Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 3 de 3
Filter
Add more filters










Database
Language
Publication year range
1.
Sensors (Basel) ; 22(22)2022 Nov 11.
Article in English | MEDLINE | ID: mdl-36433321

ABSTRACT

A reverse-offset printed temperature sensor based on interdigitated electrodes (IDTs) has been investigated in this study. Silver nanoparticles (AgNPs) were printed on a glass slide in an IDT pattern by reverse-offset printer. The sensing layer consisted of a sucrose film obtained by spin coating the sucrose solution on the IDTs. The temperature sensor demonstrated a negative temperature coefficient (NTC) with an exponential decrease in resistance as the temperature increased. This trend is the characteristic of a NTC thermistor. There is an overall change of ~2800 kΩ for the temperature change of 0 °C to 100 °C. The thermistor is based on a unique temperature sensor using a naturally occurring biocompatible material, i.e., sucrose. The active sensing material of the thermistor, i.e., sucrose used in the experiments was obtained from extract of Muscovado. Our temperature sensor has potential in the biomedical and food industries where environmentally friendly and biocompatible materials are more suitable for sensing accurately and reliably.


Subject(s)
Metal Nanoparticles , Temperature , Silver , Electrodes , Sucrose
2.
Materials (Basel) ; 15(11)2022 May 26.
Article in English | MEDLINE | ID: mdl-35683087

ABSTRACT

To realize the purpose of energy saving, materials with high weight are replaced by low-weight materials with eligible mechanical properties in all kinds of fields. Therefore, conducting research works on lightweight materials under specified work conditions is extremely important and profound. To understand the relationship of aluminum alloy AA5005 among flow stress, true strain, strain rate, and deformation temperature, hot isothermal tensile tests were conducted within the strain rate range 0.0003-0.03 s-1 and temperature range 633-773 K. Based on the true stress-true strain curves obtained from the experiment, a traditional constitutive regression Arrhenius-type equation was utilized to regress flow behaviors. Meanwhile, the Arrhenius-type equation was optimized by a sixth-order polynomial function for compensating strain. Thereafter, a back propagation artificial neural network (BP-ANN) model based on supervised machine learning was also employed to regress and predict flow stress in diverse deform conditions. Ultimately, by introducing statistical analyses correlation coefficient (R2), average absolute relative error (AARE), and relative error (δ) to the comparative study, it was found that the Arrhenius-type equation will lose accuracy in cases of high stress. Additionally, owning higher R2, lower AARE, and more concentrative δ value distribution, the BP-ANN model is superior in regressing and predicting than the Arrhenius-type constitutive equation.

3.
Materials (Basel) ; 15(4)2022 Feb 16.
Article in English | MEDLINE | ID: mdl-35207997

ABSTRACT

The surface finish is an important characteristic in the incremental sheet forming (ISF) process and is often influenced by numerous factors within the forming process. Therefore, this research was aimed at identifying the optimal forming parameters through the Taguchi method to produce high-quality formed products. The forming tool radius, spindle speed, vertical step increment, and feed rate were chosen as forming parameters in the experimental design, with surface roughness as the response variable. Taguchi L16 orthogonal array design and analysis of variance (ANOVA) test were used to identify the parameter's optimal settings and examine the statistically significant parameters on the response, respectively. Results confirmed that a significant reduction in surface roughness occurred with a drop in vertical step size and an increase in feed rate. In detail, the vertical step size has the most significant influence on the surface roughness, followed by the feed rate and the forming tool radius. In conclusion, the optimum level settings were obtained: forming tool radius at level 3, spindle speed at level 1, vertical step size at level 1, and feed rate at level 4. Additionally, confirmation experiment results based on the optimal settings indicated a good agreement against the experimental observation. Further, the response surface methodology (RSM) was also exploited to devise a mathematical model for predicting the surface roughness. The results comparison confirmed that both techniques could effectively improvise the surface finish.

SELECTION OF CITATIONS
SEARCH DETAIL
...