Strength and Hardness Assessment of Copper and Copper Alloy Coatings on Stainless Steel Substrates
Bibliographic record
Abstract
Coatings are extensively used in many areas including industrial and medical fields to serve various functions as corrosion resistance, wear resistance and antibacterial purposes. Copper and copper alloys are among the most widely applied coating materials for several industrial and medical applications. One of their widely used copper coating applications is in the antibacterial coating area. Most of the research done in this field focuses on the antibacterial behavior with no comprehensive assessment regarding their mechanical properties, such as hardness and adhesion strength. In this work, mechanical assessment of strength and hardness of pure copper and several copper alloys including Cu Sn5% P0.6%, Cu Ni18 Zn14 (German silver), and Cu Al9 Fe1 are studied experimentally and numerically. All coatings are deposited on stainless steel substrate disks of 25mm diameter by wire-arc thermal spraying at the center of advanced coating technologies, University of Toronto. All coatings are 150 microns in thickness, with two additional thicknesses up to 350 microns for Cu Ni18 Zn14 (German silver) and Cu Al9 Fe1. The effect of the coating thickness and composition on the mechanical properties is studied for all the copper and copper alloy samples with the varying thicknesses between 150 and 350 microns. Scanning Electron Microscope (SEM) is used to study the surface as well as the cross-sectional microstructure of the coatings. Vickers micro-indentation tests are used to evaluate hardness at various locations on the cross-section of the coating and the substrate. This is used to evaluate the effect of the deposition of the coating material, and the subsequent solidification, on the hardness of the coating layer as well as the substrate near the coating interface. Pull-off adhesion tests are performed to evaluate the effect of the coating composition and thickness on the strength of the coatings. Tests are carried out to compute the pull-off failure stress that causes the delamination between the coating and the substrate. Computational analysis will be used to calibrate the experimental data when available by means of finite element analysis. The preliminary pull-off tests show interesting results as the samples with lower coating thicknesses exhibit delamination at higher strengths. This is due to the increase in residual stresses in higher thicknesses building up during the deposition process. Some of the samples did not even fail at lower thicknesses of 150 microns. A comprehensive analysis between the adhesion strength and hardness will be very useful in understanding the effect of coating composition and thickness on the mechanical properties of the coating.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".