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This study explores the effectiveness of empirical mode decomposition (EMD) and filter diagonalization methods (FDM) in joint monitoring tool wear during machining operations using vibration measurements. The research investigates diverse tools and working conditions to comprehensively evaluate these techniques. Statistical analysis employing Kurtosis, Skewness, and RMS is used to select relevant intrinsic modes, with IMF 4 identified as the optimal mode for tracking tool wear. The validity of the EMD results is confirmed through Fast Fourier Transform (FFT) analysis, which further supports the efficacy of EMD in tool wear monitoring. Additionally, FDM, a frequency-domain signal processing tool commonly used in nuclear magnetic resonance, is applied for the first time to track wear. While FDM may face challenges in resolving certain peaks, it provides stable and cumulative intensities, helping in local spectral resolution. The results should be interpreted cautiously, with further research and validation needed. Overall, this study demonstrates the potential of EMD and FDM methods for practical implementation in accurate and reliable tool wear assessment during machining operations.

Machine learning (ML) is a well-known subfield of artificial intelligence (AI) that aims at developing algorithms and statistical models able to empower computer systems to automatically adapt to a specific task through experience or learning from data [...]

Nitrogen oxides (NOx), sulphur dioxide (SO2), carbon monoxide (CO), and carbon dioxide (CO2) are the primary emissions in cement industry. Small quantities of heavy metals also may be emitted. Heavy metal emissions from cement plants depend on their content in fuels and raw materials, industrial technology and emission control measures. During the cement production process at high temperatures many heavy metals present in the fuel and the raw materials evaporate and then condense on the clinker and partly on fine particles in the flue gas. The latter portion of the metals finds its way to the atmosphere after passing through the emission control equipment. The contents of heavy metals, released into the air by Gubbio in cement industry and fallout to the ground in the surrounding area of dispersion of gaseous emissions, were investigated. The analyses revealed the presence of various traces of heavy metals, namely arsenic (As), cadmium (Cd), chromium (Cr) nickel (Ni) and lead (Pb) in atmospheric emissions of cement industry. Arsenic, Cadmium, Chromium and Nickel, like most inorganic compounds, meet the classification criteria as carcinogenic substances pursuant to EC Regulation 1272/2008 and carcinogenic according to Dir. 2004/37/EC.

Control and monitoring of pipelines, dedicated to liquids transportation and distribution, are generally performed by active and passive techniques. With active techniques, we intend the possibility of using external sources to target pipelines and/or liquid inside the infrastructure. The used radiation can be partly reflected by the liquid and pipeline. Passive techniques are related to spontaneous emissions from liquids within the pipelines. However, capturing water displacements and fluctuations within the pipeline, by means of sensors and transducers, is somehow a passive technique. This latter is here illustrated thanks to pressure sensors mounted on an experimental pipeline. The basic idea is to detect leaks but also to monitor water flow regime by means of imaging without using a camera. That is possible thanks to the algorithm here developed and based on 2D/3D Decimated Signal Diagonalization (DSD). It is a technique used in nuclear magnetic resonance, and susceptible to bring to excellent results if water flow within a pipeline is considered as blood flow inside an artery or a vein. The approach has been applied to an experimental hydraulic circuit.