Management The Safety of Exploitation of the Rotation Mechanism in a Self-Propelled Crane

The article analyzes of the load capacity of the rotation mechanism of the boom arm sub-assembly for a self-propelled crane. The uneven flexibility of systems supporting structures of the machine was taken into account in the identification of the load. The consideration are illustrated by the example slewing bearing from self-propelled crane. FEM method was used for calculations. The basic problems of construction of numerical model were discussed. Sample calculations of bearing carrying capacity has been done taking into account flexibility of bearings supporting structures, which also allowed to define the internal load distribution in the bearing. On example of the supporting structure of the mobile crane DST 5050 the internal load distribution in the crane bearing at different positions of the crane machine body were defined. During the rotation of the crane body priority angels were indicated. It has been shown that the deformations of the supporting frames are so large that they significantly change the distribution of forces transmitted by the individual bearing rollers.

Efficient Technological Control of the Processing of Bearing Rollers

In the article the strategy and algorithm of technological control of the process of bilateral abrasive processing of cylindrical bearing rollers are considered. The main variable control parameters of the process are the parameters of the angular position of the grinding wheels, the size of the removed allowance and the feed rate of the workpieces. As a result of complex theoretical and experimental research, control principles, criterial conditions, functional and parametric constraints are formulated. The solution of the problem of synthesis of control parameters is given. The effectiveness of the technological control is confirmed by production tests and is expressed in increasing the accuracy and productivity of processing rollers.

Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks

Bearings are commonly used machine elements and an important part of mechanical transmission. They are widely used in automobiles, airplanes, and various instruments and equipment. Bearing rollers are the most important components in a bearing and determine the performance, life, and stability of the bearing. In order to control the surface quality of the rollers, a machine vision system for bearing roller surface inspection is proposed. We briefly introduced the design of the machine vision system and then focused on the surface inspection algorithm. We proposed a multi-task convolutional neural network to detect defects. We extracted the features of the defects through a shared convolutional neural network, then classified the defects and calculated the position of the defects simultaneously. Finally, we determined if the bearing roller was qualified according to the position, category, and area of the defect. In addition, we explored various factors affecting performance and conducted a large number of experiments. We compared our method with the traditional methods and proved that our method had good stability and robustness.

ELID Fine Grinding of SiC Bearing Rollers

Electrolytic in-process dressing (ELID) grinding is drawing more and more attention by the researchers for its smart method of in-process dressing of the grinding wheel. Many researches place emphasis on study of in-processing dressing and oxide layer. In this study, the effects of three grinding factors, including wheel speed, load applied on workpiece and eccentricity of three parts in one holder, on surface roughness (Ra) and material removal rate (MRR) were investigated in a single-side ELID grinding experiment. Each factor has three levels. The experimental results show that each factor has different influence: 1. Applied load is the main factor which significantly affects the Ra and MRR; 2. the speed of grinding wheel doesn’t show much influence; 3. eccentricity has a great influence on roughness but not on material removal rate.