A sound or a noise that accompanies wood machining processes is introduced by the tool rotation itself, by the friction of moving machine parts, or by wood-tool interaction. The sounds generated during machining with a circular saw could be analysed in order to monitor and possibly control the cutting process. Applying altered cutting parameters while cutting beech wood (Fagus sylvatica L.), which is the most common wood species in the Republic of Serbia, caused acoustic emissions that could be analysed throughout corresponding spectra. As shown in previous studies, altering the cutting parameters, e.g., the feed speed and tool override, resulted in variations in power consumption, surface roughness, and acoustic emission (or acoustic pressure). The aim of this paper was to provide a possible correlation between the applied cutting parameters and the acoustic emission spectra with respect to consumed power and the state of the machined surface. Along with acoustic emissions, the power consumption and surface roughness data were also acquired in order to make a possible relationship. By associating the idle circular saw acoustic spectra with background noise and comparing them with those obtained during machining, it was possible to indicate spectrum areas of particular interest for further analysis.

This article presents an attempt to estimate the nonlinear, multivariable dependence between the main (tangential) cutting force (FC) and the processing parameters and moduli of elasticity of oak wood (Quercus robur) during peripheral milling with a straight edge. The analysis indicated that the tangential force (FC) was affected by cutting depth (cD), feed rate per tooth (fZ), rake angle (γF), elastic modulus by stretching along the grain (ESA), elastic modulus by stretching perpendicular to the grain (ESP), elastic modulus by compression along the grain (ECA), and the elastic modulus by compression perpendicular to the grain (ECP). It was found that the elastic moduli (ESA, ESP, ECA, ECP) very well described the mechanical properties of processed wood. Several interactions between the examined parameters (namely, ESA·γF, ESP·γF, ECP·γF, fZ·γF, and fZ·cD) were confirmed in the developed relationship FC = f(ESA, ESP, ECA, ECP, fZ, cD, γF).

This article aims to investigate the mechanical characteristics of specimens fabricated using Selective Laser Sintering technology. The research covers flexural specimens, produced by PA12 materials. CAD model dimensions were selected according to the ISO 178 standard, and the chosen specimen geometry is 96 x 8 x 4 [mm] in bulk. All specimens were produced using a specialized machine Fuse 1 (FormLabs, Summerville, MA). Four specimen batches were produced, each with a different printing orientation (i.e. vertical and horizontal) and location on the printing plate (i.e. in the middle and on the edge of the powder bed). The specimens are tested using a Shimadzu universal machine for testing the mechanical characteristics of materials, AGS-X 100 kN, with a unique additional tool for testing 3-point bending specimens.

Distributions of the modulus of elasticity (MOE) and modulus of rupture (MOR) were characterized at three loading rates for small clear beech specimens in static bending. The correlation between MOE and MOR for all three loading rates was significant, but it weakened with increasing load rates. The analysis of the characteristics of empirical distributions, as well as the preliminary selection of the theoretical distributions for MOE and MOR, were performed on the basis of L-moments and L-moment diagrams. According to the standard for testing small specimens, MOE and MOR are determined as the arithmetic mean of the sample. Usage of the arithmetic mean is justified when the analyzed quantity is symmetrically distributed. It was found that the distribution of MOE and MOR is not always symmetric. The loading rate influences the shapes of the MOE and MOR empirical distributions, and consequently the choice of theoretical distribution. The general extreme value distribution stood out as the best one for both MOE and MOR, regardless of the loading rate, and the second overall ranked distribution is the three-parameter Weibull distribution. The loading rate affected the value of the fifth percentile in MOR, when determined from both the empirical and theoretical distributions.