Haijie Wang, Xintian Liu, Que Wu, Xiaolan Wang and Yansong Wang
The purpose of this paper is to obtain a more accurate fatigue life of structures by introducing the surface roughness into fatigue life prediction model.
Abstract
Purpose
The purpose of this paper is to obtain a more accurate fatigue life of structures by introducing the surface roughness into fatigue life prediction model.
Design/methodology/approach
Based on the fatigue life prediction model with surface roughness correction, the shock absorber cylinder is taken as an example to verify the feasibility of the improved method. Based on the load of the shock absorber cylinder during driving, fatigue experiments are performed under longitudinal and lateral forces, respectively. Then, the fatigue life predicted by the modified model is compared with that predicted by the traditional model.
Findings
By comparing with the test results, considering the influence of mean stress, the Manson method is more accurate in life prediction. Then, the modified Manson-Coffin and Manson method with surface roughness is more accurate in life prediction under longitudinal force and lateral forces, respectively. This verifies the feasibility of the improved method with the surface roughness.
Originality/value
The research on the influence of surface roughness on fatigue life can lay the technical foundation for the life prediction of products and have great significance to the quality evaluation of products.
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Keywords
Dimensional quality of sheet metal assemblies is an important factor for the final product. However, the part tolerance is not easily controlled because of the spring back…
Abstract
Purpose
Dimensional quality of sheet metal assemblies is an important factor for the final product. However, the part tolerance is not easily controlled because of the spring back deformation during the stamping process. Selective assembly is a means to decrease assembly tolerance of the assembly from low-precision components. Therefore, the purpose of this paper is to propose a fully efficient method of selective assembly optimization based on an improved genetic algorithm for optimization toolbox (IGAOT) in MATLAB.
Design/methodology/approach
The method of influence coefficient is first applied to calculate the assembly variation of sheet metal components since the traditional rigid assembly variation model cannot be used due to welding deformation. Afterwards, the IGAOT is proposed to generate optimal selective groups, which consists of advantages of genetic algorithm for optimization toolbox (GAOT) and simulated annealing.
Findings
The cases of two simple planes and the tail lamp bracket assembly are used to illustrate the flowchart of optimizing combinations of selective groups. These cases prove that the proposed IGAOT has better precision than that of GAOT with the same parameters for selective assembly.
Originality/value
The research objective of this paper is to evaluate the changes from rigid bodies to sheet metal parts which are very complex for selective assembly. The method of IGAOT was proposed to the selected groups which has better precision than that of current optimization algorithms.
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Shuangshuang Li, Xintian Liu, Xiaolan Wang and Yansong Wang
During the running of automobile, the stabilizer bar is frequently subjected to the impact of complex random loads, which is prone to fatigue failure and accident. In regard to…
Abstract
Purpose
During the running of automobile, the stabilizer bar is frequently subjected to the impact of complex random loads, which is prone to fatigue failure and accident. In regard to this, the purpose of this paper is to study and discuss fatigue life of automobile stabilizer bar.
Design/methodology/approach
Durability bench test shows that failure is located at the joint of sleeve and stabilizer bar body. Based on the collection and compilation of micro-strain load spectrum of the stabilizer bar, the strain-life model is studied considering the influence of average stress and maximum stress at failure area. Seven-grade strain-life curves of the stabilizer bar are established. According to the principle of linear damage accumulation, the relationship between fatigue life and damage is discussed, then the fatigue life of stabilizer bar is predicted. Fatigue life evaluation is carried out from three aspects: reliability analysis, static analysis and fatigue life simulation.
Findings
The results show that the reliability of the test sample is 99.9 percent when the confidence is 90 percent and the durability is 1,073 load spectrum cycles; the ratios of predicted and simulated life to design life are 2.77 and 2.30, respectively.
Originality/value
Based on the road load characteristics of automobile stabilizer bar, the method of fatigue life prediction and evaluation is discussed, which provides a basis for the design and development of automobile chassis components.
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Xintian Liu, Que Wu, Shengchao Su and Yansong Wang
The properties of materials under impact load are introduced in terms of metal, nonmetallic materials and composite materials. And the application of impact load research in…
Abstract
Purpose
The properties of materials under impact load are introduced in terms of metal, nonmetallic materials and composite materials. And the application of impact load research in biological fields is also mentioned. The current hot research topics and achievements in this field are summarized. In addition, some problems in theoretical modeling and testing of the mechanical properties of materials are discussed.
Design/methodology/approach
The situation of materials under impact load is of great significance to show the mechanical performance. The performance of various materials under impact load is different, and there are many research methods. It is affected by some kinds of factors, such as the temperature, the gap and the speed of load.
Findings
The research on mechanical properties of materials under impact load has the characteristics as fellow. It is difficult to build the theoretical model, verify by experiment and analyze the data accumulation.
Originality/value
This review provides a reference for further study of material properties.
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Zhiqiang Liang, Xintian Liu, Wang Yansong and Xiaolan Wang
This study aims to accurately evaluate the influence of various error intervals on the performance of the wiper.
Abstract
Purpose
This study aims to accurately evaluate the influence of various error intervals on the performance of the wiper.
Design/methodology/approach
The wiper structural system is decomposed into classical four-link planar for kinematics analysis, and it was modeled respectively by using interval method, universal grey number theory and enumeration approach depending on the nature of uncertainty.
Findings
The universal grey number theory is a viable methodology for the accurate analysis of uncertain structural system.
Originality/value
(1) The model of uncertain wiper structural system is established. (2) Universal grey number theory and new parameters are adopted to analyze the presence of uncertain wiper structural system. (3) Comparative analysis of response quantities is obtained by interval method, universal grey number theory and enumeration method.
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The purpose of this paper is to propose a new assembly variation analysis model to analyze assembly variation for sheet metal parts. The main focus is to analyze assembly…
Abstract
Purpose
The purpose of this paper is to propose a new assembly variation analysis model to analyze assembly variation for sheet metal parts. The main focus is to analyze assembly processes based on the method of power balance.
Design/methodology/approach
Starting with issues in assembly variation analysis, the review shows the critical aspects of tolerance analysis. The method of influence coefficient (MIC) cannot accurately analyze the relationship between part variations and assembly variations, as the welding point is not a point but a small area. Therefore, new sensitivity matrices are generated based on the method of power balance.
Findings
Here two cases illustrate the processes of assembly variation analysis, and the results indicate that new method has higher accuracy than the MIC.
Research limitations/implications
This study is limited to assembly variation analysis for sheet metal parts, which can be used in auto-body and airplane body.
Originality/value
This paper provides a new assembly variation analysis based on the method of power balance.
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Zirui Zeng, Junwen Xu, Shiwei Zhou, Yufeng Zhao and Yansong Shi
To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of…
Abstract
Purpose
To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance.
Design/methodology/approach
A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm.
Findings
To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact.
Practical implications
This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts.
Originality/value
The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.
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Danqing Fang, Chengjin Wu, Yansong Tan, Xin Li, Lilan Gao, Chunqiu Zhang and Bingjie Zhao
The paper aims to study the effect of sintering temperature on the microstructure, shear strength and ratcheting fatigue life of nanosilver sintered lap shear joint. In addition…
Abstract
Purpose
The paper aims to study the effect of sintering temperature on the microstructure, shear strength and ratcheting fatigue life of nanosilver sintered lap shear joint. In addition, the Gerber model is used to predict the ratcheting fatigue lives of nanosilver sintered lap shear joints at different sintering temperatures.
Design/methodology/approach
In this paper, the nanosilver sintered lap shear joints were prepared at three sintering temperatures of 250 °C, 280 °C and 310 °C. The bonding quality was characterized by scanning electron microscopy, X-ray diffraction, transmission electron microscope and shear tests, and the long-term reliability was studied by conducting ratcheting fatigue tests. In addition, three modified models based on Basquin equation were used to predict the ratcheting fatigue life of nanosilver sintered lap shear joint and their accuracies were evaluated.
Findings
When the sintering temperature is 250°C, the nanosilver sintered lap shear joint shows the porosity of 22.9 ± 1.6 %, and the shear strength of 22.3 ± 2.4 MPa. Raising the sintering temperature enhances silver crystallite size, strengthens sintering necks, thus improves shear strength and ratcheting fatigue life in joints. In addition, the ratcheting fatigue lives of the joints sintered at different temperatures are effectively predicted by three equivalent force models, and the Gerber model shows the highest life prediction accuracy.
Research limitations/implications
The sintered silver bondline is suffering a complex stress state. The study only takes the shear stress into consideration. The tensile stress and the combination of shear stress and tensile stress can to be considered in the future study.
Practical implications
The paper provides the experimental and theoretical support for robust bonding and long-term reliability of sintered silver structure.
Social implications
The introduced model can predict the ratcheting fatigue lives of the joints sintered at different temperatures, which shows a potential in engineering applications.
Originality/value
The study revealed the relationship between the sintering temperature and the microstructure, the shear strength and the ratcheting fatigue life of the joint. In addition, the Gerber model can predict the ratcheting fatigue life accurately at different sintering temperatures.
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Keywords
Xuemei Li, Yuyu Sun, Yansong Shi, Yufeng Zhao and Shiwei Zhou
Accurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote…
Abstract
Purpose
Accurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote sustainable transportation development.
Design/methodology/approach
This paper introduces a novel self-adaptive grey multivariate prediction modeling framework (FARDCGM(1,N)) to forecast port cargo throughput in China, addressing the challenges posed by mutations and time lag characteristics of time series data. The model explores policy-driven mechanisms and autoregressive time lag terms, incorporating policy dummy variables to capture deviations in system development trends. The inclusion of autoregressive time lag terms enhances the model’s ability to describe the evolving system complexity. Additionally, the fractional-order accumulative generation operation effectively captures data features, while the Grey Wolf Optimization algorithm determines optimal nonlinear parameters, enhancing the model’s robustness.
Findings
Verification using port cargo throughput forecasts for FTZs in Shanghai, Guangdong and Zhejiang provinces demonstrates the FARDCGM(1,N) model’s remarkable accuracy and stability. This innovative model proves to be an excellent forecasting tool for systematically analyzing port cargo throughput under external interventions and time lag effects.
Originality/value
A novel self-adaptive grey multivariate modeling framework, FARDCGM(1,N), is introduced for accurately predicting port cargo throughput, considering policy-driven impacts and autoregressive time-lag effects. The model incorporates the GWO algorithm for optimal parameter selection, enhancing adaptability to sudden changes. It explores the dual role of policy variables in influencing system trends and the impact of time lag on dynamic response rates, improving the model’s complexity handling.
Details
Keywords
Yansong Tan, Xin Li, Xu Chen, Zhenwen Yang and Guo-Quan Lu
This paper aims to use nano-silver paste to design a new bonding method for super-large-area direct-bonded-aluminum (DBA) plates. It compared several frequently used bonding…
Abstract
Purpose
This paper aims to use nano-silver paste to design a new bonding method for super-large-area direct-bonded-aluminum (DBA) plates. It compared several frequently used bonding methods and proved the feasibility of an optimized low-pressure-assisted double-layer-printed silver sintering technology for large-area bonding to increase the thermal conductivity of power electronic modules with high junction temperature, higher power density and higher reliability.
Design/methodology/approach
The bonding profile was optimized by using transparent glasses as substrates. Thus, the bonding qualities could be directly characterized by optical observation. After sintering, the bonded DBA samples were characterized by nondestructive X-ray computed tomography system, scanning electron microscopy equipped with an energy dispersive spectrometer. Finally, bonding stress evolution was characterized by shear tests.
Findings
Low-pressure-assisted large-area double-layer-printed bonding process consisting of six-step was successfully developed to bond DBA substrates with the size of 50.8 × 25.4 mm. The thickness of the sintered-silver bond-line was between 33 and 74 µm with the average porosity of 12.5 per cent. The distribution of shear strength along the length of DBA/DBA bonded sample was from 9.7 to 18.8 MPa, with average shear strength of 15.5 MPa. The typical fracture primarily propagated in the sintered-silver layer and partially along the Ni layer.
Research limitations/implications
The bonding stress needs to be further improved. Meanwhile, the thermal and electrical properties are encouraged to test further.
Practical implications
If nano-silver paste can be used as thermal interfacial material for super-large-area bonding, the thermal performance will be improved.
Social implications
The paper accelerated the use of nano-silver paste for super-large-area DBA bonding.
Originality/value
The proposed bonding method greatly decreased the bonding pressure.