ABSTRACT
Grinding is commonly used for machining parts made of hard or brittle materials with the intent of ensuring a better surface finish. The material removal ability of a grinding wheel depends on whether the wheel surface is populated with a sufficiently high number of randomly distributed active abrasive grains. This condition is ensured by performing dressing operations at regular time intervals. The effectiveness of a dressing operation is determined by measuring the surface topography of the wheel (regions and their distributions on the grinding wheel work surface where the active abrasive grains reside). In many cases, image processing methods are employed to determine the surface topography. However, such procedures must be able to remove the regions where the abrasive grains do not reside while keeping, at the same time, the regions where the abrasive grains reside. Thus, special kinds of image processing techniques are needed to distinguish the non-grain regions from the grain regions, which requires a heavy computing load and long duration. As an alternative, in the framework of the "Biologicalisation in Manufacturing" paradigm, this study employs a bio-inspiration-based computing method known as DNA-based computing (DBC). It is shown that DBC can eliminate non-grain regions while keeping grain regions with significantly lower computational effort and time. On a surface of size 706.5 µm in the circumferential direction and 530 µm in the width direction, there are about 7000 potential regions where grains might reside, as the image processing results exhibit. After performing DBC, this number is reduced to about 300 (representing a realistic estimate). Thus, the outcomes of this study can help develop an intelligent image processing system to optimize dressing operations and thereby, grinding operations.
ABSTRACT
Cold expansion technology is a cold-forming process widely used in aeronautics to extend the fatigue life of riveted and bolted holes. During this process, an oversized mandrel is pushed through the hole in order to yield it and generate compressive residual stresses contributing to the fatigue life extension of the hole. In this paper, a parametric analysis of the mandrel geometrical data (inlet angle straight zone length and diametric interference) and their influence on the residual stresses was carried out using a finite element method (FEM). The obtained results were compared with the conclusions presented in a previous parametric FEM analysis on the influence of the swage geometry in a swaging cold-forming process of gun barrels. This process could be considered, in a simplified way, as a scale-up of the cold expansion process of small holes, and this investigation demonstrated the influence of the diameter ratio (K) on the relation between the mandrel or swage geometry and the residual stresses obtained after the cold-forming process.
ABSTRACT
Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions.
