Results 111 to 120 of about 1,838,834 (287)

An Analytical and Numerical Study of Wear Distribution on the Combine Harvester Header Platform: Model Development, Comparison, and Experimental Validation

Lubricants
The header platform of a combine harvester is subjected to severe abrasive and corrosive wear from rice stalks and environmental factors, which significantly limits its service life and operational efficiency.
Honglei Zhang, Zhong Tang, Liquan Tian, Tiantian Jing, Biao Zhang   +4 more
doaj   +1 more source

Rational Tuning of Hygroscopic Oscillation of Stacked Nanoflake Assemblies for Continuous Ambient Energy Harvesting

Advanced Materials, EarlyView.
Stacked nanoflake assembly (SNA) membranes can oscillate autonomously, offering opportunities for soft actuation and energy harvesting. This work uncovers the physical mechanism behind the sustained oscillation of SNA membranes in gradient humidity and identifies three governing dimensionless parameters, enabling rational design for optimizing SNA ...
Zijing Zhang, Jimin Fu, Gesa Zhang, Zhihua Liang, Boxun Zhang, Haimin Yao   +5 more
wiley   +1 more source

Data-driven wear prediction method for complex engineering structures

Frontiers in Mechanical Engineering
Predicting the evolution of wear in metallic structural components is vital for accurately estimating the lifetime of engineering equipment. However, this remains a significant challenge due to the prohibitively large number of cycles required for ...
Haibo Zhang, Qingyuan Zhao, Yingxin Zhao, Baiyang Zhao, Baiyang Zhao, Meng Zhao, Chuang Liu   +6 more
doaj   +1 more source

Enhancing Tool Wear Prediction Accuracy Using Walsh-Hadamard Transform, DCGAN and Dragonfly Algorithm-Based Feature Selection. [PDF]

Sensors (Basel), 2023
Shah M, Borade H, Sanghavi V, Purohit A, Wankhede V, Vakharia V.   +5 more
europepmc   +1 more source

A Geometrically Transient Platform for Bioelectronic Implants

Advanced Materials, EarlyView.
Minimally invasive bioelectronic implants often compromise performance for smaller sizes. To resolve this optimization dilemma, a wireless bioelectronic implant with a transient geometry is introduced (MiFi). The origami‐inspired device miniaturizes up to sixfold for syringe insertion and autonomously unfolds post‐implantation.
Selin Olenik, John D. Goodwin, Atharv Naik, Philip Coatsworth, Ioi Chit Cheung, Farbod Amirghasemi, Anies Sohi, Ehsan Abedi, Tayeb Tadlaoui, Maral P. S. Mousavi, Andriy S. Kozlov, Victoria Salem, Andrew S. Cowburn, Firat Guder   +13 more
wiley   +1 more source

平底从动件盘型凸轮机构系统磨损的数值仿真

Jixie chuandong, 2000
Wear life prediction, wear condition monitoring and op timal design of the parameters are three difficulties in wear studies. Based on the discrete mathematics and computer science,a numerical simulation model of a rubbing system of plat- bottomed cam ...
李宝良, 江亲瑜, 葛宰林
doaj  

Tool Wear Prediction When Machining with Self-Propelled Rotary Tools. [PDF]

Materials (Basel), 2022
Umer U, Mian SH, Mohammed MK, Abidi MH, Moiduddin K, Kishawy H.   +5 more
europepmc   +1 more source

Can Elastomers Combine Stiffness, Toughness and Fatigue Resistance?

Advanced Materials, EarlyView.
This review studies how the molecular and macroscopic architecture of elastomers influence their mechanical properties including: stiffness, stretchability, toughness, fatigue resistance, and damping behavior. By linking the structure to performance, it proposes design principles for advanced elastomeric systems that combine mechanical properties ...
Eva Baur, Esther Amstad
wiley   +1 more source

Understanding the Lubrication and Wear Behavior of Agricultural Components Under Rice Interaction: A Multi-Scale Modeling Study

Lubricants
This study investigates the tribological behavior and wear mechanisms of Q235 steel components subjected to abrasive interaction with rice, a critical challenge in agricultural machinery performance and longevity.
Honglei Zhang, Zhong Tang, Xinyang Gu, Biao Zhang   +3 more
doaj   +1 more source

Local-feature and global-dependency based tool wear prediction using deep learning. [PDF]

Sci Rep, 2022
Yang C, Zhou J, Li E, Wang M, Jin T.
europepmc   +1 more source