condition by implementing the individual steering and individual wheel speed control system at low speed. Individual eight-wheel rotational velocity control and individual eight-wheel steering angle control in real time are developed and installed on the remotely controlled scaled vehicle to meet a perfect Ackermann condition. Three different steering scenarios are developed and applied: a traditional steering scenario (first and second axle steering), fixed third axle steering scenario (first, second, and fourth axle steering), and all-wheel steering scenario. Stationary evaluation, turn radius evaluation, and double lane change evaluation are conducted to verify the application of the Ackermann condition. The differences between the experimental results and the simulated data are within an acceptable range. An important demonstration of this research is the novel validation of physical and simulated data in the application of the Ackermann condition for eight-wheel steering and velocity control for the three steering scenarios. Full article

A new portable arm exercise device is presented as a laboratory prototype to assist arm movements in rehabilitation therapies and movement exercises. Unlike the devices currently used, a portable design is proposed, with easy assembly and operational characteristics that enable it to be used by users in the home and in a familiar environment. Sensors are also provided on the rotating crank to validate and monitor the efficiency of the arm exercise. A low-cost prototype is assembled using off-the-shelf components and 3D-printed parts. Design issues are discussed and elaborated on to build a prototype for future laboratory testing using fairly simple experimental methodology. Preliminary testing by one author shows good feasibility of the device. The findings from the experimental results can be summarized as effective smooth-monitored cyclic motion in the crank rotation with limited values for acceleration less than 1 g and for acting user forces less than 22 N. The values detected are significantly lower in the left hand, with the testing subject being right-handed and healthy, without injury to her upper limbs. Full article

The recharging of electric vehicles will undoubtedly entail an increase in demand. Traditionally, efforts have been made to shift their recharging to off-peak hours of the consumption curve, where energy demand is lower, typically during nighttime hours. However, the introduction of photovoltaic solar energy presents a new scenario to consider when synchronizing generation and demand curves. High-generation surpluses are expected during the central day hours, due to the significant contribution of this generation; these surpluses could be utilized for electric vehicle recharging. Hence, these demand-side management analyses present important challenges for electricity systems and markets. This research explores this overdemand avenue and presents a method for determining the ideal recharge curve of the electric vehicle. Consequently, with this objective of maximizing photovoltaic generation to cover as much of the foreseeable demand for electric vehicles as possible in future scenarios of the electrification of the economy, the six fundamental electric vehicle charging profiles have been analyzed. A practical scenario for 2040 is projected for the Canary Islands, estimating the potential levels of demand-side management and associated coverage. The coverage ranges from less than 20% to over 40%, considering the absence of demand-side management measures and the maximum displacement achievable through such measures. Full article

Abrasive disc grinding is currently a key manufacturing process to achieve better accuracy and high-quality surfaces of TC17 components. Grinding force, which results from the friction and elastic–plastic deformation during the contact and interaction between the abrasive grains and the workpiece, is a critical parameter that represents the grinding accuracy and efficiency. In order to understand the influence factors of grinding force, the characteristics of the flexible abrasive disc grinding process were studied. Considering the contact state between the abrasive tool and the workpiece, the theoretical model of normal grinding force was established in detail, from macro- and micro-perspectives. By conducting single-factor and orthogonal grinding experiments of TC17 components, the influence of different process parameters on the normal grinding force was revealed. The normal grinding force prediction models of the abrasive disc grinding process were developed based on the Box–Behnken design (BBD) and particle swarm optimization–back propagation (PSO-BP) neural networks, respectively. The results showed that the normal grinding force was negatively correlated with the disc rotational speed, and positively correlated with the contact angle, grinding depth, and feed rate, and the interaction of the factor feed rate and grinding depth was the more influential factor. Both the BBD and PSO-BP force models had good reliability and accuracy, and the mean absolute error (MAE) and mean relative error (MRE) of the above two prediction models were 0.22 N and 0.16 N, and 13.3% and 10.9%, respectively. Full article

The Industry 4.0 research initiative strives to facilitate globally interconnected, flexible, and highly adaptable production systems. The use of skill-based control mechanisms such as OPC UA skills offers the prospect of a straightforward and flexible interchange, as well as the seamless integration of individual participants and processes through standardized interfaces. Furthermore, by enhancing these skills with evaluation parameters pertinent to the processes, such as CO₂ equivalents or the duration of specific skill executions, a foundation is laid for creating a customizable and adaptable composition of processes based on specific production process needs. In this article, the OPC UA skill concept is expanded with process-relevant properties, and a structured procedure for the introduction of skill-based process control is presented. The developed concept was implemented and tested on an industrial use case of glass pane completion. The aim of this publication is to demonstrate the potential of skill-based process control that has an integrated assessment of skills. Full article

This paper focuses on the development of a novel climbing robot that is designed for autonomous maintenance of vertical gardens in urban environments. The robot, designed with a unique five-legged structure, is equipped with a range of electrical and mechanical components, enabling it to autonomously navigate and maintain a specially designed vertical garden wall facilitating interactive maintenance and growth monitoring. The motion planning and control of the robot were developed to ensure precise and adaptive movement across the vertical garden wall. Advanced algorithms were employed to manage the complex dynamics of the robot’s movements, optimizing its efficiency and effectiveness in navigating and maintaining the garden structure. The operation of the robot in maintaining the vertical garden was evaluated during a two-week trial where the robot successfully performed nearly 8000 leg movements, with only 0.6% requiring human intervention. This demonstrates a high level of autonomy and reliability. This study concludes that the pentapod robot demonstrates significant potential for automating the maintenance of vertical gardens, offering a promising tool for enhancing urban green spaces. Full article

Milling parts with low rigidity (thin-walled parts) are increasingly attracting the interest of the academic and industrial environment, due to the applicability of these components in industrial sectors of strategic interest at the international level in the aerospace industry, nuclear industry, defense industry, automotive industry, etc. Their low rigidity and constantly changing strength during machining lead on the one hand to instability of the cutting process and on the other hand to part deformation. Solving both types of problems (dynamic and static) must be preceded by prediction of cutting forces as accurately as possible, as they have a significant meaning for machining condition identification and process performance evaluation. Since there are plenty of papers dealing with this topic in the literature, the current research attempts to summarize the models used for prediction of force in milling of thin-walled parts and to identify which are the trends in addressing this issue from the perspective of intelligent production systems. Full article

This study is focused on analysing residual stresses (RSs) after turning high-tempered bearing steel through the use of the X-ray diffraction (XRD) technique. Phase transformations expressed in terms of the near-surface white layer (WL) and the corresponding microhardness profiles are correlated with the RSs as well as the depth of the RS profiles. Normal and shear components of RS and FWHM (full width at half maximum) of the diffraction peaks are analysed as a function of cutting insert flank wear as well as the cutting speed. It was found that the influence of tool wear prevails over cutting speed, RSs tend to shift into the compressive region with increasing tool flank wear, and the valuable shear components of RSs can be found in the near-surface region when the cutting inserts of lower flank wear are employed. The increasing flank wear also increases the depth in which the compressive RSs can be found. Furthermore, surface RSs are affected by the phase transformation process (formation of re-hardened WL) as well as the superimposing mechanical and thermal load. Full article

Nomex honeycomb composite (NHC) cores have seen significant growth in recent years, particularly in the aeronautics, aerospace, naval and automotive industries. This development presents significant challenges in terms of improving machining quality, requiring the use of specialized cutting tools and favorable cutting techniques. In this context, experimental studies have been carried out to highlight the characteristics of the milling of NHCs by rotary ultrasonic machining (RUM). However, the rapid motion of the cutting tool and the inaccessibility of the tool/part interface prevent the visualization of the chip formation process. For this purpose, a three-dimensional numerical model for milling the NHC structure using RUM technology was developed by Abaqus Explicit software. On the basis of this model, the components of the cutting force, the quality of the machined surface and the chip accumulation in front of the cutting tool were analyzed. The numerical results agree with the experimental tests, demonstrating that the use of RUM technology effectively reduces the cutting force components. An in-depth analysis of the influence of feed component F_y on the quality of the generated surface was carried out, revealing that the surface quality improved with low values of feed component F_y. Furthermore, the impact of ultrasonic vibrations on the accumulation of chips in front of the cutting tool is particularly optimized, in particular for large amplitudes. Full article

The Industrial Product Service System (IPS²) is considered a sustainable and efficient business model, which has been gradually popularized in manufacturing fields since it can reduce costs and satisfy customization. However, a comprehensive design method for IPS² is absent, particularly in terms of requirement perception, resource allocation, and service activity arrangement of specific industrial fields. Meanwhile, the planning and scheduling of multiple parallel service activities throughout the delivery of IPS² are also in urgent need of resolution. This paper proposes a method containing service order design, service resource configuration, and service flow modeling to establish an IPS² for robot-driven sanding processing lines. In addition, we adopt the modified Deep Q-network (DQN) to realize a scheduling scheme aimed at minimizing the total tardiness of multiple parallel service flows. Finally, our industrial case study validates the effectiveness of our methods for IPS² design, demonstrating that the modified deep reinforcement learning algorithm reliably generates robust scheduling schemes. Full article

The optimization of the brake systems is crucial for vehicle performance and safety of Formula SAE (FSAE) race cars. This study introduces a specialized brake test bench designed to enhance the understanding and testing of these systems. The bench integrates a rotating mechanical system mounting a brake disc-caliper group, which is driven by an electric motor, a pneumatic brake pedal assembly to simulate real braking conditions, and a comprehensive array of sensors that facilitate the measurement of critical parameters, such as rotation speed, braking torque, oil pressure, and disc temperature. Its structure, sensor integration, and control electronics are fully described, demonstrating the capability to replicate on-track scenarios in a controlled environment. The results underscore the utility of the bench in providing precise and consistent testing conditions essential for analyzing the efficiency, durability, and safety of the braking systems of FSAE race cars. Full article

With the rapid growth of the photovoltaic industry, fire incidents in photovoltaic systems are becoming increasingly concerning as they pose a serious threat to their normal operation. Research findings indicate that direct current (DC) fault arcs are the primary cause of these fires. DC arcs are characterized by high temperature, intense heat, and short duration, and they lack zero crossing or periodicity features. Detecting DC fault arcs in intricate photovoltaic systems is challenging. Hence, researching DC fault arcs in photovoltaic systems is of crucial significance. This paper discusses the application of mathematical morphology for detecting DC fault arcs. The system utilizes a multi-stage mathematical morphology filter, and experimental results have shown its effective extraction of fault arc features. Subsequently, we propose a method for detecting DC fault arcs in photovoltaic systems using a cyclic neural network, which is well-suited for time series processing tasks. By combining multiple features extracted from experiments, we trained the neural network and achieved high accuracy. This experiment demonstrates that our recurrent neural network (RNN) based scheme for DC fault arc recognition has significant reference value and implications for future research. The ROC curve on the test set approaches 1 from the initial state, and the accuracy on the test set remains at 98.24%, indicating the strong robustness of the proposed model. Full article

This study aims to develop, evaluate, and improve a polygeneration system that combines solar and Brayton cycle technologies and focuses on the sequential integration of heat. In this configuration, the exhaust gases from the Al-Qayyarah gas turbine power plant and the parabolic trough collector (PTC) array generate steam through a high recovery steam generation process. An absorption refrigeration system also supplies the Brayton circuit with low-temperature air. This process is evaluated from a 3E perspective, which includes exergy, energy, and exergoeconomic analyses for two different configurations. These configurations are integrated solar combined cycle (ISCC) with and without absorption systems (ISCC and ISCC-ARC). In addition, a comprehensive analysis was carried out to assess the impact of critical factors on the output generated, the unit cost of the products, and the exergy and energy efficiency for each configuration. The results revealed that the power produced by the ISCC-ARC and ISCC systems is 580.6 MW and 547.4 MW, respectively. Accordingly, the total energy and exergy efficiencies for the ISCC-ARC are 51.15% and 49.4%, respectively, while for the ISCC system, they are 50.89% and 49.14%, respectively. According to the results, the total specific costs for the ISCC-ARC system increased from 69.09 $/MWh in June to 79.05 $/MWh in December. ISCC’s total specific costs also fluctuate throughout the year, from 72.56 $/MWh in June to 78.73 $/MWh in December. Full article

In recent years, increased sales of fuel cell electric vehicles (FCEVs) have required composite overwrapped pressure vessel (COPV) designs to be lightweight and allow safe high-pressure hydrogen storage. In this study, we propose the weight minimization of Type 2 COPVs for FCEVs considering mechanical safety. Steel liner thickness, ply thickness, ply orientation, and the number of plies were set as design variables, and weight minimization was performed. For the constraints of optimization, the Tsai–Wu failure index of the composite layer and von Mises stress of the steel liner are considered. The design of experiments (DoE) was conducted to generate kriging model and perform sensitivity analysis. The optimized design of Type 2 COPVs was determined by satisfying all constraints, with significant weight reduction and preserved mechanical safety of the structure. Full article

Periodic monitoring of large industrial and civil structures is carried out through static and dynamic measurements. The monitoring, carried out over many years, offers important information for evaluating the health of structures and their management. Dynamic tests are carried out starting from measurements of the vibrations of the structure induced by mechanical devices or by the surrounding environment. If a ground support element is available, it is possible to exert a forcing action on the structure using actuators fixed to the support. When a ground support is unavailable, the structure can be forced using devices comprised of masses with rotary or reciprocating translational motion. These masses must be large enough to excite appreciable mechanical vibrations of the structure. In this paper, a vibration exciter, based on a mass suspended on an air spring and forced to vibrate at the resonant frequency, is proposed. Thanks to the resonant condition, the force transmitted to the structure is amplified compared to that applied to the mass. The excitation frequency can be adjusted by altering the inflation pressure of the air spring to modify the natural frequency of the system. In the paper, after the presentation of some mechanical devices used as vibration exciters for large structures, the proposed device is described and the first experimental results are reported. Full article

This study delves into the EA-SAS platform, a digital twin environment developed by our team, with a particular focus on the EA-SAS Cloud Scheduler, our bespoke program designed to optimize ETL (extract, transform, and load) scheduling and thereby enhance automation within industrial systems. We elucidate the architectural intricacies of the EA-SAS Cloud Scheduler, demonstrating its adeptness in efficiently managing computationally heavy tasks, a capability underpinned by our empirical benchmarks. The architecture of the scheduler incorporates Docker to create isolated task environments and leverages RabbitMQ for effective task distribution. Our analysis reveals the EA-SAS Cloud Scheduler’s prowess in maintaining minimal overhead times, even in scenarios characterized by high operational loads, underscoring its potential to markedly bolster operational efficiency in industrial settings. While acknowledging the limitations inherent in our current assessment, particularly in simulating real-world industrial complexities, the study also charts potential future research pathways. These include a thorough exploration of the EA-SAS Cloud Scheduler’s adaptability across diverse industrial scenarios and an examination of the integration challenges associated with its reliance on specific technological frameworks. Full article

This paper presents a planar cable-driven model of a simple mechanism that is able to measure forces and displacements. Recently, a preliminary study based on a cable-driven sensitive mechanism was presented to the research community, underlining the innovative characteristics of the model in under-actuation and under-sensing. The core of the research work was to conceive a compliant system able to measure forces and displacements from a point located in a different zone with respect to the one where the force is applied, and this is possible thanks to cable-driven systems. In this paper, a new simplified model with respect to our published work is presented, reducing the number of cables and including the calculation of friction in the developed test bench. The formulation to calculate the displacement of the point of the applied force and the formulation to calculate the force are presented and validated with a simulation and by using a real test bench for experimentation. A multi-body system is used for the simulation, and the results are compared and discussed. Four cases are analysed to test the formulation, including the friction in pulleys and in the joint connection between the mobile part and the fixed part of the mechanism. Future works will be oriented toward reducing the dimensions of the conceived mechanism in order to implement the model in minimally invasive robotic surgery instruments. Full article

In this paper, the direct differentiation of generalized-$\mathsf{\alpha }$ time integration is derived, equations are introduced and results are shown. Although generalized-$\mathsf{\alpha }$ time integration has found usage, the derivation and the resulting equations for the analytical sensitivity analysis via direct differentiation are missing. Thus, here, the sensitivity equations of generalized-$\mathsf{\alpha }$ time integration via direct differentiation are provided. Results with generalized-$\mathsf{\alpha }$ are compared with Newmark-$\mathsf{\beta }$ time integration and their sensitivities with numerical sensitivities via forward finite differencing in terms of accuracy and performance. An example is shown for each linear structural dynamics and flexible multibody dynamics. Full article

Data-based equipment fault detection and diagnosis is an important research area in the smart factory era, which began with the Fourth Industrial Revolution. Steel manufacturing is a typical processing industry, and efficient equipment operation can improve product quality and cost. Steel production systems require precise control of the equipment, which is a complex process. A gearbox transmits power between shafts and is an essential piece of mechanical equipment. A gearbox malfunction can cause serious problems not only in production, quality, and delivery but in safety. Many researchers are developing methods for monitoring gearbox condition and for diagnosing failures in order to resolve problems. In most data-driven methods, the analysis data set is derived from a distribution of identical data with failure mode labels. Industrial sites, however, often collect data without information on the failure type or failure status due to varying operating conditions and periodic repair. Therefore, the data sets not only include frequent false alarms, but they cannot explain the causes of the alarms. In this paper, a framework called the Reduced Lagrange Method (R-LM) periodically assigns pseudolabels to vibration signals collected without labels and creates an input data set. In order to monitor the status of equipment and to diagnose failures, the input data set is fed into a supervised learning classifier. To verify the proposed method, we build a test rig using motors and gearboxes that are used on production sites in order to artificially simulate defects in the gears and to operate them to collect vibration data. Data features are extracted from the frequency domain and time domain, and pseudolabeling is applied. There were fewer false alarms when applying R-LM, and it was possible to explain which features were responsible for equipment status changes, which improved field applicability. It was possible to detect changes in equipment conditions before a catastrophic failure, thus providing meaningful alarm and warning information, as well as further promising research topics. Full article

Any 3D AFM image is a convolution of the geometry of the AFM tip and the profile of the scanned sample, especially when the dimensions of the scanned sample are comparable to those of the AFM tip shape. The precise profile of the scanned sample can be extracted from the 3D AFM image if the geometry of the AFM tip is known. Therefore, in order to separate the geometry of the AFM probe tip from the 3D AFM image of a diffraction grating with a rectangular profile and to correct for the topographic convolutions induced by the AFM probe tip, a method is used to quantitatively evaluate the geometry of the AFM probe tip, including the tip radius and the included angle. A model for reconstructing the measured AFM image is proposed to correct topography convolutions caused by the AFM tip shape when scanning a diffraction grating with rectangular profiles. A series of experiments were performed to verify the effectiveness of the proposed AFM tip geometry evaluation method, and comparison experiments were conducted to demonstrate the feasibility and reliability of the proposed reconstruction model. Full article

Computer-aided engineering (CAE) is an essential tool in a digital twin not only to verify and validate a virtual twin before it is transformed into a physical twin, but also to monitor the use of the physical twin for enhanced sustainability. This paper aims to develop a CAE model for a digital twin to predict the fatigue life of materials. Fatigue damage is represented by the size of a macro-crack that grows with a cluster of micro-cracks subjected to three different loads. The growth angle is related to the maximum circumferential tensile stress, and the growth rate is determined by the stress intensity factor (SIF) at the crack tip. The prediction model takes into consideration the main factors, including micro-cracks, crack closures, and initial configurations. Simulations are developed for the growth of macro-cracks with radially distributed micro-cracks and randomly distributed micro-cracks, and we find that (1) the macro-crack in the second case grows faster than that in the first case; (2) a pure shear load affects the macro-crack propagation more than a combined shear and tensile load or a tensional load; (3) the external stresses required to propagate are reduced when the inclination angle of the micro-crack is small and within (−25° < β < 25°); (4) micro-cracks affect the propagating path of the macro-crack and generally guide the direction of propagation. The developed model has been verified and validated experimentally for its effectiveness in predicting the fracture or fatigue damage of a structure. Full article

This study introduces a cybernetic control and architectural framework for a robotic fish avatar operated by a human. The behavior of the robot fish is influenced by the electromyographic (EMG) signals of the human operator, triggered by stimuli from the surrounding objects and scenery. A deep artificial neural network (ANN) with perceptrons classifies the EMG signals, discerning the type of muscular stimuli generated. The research unveils a fuzzy-based oscillation pattern generator (OPG) designed to emulate functions akin to a neural central pattern generator, producing coordinated fish undulations. The OPG generates swimming behavior as an oscillation function, decoupled into coordinated step signals, right and left, for a dual electromagnetic oscillator in the fish propulsion system. Furthermore, the research presents an underactuated biorobotic mechanism of the subcarangiform type comprising a two-solenoid electromagnetic oscillator, an antagonistic musculoskeletal elastic system of tendons, and a multi-link caudal spine composed of helical springs. The biomechanics dynamic model and control for swimming, as well as the ballasting system for submersion and buoyancy, are deduced. This study highlights the utilization of EMG measurements encompassing sampling time and $\mu$-volt signals for both hands and all fingers. The subsequent feature extraction resulted in three types of statistical patterns, namely, $\mathsf{\Omega },\gamma ,\lambda$, serving as inputs for a multilayer feedforward neural network of perceptrons. The experimental findings quantified controlled movements, specifically caudal fin undulations during forward, right, and left turns, with a particular emphasis on the dynamics of caudal fin undulations of a robot prototype. Full article

The increasing demand for personalized products and the lack of skilled workers, intensified by demographic change, are major challenges for the manufacturing industry in Europe. An important framework for addressing these issues is a digital twin that represents the dynamic behavior of machine tools to support the remaining skilled workers and optimize processes in virtual space. Existing methods for modeling the dynamic behavior of machine tools rely on the use of expert knowledge and require a significant amount of manual effort. In this paper, a concept is proposed for individualized and lifetime-adaptive modeling of the dynamic behavior of machine tools with the focus on the machine’s tool center point. Therefore, existing and proven algorithms are combined and applied to this use case. Additionally, it eliminates the need for detailed information about the machine’s kinematic structure and utilizes automated data collection, which reduces the dependence on expert knowledge. In preliminary tests, the algorithm for the initial model setup shows a fit of 99.88% on simulation data. The introduced re-fit approach for online parameter actualization is promising, as in preliminary tests, an accuracy of 95.23% could be reached. Full article

Manual counting and packaging processes often involve repetitive, error-prone tasks. Specifically, packaging wooden handles, utilized in gardening tools and cutlery, typically relies on labor-intensive methods with dimensions varying in diameter, length and mass. These variations complicate packaging, requiring precise counting and diverse handling solutions. This article introduces an automated counting structure tailored for a wide array of wooden handles manufactured by a company in northern Portugal. Employing standardized mechanical design methodologies, we delineate crucial stages encompassing the design, development, implementation and testing of this specialized counting equipment. The machine has been partially integrated into the management system of the company, taking into account future global integration according to the Industry 4.0 concept. Full article

Condition monitoring (CM) is essential for maintaining operational reliability and safety in complex machinery, particularly in robotic systems. Despite the potential of deep learning (DL) in CM, its ‘black box’ nature restricts its broader adoption, especially in mission-critical applications. Addressing this challenge, our research introduces a robust, four-phase framework explicitly designed for DL-based CM in robotic systems. (1) Feature extraction utilizes advanced Fourier and wavelet transformations to enhance both the model’s accuracy and explainability. (2) Fault diagnosis employs a specialized Convolutional Long Short-Term Memory (CLSTM) model, trained on the features to classify signals effectively. (3) Model refinement uses SHAP (SHapley Additive exPlanation) values for pruning nonessential features, thereby simplifying the model and reducing data dimensionality. (4) CM interpretation develops a system offering insightful explanations of the model’s decision-making process for operators. This framework is rigorously evaluated against five existing fault diagnosis architectures, utilizing two distinct datasets: one involving torque measurements from a robotic arm for safety assessment and another capturing vibration signals from an electric motor with multiple fault types. The results affirm our framework’s superior optimization, reduced training and inference times, and effectiveness in transparently visualizing fault patterns. Full article

During machining, the surface of the machined materials is damaged and tool wear occurs, sometimes even to complete failure. Machining of thin-walled parts is generally cumbersome due to their low structural rigidity. The study deals with the effect of the feed rate and the thickness of the thin-walled part on the dynamic behavior and stability of the turning process during the roughing and finishing of thin-walled tubular workpieces made of steel alloy 42CrMo4. At the same time, the cutting forces and deformations of the workpiece were also evaluated via numerical and experimental approaches. The numerical study is based on a three-dimensional (3D) finite element model (FEM) developed using the ABAQUS/Explicit frame. In the model, the workpiece material is governed by the behavior law of Johnson–Cook. Numerical and experimental results show that the cutting forces and the quality of the machined surface depend not only on the choice of cutting parameters but also on the dynamic behavior of thin-walled parts due to their low rigidity and low structural damping during the machining operation. Cutting forces are proportional to the feed rate and inversely proportional to the thickness of the part. Their variations around the average values are low for roughing tests where the wall-part thickness is higher or equal to 3.5 mm. However, these variations intensify for finishing tests where the wall thickness is less or equal to 1.5 mm. Indeed, the recorded FFT spectra for a finishing operation show several harmonics that occurred at around 550 Hz, and the amplitude of the peaks, which describes the level of power contained in the signals, shows an increase similar to that of the amplitudes of the temporal signal. The flexibility of the part generates instability in the cutting process, but the frequencies of the vibrations are higher than the frequency of rotation of the part. Full article

In this paper, the cooling performance of oil-cooling PMSM with hairpin winding under various oil parameters is analyzed via a simulation and an experiment. The effects of oil jet positions, oil temperatures, and oil flow rates on the cooling performance are analyzed. It is found that increasing the oil temperature in the range of 20 °C to 60 °C, increasing the flow rate of oil jets whose position angle is from 15° to 45°, and increasing the flow rate in the range of 1 L/min to 2 L/min will significantly improve the cooling performance. The apertures of the oil spray ring are optimized using the Taguchi algorithm. The cooling performance is the best when the flow ratio is m(0°):m(15°):m(30°):m(45°):m(60°):m(75°) = 4%:19%:10%:10%:4%:4%. This study provides a guide for the design of the oil-cooling system for the hairpin winding of the PMSM. Full article

The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicle localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains. Full article