Search results

The purpose of this study is to reduce the cracks, pores and unfused defects in arc welding, improve the crystalline structure of the weld, refine its grains and improve the mechanical properties.

Taking E690 marine steel as the research object, the experiment adopts a new process method of laser forging coupled arc welding. Welding for comparative experiments. Experiments show that the “V”-shaped groove arc welding process has a larger fusion area, but has pores, the arc current is 168 A, the arc voltage is 28 V and the welding speed is 600 mm/min.

It can be seen from tensile tests that the coupling welding process has the highest tensile strength and yield strength, 872 MPa and 692 MPa, respectively, and the fracture elongation is 29.29%. The single-beam laser forging coupled arc welding process has a distance of laser and wire of 6–8 mm, a laser wavelength of 1,064 nm and the highest weld fusion ratio. The microhardness test shows that the average hardness of single-beam laser forging in the weld zone is 487.54 HV, which is 10.30% higher than that of arc welding. The average hardness in the fusion zone is 788.08 HV, which is 14.52% higher than that of the arc welding process.

The originality of the experiment: proposed a new process method of coupling arc repair for offshore steel forging; adopted a new process method of simultaneous coupling of single-beam short-pulse laser, double-beam short-pulse laser and arc welding; and obtained effect of pulsed laser and arc composite repair on porosity and fusion of E690 marine steel welds.

This paper aims to investigate the effect of laser energy density on the fretting wear properties of TC4 alloys prepared by selective laser melting to enhance their service life.

The friction test used a ball-to-plane contact structure and simulated the actual working conditions by applying a linear reciprocating motion. In the experiments, the authors prepared TC4 alloys using different laser energy densities and performed fretting wear tests with D = 100 µm on a friction machine, as well as comparative analyses with conventional cast TC4 materials. 3D optical microscope, scanning electron microscopy and energy spectrum x-ray spectrometer were used to analyze the surface morphology and elemental content of the samples.

The results indicate that the friction coefficient and wear volume of selective laser melting (SLM)-TC4 are lower than those of cast TC4, demonstrating superior wear resistance. As the laser energy density increases, the grain size of the SLM-TC4 samples gradually coarsens, leading to an initial improvement followed by a decline in friction coefficient and wear resistance. Among the samples, ET3 (42 J/mm³) exhibits the lowest friction coefficient and wear volume, with the friction coefficient ranging from 0.67 to 0.7 and the wear volume being only 9.92% of that of cast TC4.

The main contribution of this work is to provide a new reference for improving the wear resistance of TC4 prepared by selective laser melting. By studying the effect of different laser energy densities on the wear resistance of TC4, it provides a theoretical basis for the preparation of SLM-TC4 alloys for industrial applications.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0414/

This study aims to address the challenge of automatic guided vehicle (AGV) scheduling for parcel storage and retrieval in an intelligent warehouse.

This study presents a scheduling solution that aims to minimize the maximum completion time for the AGV scheduling problem in an intelligent warehouse. First, a mixed-integer linear programming model is established, followed by the proposal of a novel genetic algorithm to solve the scheduling problem of multiple AGVs. The improved algorithm includes operations such as the initial population optimization of picking up goods based on the principle of the nearest distance, adaptive crossover operation evolving with iteration, mutation operation of equivalent exchange and an algorithm restart strategy to expand search ability and avoid falling into a local optimal solution. Moreover, the routing rules of AGV are described.

By conducting a series of comparative experiments based on the actual package flow situation of an intelligent warehouse, the results demonstrate that the proposed genetic algorithm in this study outperforms existing algorithms, and can produce better solutions for the AGV scheduling problem.

This paper optimizes the different iterative steps of the genetic algorithm and designs an improved genetic algorithm, which is more suitable for solving the AGV scheduling problem in the warehouse. In addition, a path collision avoidance strategy that matches the algorithm is proposed, making this research more applicable to real-world scheduling environments.

The affordable loss (AL) heuristic, as one crucial sub-dimension of effectuation, delineates the maximum level of investment entrepreneurs are ready to lose in a worst-case scenario. Conflicting conceptualizations remain regarding whether entrepreneurs’ psychological traits matter for AL. Based on the narcissistic admiration and narcissistic rivalry perspective, this study investigates the relationship between chief executive officer (CEO) narcissism and AL behaviors.

Using data collected from the CEOs and paired vice presidents at 122 small and medium enterprises (SMEs) in mainland China, the authors intend to further explore the association between psychological traits, especially CEO narcissism and AL behaviors under environment and resource constraints (e.g. perceived uncertainty and slack resources).

The findings show that CEO admiration-based narcissism is positively related to AL behaviors in the firm. Furthermore, when firms hold more slack resources, narcissistic admiration has a stronger positive association with AL; while when the environment becomes more uncertain, narcissistic admiration has a weaker positive association with AL. In contrast, CEO rivalry-based narcissism is negatively related to AL behaviors in the firm. When the environment becomes more uncertain, narcissistic rivalry has a stronger negative association with AL.

This article contributes to trait-based effectuation research and suggests that individual psychological traits affect AL behaviors at the firm level, though the patterns of the relationship vary with both the type of narcissism and contexts.

This study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect..

The machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem.

The simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling.

The prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.

Despite the growing attention being paid to the role of uncertainty in the competitive business environment, few studies have considered uncertainty as an antecedent factor and explored its direct impact on accelerating a firm’s innovation speed. This study develops a conceptual framework that examines the impacts of technological uncertainty and market uncertainty on innovation speed, building on complex adaptive theory. Furthermore, it is important to note that the internal resources of a firm and its external environment are not separate entities. In this study, we investigate the moderating role of a firm's internal and external resource ability (financial constraints level and organizational slack level) in the relationship between environmental uncertainty and innovation speed.

Our data sample is the panel data of China's A-share listed companies. The data year span is from 2000 to 2018. We use a hierarchical regression analysis model.

Our results reveal that both technology uncertainty and market uncertainty can promote innovation speed. Still, a firm’s organizational slack positively moderates the relationship between technology uncertainty and innovation speed, and financial constraints negatively moderate the relationship between demand uncertainty and innovation speed.

Our research contributes to the existing literature on uncertainty and extends its research perspective by no longer taking uncertainty as an environmental factor but exploring its direct impact. Still, our research focuses on innovation speed and discusses the impact of environmental uncertainty (including technology uncertainty and demand uncertainty) on firms’ innovation speed, expanding the limitations of previous research, which usually holds a relatively general perspective on innovation problems.

The present study examines the impact of sustainable transitions on the energy and environmental efficiency (EEE) of nations across the developed and developing world. It studies the temporal shift in the share of renewable sources in energy generation. It also analyses the shift in the efficiency frontier of nations using data envelopment analysis (DEA). Further, it studies the macro-level drivers of EEE in the countries.

As the first step, we benchmark the EEE of the developed and developing nations using DEA. Subsequently, we look at the influence of institutional quality, human capital, R&D and knowledge systems on EEE, to develop a comprehensive understanding of the macro-level drivers of EEE.

Our analyses reveal that a country’s institutional quality, human capital and R&D are critical determinants of EEE. The results show that while human capital has a significant positive impact on EEE, R&D expenditure alone has no substantial impact. The findings also suggest that knowledge diffusion disperses best practices across nations and bridges EEE gaps.

Attempts to promote sustainable energy transitions and improve EEE have met with varying levels of success. The results of this study will provide a useful guideline for the governments to achieve the goal of EEE through sustainable energy transitions (SET).

Unlike previous studies, we adopt a multi-factor EEE assessment. We also examine additional influences like the human capital of a nation and its knowledge management system to develop a comprehensive understanding of the macro-level drivers of EEE.

The purpose of this paper is to improve the mechanical and tribological properties of Si₃N₄ ceramics and to make the application of Si₃N₄ ceramics as tribological materials more extensive.

Si₃N₄-based composite ceramics (SN-2L) containing nitrogen-doped graphene quantum dots (N-GQDs) were prepared by hot press sintering process through adding 2 Wt.% nanolignin as precursor to the Si₃N₄ matrix, and the dry friction and wear behaviors of Si₃N₄-based composite against TC4 disc were performed at the different loads by using pin-on-disc tester.

The friction coefficients and wear rates of SN-2L composite against TC4 were significantly lower than those of the single-phase Si₃N₄ against TC4 at the load range from 15 to 45 N. At higher load of 45 N, SN-2L/TC4 pair presented the lowest friction coefficient of 0.25, and the wear rates of the pins and discs were as low as 1.76 × 10⁻⁶ and 2.59 × 10⁻⁴mm³/N·m. The low friction and wear behavior could be attributed to the detachment of N-GQDs from the ceramic matrix to the worn surface at the load of 30 N or higher, and then an effective lubricating film containing N-GQDs, SiO₂, TiO₂ and Al₂SiO₅ formed in the worn surface. While, at the same test condition, the friction coefficient of the single-phase Si₃N₄ against TC4 was at a range from 0.45 to 0.58. The spalling and cracking morphology formed on the worn surface of single-phase Si3N4, and the wear mechanism was mainly dominated by adhesive and abrasive wear.

Overall, a high-performance green ceramic composite was prepared, and the composite had a good potential for application in engineering tribology fields (such as aerospace bearings).

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0161/

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

This study aims to investigate the surface modification on 20CrMnTi gear steel individually treated by diamond-like carbon films and nitride coatings.

For this purpose, the mechanical properties of a-C:H, ta-C and AlCrSiN coatings are characterized by nano-indentation and scratch tests. The friction and wear behaviors of these three coatings are evaluated by ball-on-disc tribological experiments under dry contact conditions.

The results show that the a-C:H coating has the highest coating-substrate adhesion strength (495 mN) and the smoothest surface (Ra is about 0.045 µm) compared with the other two coatings. The AlCrSiN coating shows the highest mean coefficient of friction (COF), whereas the ta-C coating exhibits the lowest one (steady at about 0.16). The carbon-based coatings possess excellent self-lubricating properties compared with nitride ceramic ones, which effectively reduce the COF by about 64%. The major failure mode of carbon-based coatings in dry contact is slight abrasive wear. The damage of AlCrSiN coating is mainly adhesive wear and abrasive wear.

It is suggested that the carbon-based film can effectively improve the friction-reducing and wear resistance performance of the gear steel surface, which has a promising application prospect in the mechanical transmission field.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0129/