Intelligent Sensor Software for Robust and Energy-Sustainable Decision-Making in Welding of Steel Reinforcement for Concrete.

In today's industrial landscape, optimizing energy consumption, reducing production times, and maintaining quality standards are critical challenges, particularly in energy-intensive processes like resistance spot welding (RSW). This study introduces an intelligent decision support system designed to optimize the RSW process for steel reinforcement bars. By creating robust machine learning models trained on limited datasets, the system generates interactive heat maps that provide real-time guidance to production engineers or intelligent systems, enabling dynamic adaptation to changing conditions and external factors such as fluctuating energy costs.

A Versatile Open-Source Printhead for Low-Cost 3D Microextrusion-Based Bioprinting.

Three-dimensional (3D) bioprinting promises to be essential in tissue engineering for solving the rising demand for organs and tissues. Some bioprinters are commercially available, but their impact on the field of Tissue engineering (TE) is still limited due to their cost or difficulty to tune. Herein, we present a low-cost easy-to-build printhead for microextrusion-based bioprinting (MEBB) that can be installed in many desktop 3D printers to transform them into 3D bioprinters.

Efficient Fabrication of Polycaprolactone Scaffolds for Printing Hybrid Tissue-Engineered Constructs.

Hybrid constructs represent substantial progress in tissue engineering (TE) towards producing implants of a clinically relevant size that recapitulate the structure and multicellular complexity of the native tissue. They are created by interlacing printed scaffolds, sacrificial materials, and cell-laden hydrogels. A suitable biomaterial is a polycaprolactone (PCL); however, due to the higher viscosity of this biopolymer, three-dimensional (3D) printing of PCL is slow, so reducing PCL print times remains a challenge.

Accurate Calibration in Multi-Material 3D Bioprinting for Tissue Engineering.

Most of the studies in three-dimensional (3D) bioprinting have been traditionally based on printing a single bioink. Addressing the complexity of organ and tissue engineering, however, will require combining multiple building and sacrificial biomaterials and several cells types in a single biofabrication session. This is a significant challenge, and, to tackle that, we must focus on the complex relationships between the printing parameters and the print resolution.

TAO-robust backpropagation learning algorithm.

In several fields, as industrial modelling, multilayer feedforward neural networks are often used as universal function approximations. These supervised neural networks are commonly trained by a traditional backpropagation learning format, which minimises the mean squared error (mse) of the training data. However, in the presence of corrupted data (outliers) this training scheme may produce wrong models.