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 ($\delta$) 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 $\delta$ value distribution, the BP-ANN model is superior in regressing and predicting than the Arrhenius-type constitutive equation.

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.

Forming a metallic sheet along with the consideration of computer simulation and experiment had benefited the milling industry for a long time. The ideal forming, without an error, is a concerning topic. So, the computer simulation had the advantage then direct forming. To observe the results before doing the real experiments simulation comes handy. Which helped to set the parameters for the milling process for the single point incremental forming (SPIF) process. For milling, a CAD design was converted into a 3D model. For this, a conical shape of 3D modeling was made in fusion 360. After onwards, it was simulated for finding the maximum depth for the cracking point. Next for the experimental part, the maximum forming depth was considered, and used lubricant grease for reducing friction. While forming with the grease, the impact of parameters was also changed. Throughout the process, an optimization approach was set to reduce the cracking areas for the G-code. Along with the lubricant use, smooth milling finished surface was observed. To reducing the depth forming errors, an optimization approach was introduced in this research.