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Bearings are critical components used to support loads and facilitate motion for rotating and sliding parts of the machinery. Bearing malfunctions can cause catastrophic failures. Hence, failure analysis and endeavors to improve bearing performance are essential discussions for worldwide designers, manufacturers and end users of vital machinery. This study aims to investigate a type of roller bearing from the railway industry with premature failures. The task arises because locomotives’ maintenance and service life quality are vital to railway operations while providing transportation services for the nation. To assist in maintaining the designated locomotives, the present study scrutinizes the causes of failure of heavy-duty roller bearings from locomotive bogie axleboxes.

It is intended to inspect this bearing service life and statistically scrutinize its design parameters to reveal the failures’ shortcomings and origins. The significant measures include examinations of their failures’ primary and vital factors by comparing them with a real-life service history of 16 roller bearings of the same type. The bearings come from the axleboxes of a locomotive bogie with an axle load of 20 tons. The bearing loads are estimated using the EN13104 standard document and confirmed by the finite element method using ABAQUS engineering software. To validate the finite element modeling results, the bearings’ stress analysis is performed using the Hertzian contact theory that demonstrated perfect conformity. The said methods are also used to search for the areas susceptible to failures in these bearings. With the inclusion and exploitation of the bearing maintenance conditions and the logbook recordings of the locomotives for the past seven years, the critical cause for this type of bearing’s failures is surveyed and discussed.

With the inclusion and exploitation of the bearing maintenance conditions and the logbook recordings of the locomotives for the past seven years, the critical cause for this type of bearing’s failures is surveyed and discussed. As a crucial result, it is found that deprived maintenance and inadequate lubrication are the root causes of the loss of the selected bearings.

For the designated locomotives, the origins of the heavy-duty roller bearing failures and its design shortcomings are revealed by examining and comparing them with a real-life service history of many of the same types of bearings. The novelty of the research is in using the combination of the methods mentioned above and its decent outcome.

This study aims to design and validate a theoretical model for capacitive imaging (CI) sensors that incorporates the interelectrode shielding and surrounding shielding electrodes. Through experimental verification, the effectiveness of the theoretical model in evaluating CI sensors equipped with shielding electrodes has been demonstrated.

The study begins by incorporating the interelectrode shielding and surrounding shielding electrodes of CI sensors into the theoretical model. A method for deriving the semianalytical model is proposed, using the renormalization group method and physical model. Based on random geometric parameters of CI sensors, capacitance values are calculated using both simulation models and theoretical models. Three different types of CI sensors with varying geometric parameters are designed and manufactured for experimental testing.

The study’s results indicate that the errors of the semianalytical model for the CI sensor are predominantly below 5%, with all errors falling below 10%. This suggests that the semianalytical model, derived using the renormalization group method, effectively evaluates CI sensors equipped with shielding electrodes. The experimental results demonstrate the efficacy of the theoretical model in accurately predicting the capacitance values of the CI sensors.

The theoretical model of CI sensors is described by incorporating the interelectrode shielding and surrounding shielding electrodes into the model. This comprehensive approach allows for a more accurate evaluation of the detecting capability of CI sensors, as well as optimization of their performance.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

This paper aims to develop a specific type of mobile nonrigid support friction stir welding (FSW) robot, which can adapt to aluminum alloy trucks for rapid online repair.

The friction stir welding robot is designed to complete online repair according to the surface damage of large aluminum alloy trucks. A rotatable telescopic arm unit and a structure for a cutting board in the shape of a petal that was optimized by finite element analysis are designed to give enough top forging force for welding to address the issues of inadequate support and significant deformation in the repair process.

The experimental results indicate that the welding robot is capable of performing online surface repairs for large aluminum alloy trucks without rigid support on the backside, and the welding joint exhibits satisfactory performance.

Compared with other heavy-duty robotic arms and gantry-type friction stir welding robots, this robot can achieve online welding without disassembling the vehicle body, and it requires less axial force. This lays the foundation for the future promotion of lightweight equipment.

The designed friction stir welding robot is capable of performing online repairs without dismantling the aluminum alloy truck body, even in situations where sufficient upset force is unavailable. It ensures welding quality and exhibits high efficiency. This approach is considered novel in the field of lightweight online welding repairs, both domestically and internationally.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

The purpose of this study is to evaluate service robots as an alternative service provider that can reduce customers’ social discomfort in hospitality service encounters. Specifically, the authors discuss when and in what scenarios service robots can alleviate such social discomfort and explain this effect from the perspective of dehumanization.

Following a social constructivist paradigm, the authors adopt a qualitative research design, gathering data through 21 semistructured interviews to explore why the presence of service employees causes customers’ social discomfort in hospitality service encounters and how service robots alleviate such discomfort.

This study’s results suggest that both the active and passive engagement of service employees are sources of customers’ social discomfort in hospitality service encounters; thus, adopting service robots can help reduce such discomfort in some scenarios. Customers’ differentiating behaviors, a downstream effect of social discomfort, are also addressed.

Service robots can reduce customers’ social discomfort in certain scenarios and influence their consumption behaviors. This finding offers actionable insights regarding the adoption of service robots in hospitality service encounters.

This research enhances the understanding of social discomfort in hospitality service encounters and expands the research on service robots. To the best of the authors’ knowledge, it is the first attempt to reveal the bright side of robots in service encounters from a dehumanization perspective.

The number of people with special needs, including citizens and military personnel, has increased as a result of terrorist attacks and challenging conditions in Iraq and other countries. With almost 80% of the world’s amputees having below-the-knee amputations, Iraq has become a global leader in the population of amputees. Important components found in lower limb prostheses include the socket, pylon (shank), prosthetic foot and connections.

There are two types of prosthetic feet: articulated and nonarticulated. The solid ankle cushion heel foot is the nonarticulated foot that is most frequently used. The goal of this study is to use a composite filament to create a revolutionary prosthetic foot that will last longer, have better dorsiflexion and be more stable and comfortable for the user. The current study, in addition to pure polylactic acid (PLA) filament, 3D prints test items using a variety of composite filaments, such as PLA/wood, PLA/carbon fiber and PLA/marble, to accomplish this goal. The experimental step entails mechanical testing of the samples, which includes tensile testing and hardness evaluation, and material characterization by scanning electron microscopy-energy dispersive spectrometer analysis. The study also presents a novel design for the nonarticulated foot that was produced with SOLIDWORKS and put through ANSYS analysis. Three types of feet are produced using PLA, PLA/marble and carbon-covered PLA/marble materials. Furthermore, the manufactured prosthetic foot undergoes testing for dorsiflexion and fatigue.

The findings reveal that the newly designed prosthetic foot using carbon fiber-covered PLA/marble material surpasses the PLA and PLA/marble foot in terms of performance, cost-effectiveness and weight.

To the best of the author’s knowledge, this is the first study to use composite filaments not previously used, such as PLA/wood, PLA/carbon fiber and PLA/marble, to design and produce a new prosthetic foot with a longer lifespan, improved dorsiflexion, greater stability and enhanced comfort for the patient. Beside the experimental work, a numerical technique specifically the finite element method, is used to assess the mechanical behavior of the newly designed foot structure.

Increasing personnel costs and labour shortages have pushed retailers to give increasing attention to their intralogistics operations. We study hybrid order picking systems, in which humans and robots share work time, workspace and objectives and are in permanent contact. This necessitates a collaboration of humans and their mechanical coworkers (cobots).

Through a longitudinal case study on individual-level technology adaption, we accompanied a pilot testing of an industrial truck that automatically follows order pickers in their travel direction. Grounded on empirical field research and a unique large-scale data set comprising N = 2,086,260 storage location visits, where N = 57,239 storage location visits were performed in a hybrid setting and N = 2,029,021 in a manual setting, we applied a multilevel model to estimate the impact of this cobot settings on task performance.

We show that cobot settings can reduce the time required for picking tasks by as much as 33.57%. Furthermore, practical factors such as product weight, pick density and travel distance mitigate this effect, suggesting that cobots are especially beneficial for short-distance orders.

Given that the literature on hybrid order picking systems has primarily applied simulation approaches, the study is among the first to provide empirical evidence from a real-world setting. The results are discussed from the perspective of Industry 5.0 and can prevent managers from making investment decisions into ineffective robotic technology.

In response to the COVID-19 pandemic, many employees had to switch to remote work. While some adjusted successfully to this transition, others have struggled. Leveraging information systems (IS) to adjust to major exogenous shocks is called digital resilience. The purpose of this paper is to understand what we can learn about employees' digital resilience from externally enforced transitions to remote work.

As digital resilience is challenging to measure, this study uses an embedded mixed methods approach. The authors conducted a qualitative analysis of 40 employees' statements on their remote work experience during the first six months of the pandemic and complemented these findings with scale-based digital resilience scores.

The authors find that employees' digital resilience largely depends on the amount of technical equipment and support they receive from their organizations as well as their ability and willingness to learn how to adequately use and communicate through information and communication technologies. Being self-disciplined and self-responsible positively affects digital resilience, while social isolation threatens it. Organizations can foster digital resilience building by encouraging digital networking, building a digital culture and netiquette, and treating digital resilience as a sociotechnical phenomenon.

This is one of the first empirical studies of digital resilience on a human level. It sheds light on the missing link between IS-enabled resilience and transitions to remote work. Specifically, it provides original insights into its development and manifestation in a remote work context during the COVID-19 pandemic. For researchers, it provides novel guidance on choosing appropriate measurement instruments to capture digital resilience.