Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks

New Generation of the Compact System for Performing Measurements of Sold Liquids by Gas Station Dispensers

This paper presents an advanced compact system that represents an innovative solution determined for the precise measurement of the fuel liquids and chemical materials used in the transport area. This system was created as a product of the applied research, development, and following realisation in the Slovak Legal Metrology. This organisation is authorised for the certification of the measuring systems. The given system enables to perform metrological control for a wide range of the measuring systems in order to achieve reliable results in the measuring process and the required measuring precision, as well as to minimise idle times of the measuring stands during the metrological control. In addition, the presented system is user-friendly and it requires only a short time for training of the metrological personnel.

Application of ANN for Analysis of Hole Accuracy and Drilling Temperature When Drilling CFRP/Ti Alloy Stacks

Drilling of Carbon Fiber-Reinforced Plastic/Titanium alloy (CFRP/Ti) stacks represents one of the most widely used machining methods for making holes to fasten assemblies in civil aircraft. However, poor machinability of CFRP/Ti stacks in combination with the inhomogeneous behavior of CFRP and Ti alloy face manufacturing and scientific community with a problem of defining significant factors and conditions for ensuring hole quality in the CFRP/Ti alloy stacks. Herein, we investigate the effects of drilling parameters on drilling temperature and hole quality in CFRP/Ti alloy stacks by applying an artificial neuron network (ANN). We varied cutting speed, feed rate, and time delay factors according to the factorial design L₉ Taguchi orthogonal array and measured the drilling temperature, hole diameter, and out of roundness by using a thermocouple and coordinate measuring machine methods for ANN analysis. The results show that the drilling temperature was sensitive to the effect of stack material layer, cutting speed, and time delay factors. The hole diameter was mainly affected by feed, stack material layer, and time delay, while out of roundness was influenced by the time delay, stack material layer, and cutting speed. Overall, ANN can be used for the identification of the drilling parameters–hole quality relationship.

Fire Models as a Tool for Evaluation of Energy Balance in Burning Space Relating to Building Structures

Fire is defined as an extremely hazardous event, causing a threat to life and health of persons, but also damage to the economic sphere. It has been shown many times that fire can occur anywhere and at any time. In order to minimize the risk of fire manifestations, it is necessary to understand its course. In technical practice, computational models are used to determine the partial manifestations of fire, such as fire spread rate, smoke generation rate in the burning area, formation of toxic burning products, flame height, and others. One of the important characteristics is also the energy balance in the burning area relating to the character of burning material, access of oxygen necessary for exothermic reaction of burning, and reaction of the installed safety devices. In this paper we will point out the fire safety of the building. The FDS (Fire Dynamics Simulator) model is recently used in practice, and its advantage is the possibility to model fire even in large and atypical spaces. The contribution of this paper is the practical application of fire safety of construction using the FDS Model, to reduce the cost of fire safety for the structure being constructed. Attention was paid to evaluating how the heat energy that is released during a fire can be influenced by the installed stable fire-extinguishing equipment, taking into consideration the fire resistance of the building structures.

Influence of Gating System Parameters of Die-Cast Molds on Properties of Al-Si Castings

The resulting quality of castings indicates the correlation of the design of the mold inlet system and the setting of technological parameters of casting. In this study, the influence of design solutions of the inlet system in a pressure mold on the properties of Al-Si castings was analyzed by computer modelling and subsequently verified experimentally. In the process of computer simulation, the design solutions of the inlet system, the mode of filling the mold depending on the formation of the casting and the homogeneity of the casting represented by the formation of shrinkages were assessed. In the experimental part, homogeneity was monitored by X-ray analysis by evaluating the integrity of the casting and the presence of pores. Mechanical properties such as permanent deformation and surface hardness of castings were determined experimentally, depending on the height of the inlet notch. The height of the inlet notch has been shown to be a key factor, significantly influencing the properties of the die-cast parts and influencing the speed and filling mode of the mold cavity. At the same time, a significant correlation between porosity and mechanical properties of castings is demonstrated. With the increasing share of porosity, the values of permanent deformation of castings increased. It is shown that the surface hardness of castings does not depend on the integrity of the castings but on the degree of subcooling of the melt in contact with the mold and the formation of a fine-grained structure in the peripheral zones of the casting.

The Use of Neural Networks in the Analysis of Dual Adhesive Single Lap Joints Subjected to Uniaxial Tensile Test

Adhesive bonding are becoming increasingly important in civil and mechanical engineering, in the field of mobile applications such as aircraft or automotive. Adhesive joints offer many advantages such as low weight, uniform stress distribution, vibration damping properties or the possibility of joining different materials. The paper presents the results of numerical modeling and the use of neural networks in the analysis of dual adhesive single-lap joints subjected to a uniaxial tensile test. The dual adhesive joint was created through the use of adhesives with various parameters in terms of stiffness and strength. In the axis of the overlap, there was a point bonded joint characterized by greater stiffness and strength, and on the outside, there was a bonded joint limited by the edges of the overlap and characterized by lower stiffness and strength. It is an innovative solution for joining technology and the influence of such parameters as the thickness of one of the adherends, the radius of the point bonded joint and the material parameters of both adhesive layers were analyzed. The joint is characterized by a two-stage degradation process, i.e., after the damage of the rigid adhesive, the flexible adhesive ensures the integrity of the entire joint. For numerical modeling, the Finite Element Method (FEM) and cohesive elements was used, which served as input data to an Artificial Neural Network (ANN). The applied approach allowed the impact of individual parameters on the maximum force, initiation energy, and fracture energy to be studied.