Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Aiming to address the wear and replacement inconvenience of concrete mixer liners, this study utilizes a laser cladding system to clad Fe60 alloy powder on the liner. It investigates the influence of different process parameters on the forming quality of the Fe60 alloy powder cladding layer. The optimal process parameters were obtained by weighted comprehensive evaluation, and single-layer multi-pass cladding experiments were carried out under the optimal process parameters to investigate the effects of a 30%, 40%, and 50% lap rate on the surface flatness and forming quality of the cladding layer. Using a metallographic microscope, a scanning electron microscope analysis of the macro morphology and microstructure of the cladding layer was conducted, a DPT-5 penetration flaw detector was used to observe the cracks on the surface of the multi-channel cladding, a microhardness tester and friction and wear experimental machine were used for the hardness of the cladding layer, and an abrasive wear resistance test was conducted. The results show that under the process parameters of a laser power of 900 W, powder feeding speed of 7 g/min, scanning speed of 600 mm/min, and 50% lap rate, the average microhardness of the fused cladding layer reaches 742 HV, which is 1.8 times higher than that of the liner plate, and the coefficient of friction is 0.57, which improves the liner plate's wear resistance performance and service life.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11547055PMC
http://dx.doi.org/10.3390/ma17215158DOI Listing

Publication Analysis

Top Keywords

process parameters
20
cladding layer
20
cladding
9
laser cladding
8
mixer liners
8
fe60 alloy
8
alloy powder
8
forming quality
8
optimal process
8
50% lap
8

Similar Publications

Purpose: Real‑time magnetic resonance-guided radiation therapy (MRgRT) integrates MRI with a linear accelerator (Linac) for gating and adaptive radiotherapy, which requires robust image‑quality assurance over a large field of view (FOV). Specialized phantoms capable of accommodating this extensive FOV are therefore essential. This study compares the performance of four commercial MRI phantoms on a 0.

View Article and Find Full Text PDF

Introduction: The role of imaging in radiotherapy is becoming increasingly important. Verification of imaging parameters prior to treatment planning is essential for safe and effective clinical practice.

Methods: This study described the development and clinical implementation of ImageCompliance, an automated, GUI-based script designed to verify and enforce correct CT and MRI parameters during radiotherapy planning.

View Article and Find Full Text PDF

Structural biology is fundamental to understanding the molecular basis of biological processes. While machine learning-based protein structure prediction has advanced considerably, experimentally determined structures remain indispensable for guiding structure-function analyses and for improving predictive modeling. However, experimental studies of protein complexes continue to pose challenges, particularly due to the necessity of high protein concentrations and purity for downstream analyses such as cryogenic electron microscopy.

View Article and Find Full Text PDF

Microgravity experiments on board the International Space Station, combined with particle-resolved direct numerical simulations, were conducted to investigate the long-term flocculation behavior of clay suspensions in saline water in the absence of gravity. After an initial homogenization of the suspensions, different clay compositions were continuously monitored for 99 days, allowing a detailed analysis of aggregate growth through image processing. The results indicate that the onboard oscillations (g-jitter) may have accelerated the aggregation process.

View Article and Find Full Text PDF

Spiking neural networks (SNNs) inherently rely on the timing of signals for representing and processing information. Augmenting SNNs with trainable transmission delays, alongside synaptic weights, has recently shown to increase their accuracy and parameter efficiency. However, existing training methods to optimize such networks rely on discrete time, approximate gradients, and full access to internal variables such as membrane potentials.

View Article and Find Full Text PDF