International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

Vortex Gas Pistons technology has been developed and manufactured to be specifically applied in most air rifles and it uses regular air instead of any other type of gas. Manufacturer ****** offers 6 different types of Vortex Gas Pistons covering most of the Break Barrel and Underlever lines. Despite the demand for high quality materials and optimal construction, operational impacts invariably take their toll on the weapon and defects often occur due to a variety of reasons – either quality or manufacturing defects in the material and those resulting from friction between individual parts, pressure and errors in the maintenance and operation of the weapon.
Each one of the listed impacts, as well as their combination, may be the cause of defects, which in this study are searched for on the items
submitted for evaluation details of original and destroyed air gun piston and barrel, showing defect.

The influence of non-metallic inclusions on the formation of defects in deformed steels and their role in providing crack resistance are considered. It is shown that non-metallic inclusions as stress and strain concentrators are one of the most dangerous sources of defect initiation in steels during pressure treatment. It has been established that the nature of cracks and the features of their growth near nonmetallic inclusions are determined by the type of inclusions, as well as by the scheme of the local stress state, which depends on the loading conditions. An analysis was made of the features of the initiation and development of the cracks near non-metallic inclusions of various types during tensile strain, compressive and bending deformation. It is shown that the most severe way of deformation for the inclusion-matrix system is tensile strain, the softest is compression.

Defects in the cooling jacket occur for a variety of reasons. This type of details works in severe heat load conditions – fluctuating fluid pressure, mechanical friction and corrosion. The reason for appearance of defects in auto truck engines is often the type of material from which their individual parts are made. For various reasons, they are susceptible to the influence of natural wear, material fatigue and the complex action of many physical and chemical fast-flowing processes.
One of the main parts of internal combustion engines is the cooling jacket. This part operates under severe thermal stress conditions caused by mechanical friction in contact with the piston, fluctuating pressure where the refrigerant fluid moves, and corrosive action from that same fluid containing antifreeze. The listed factors require the material from which this part is made to have strength and good wear and corrosion resistance – prerequisites for a long service life.
Therefore, it is important to identify the exact causes of cooling jacket defects and to found ways to prevent them.
The examined details are analyzed by standard methods for chemical and metallographic analysis, as well as for mechanical tests.

Possible defects in the system: piston-cooling jacket occur for various reasons. This system work in severe conditions: heat load; variable gas pressure and mechanical friction, which requires high strength, good wear resistance with limited lubrication and overall high resistance to abrasion, corrosion and other wear.The operation of the cylinder-piston group with insufficient or poor quality oil is the most common cause of overheating or engine blockage. Seizing of one or more pistons leads to major engine repairs or even scrappin g. Damaged fuel injectors, inaccurate injection or ignition timing can also cause the mechanism to melt or puncture. Therefore, it is essential to identify the exact causes of defects in the system and look for ways to prevent them.
Studied details were functioned under the identical conditions of good lubrication and no overheating. The reason, apart from the ones mentioned above, which led to their destruction, has been established.

The paper focuses on the evaluation of the quality of fillet welds produced robotically by the method of impulse synergic welding MAG. This welding technology is used in the automotive industry in the production of rear seat backs for passenger cars. In the process of automated production, the speed of the process, accuracy, and quality of the made joints are important. Several factors affect the accuracy and quality of welded joints. The paper presents the results of research where the influence of filleting of fillet welds on their quality was confirmed. Visual and capillary methods were used to determine the quality of welds, metallographic analysis for evaluation depth of weld root penetration, strength properties of welds were evaluated by static tensile test. Statistical ANOVA methods were used to process the obtained values. Experimental work confirmed that the depth of welding of the weld root into the base material has the greatest influence on the final quality of fillet welds. It is this parameter that results in the elimination of the weld and thus the entire produ ct.

The main consumers of grinding balls are mining, metallurgical, cement and energy industries. In these industries one of the main and very common operations is the grinding of various materials. Rolled steel balls are mainly produced by plastic deformation on crossscrew rolling mills. Technological limitations of increasing the hardness groups and the wear resistance of the balls currently available at the Metal Rolling plant of JSC “Sokolov-Sarbay mining and processing production association” (MRP JSC “SSGPO”), and they do not allow currently to quickly improve their quality. But at the same time, if the quality of finished products is improved, the MRP JSC “SSGPO” may have the opportunity to sell balls not only between its enterprises, but also to the domestic market, as well as the opportunity to develop export sales of balls to the countries of near and far abroad. Therefore, this paper presents the results of the study of the main causes of defects at the ball rolling mill 30-60 of metal rolling plant JSC “SSGPO” and the search for their exclusion

Regardless of the length of the period of use of the equipment, due to a change in technology, an increase in maintenance costs or the introduction of new requirements with a change in legislation, the machines become obsolete and are disposed of. The main methods for utilization of tractors and self-propelled agricultural machinery are considered. The equipment necessary for the disposal is also described. The methodology for determining the costs for utilization and the value of the equipment for utilization of the whole machine, element-by-element utilization and utilization after defecting has been supplemented.

This paper considers the process electron beam welding of stainless steel type 1H18NT in vacuum. Based on experimental data, the influence of the variations of the following process parameters: electron beam power, welding velocity, the distances from the magnetic lens of the electron gun to the beam focus and to the surface of the treated sample is investigated.

Neural and regression models for the geometry characteristics of the welded joints: surface of the weld cross-sections, weld depths and mean weld widths of the samples are estimated, as well as models for defining the areas of the process parameters, where the appearance of defects is or is not expected. The obtained models are used for developing the graphical user interface aiming investigation and prediction of the electron beam welding characteristics and process parameter optimization. This software can be implemented for supporting the operator’s choice of appropriate work regimes, obtaining the required welds quality standards, for education and investigations.

The scanning nozzle hot air system for thermographic detection of the surface incorporated hidden defects is proposed. Subsurface defects in the sample are detected using the high resolution thermal imaging camera FLIR SC7000. To introduce additional energy in are searched sample, a scanning hot air (about 110°C) nozzle is applied (a patent application P.403346). The hidden defect causes a temperature increase in comparison with the remaining area what is a result of changes in emissivity. The results are compared with the pulse thermography method using the xenon lamp for excitation.

Thermography measurements allow to detect the defects that may appear on a joint at welding of components. Energy pulse generated by a xenon lamp with adequate power in a short period of time is sufficient for thermal excitation and enables to register the temperature distribution using the thermography high resolution camera FLIR SC7000. The impulse with 6kJ energy and 6ms time generate sufficient power to measure the temperature distribution on the surface of the weld tested. During cooling the temperature of the area with defect changes more slowly than in the areas without defects, because of to the less intense heat dissipation. This allows the registration of defects in welds "on-line" at the production process. Material used for analysis detection of defects in the welded joints is Inconel 718, stainless steel 410 and stainless steel 321. The peak energy which flow throw the samples with defects in the welded joints its completely or partially blocked. It cause different temperature distribution on the surface in the places where the connection discontinuity take place.

This paper considers the process electron beam welding in vacuum of stainless steel 1H18NT. Neural network based models are developed and used for the description of the defectiveness, depending on the process parameters – electron beam power, welding velocity, the distance between the main surface of the magnetic lens of the electron gun and the beam focusing plane and the distance between the main surface of the magnetic lens of the electron gun and the sample surface. Neural network (NN) models, based on a multi-layered feedforward neural network, trained with Levenberg-Marquardt error backpropagation algorithm are compared with NN models, based on Pattern recording neural network, trained with Conjugate Gradient Algorithm. The neural networks are trained, verified and tested using a set of experimental data. The obtained models are implemented to predict areas of process parameters, where the appearance of defects is most probable and the location of welding regimes that should be avoided.