Tool Wear Prediction When Machining with Self-Propelled Rotary Tools

Enhancing Tool Wear Prediction Accuracy Using Walsh-Hadamard Transform, DCGAN and Dragonfly Algorithm-Based Feature Selection

Tool wear is an important concern in the manufacturing sector that leads to quality loss, lower productivity, and increased downtime. In recent years, there has been a rise in the popularity of implementing TCM systems using various signal processing methods and machine learning algorithms. In the present paper, the authors propose a TCM system that incorporates the Walsh-Hadamard transform for signal processing, DCGAN aims to circumvent the issue of the availability of limited experimental dataset, and the exploration of three machine learning models: support vector regression, gradient boosting regression, and recurrent neural network for tool wear prediction. The mean absolute error, mean square error and root mean square error are used to assess the prediction errors from three machine learning models. To identify these relevant features, three metaheuristic optimization feature selection algorithms, Dragonfly, Harris hawk, and Genetic algorithms, were explored, and prediction results were compared. The results show that the feature selected through Dragonfly algorithms exhibited the least MSE (0.03), RMSE (0.17), and MAE (0.14) with a recurrent neural network model. By identifying the tool wear patterns and predicting when maintenance is required, the proposed methodology could help manufacturing companies save money on repairs and replacements, as well as reduce overall production costs by minimizing downtime.

Turned Surface Monitoring Using a Confocal Sensor and the Tool Wear Process Optimization

Laser scanning technology has been used for several years. Nevertheless, no comprehensive study has been conducted to prove that the application of confocal chromatic sensor (CCHS) laser technology is effective and suitable to verify the integrity parameters of machined surfaces in terms of cutting tool damage. In this paper, the optimization and effects of five factors (cutting speed, feed, depth of cut, attachment length of the workpiece, and tip radius) on the roundness deviation measured by CCHS and, at the same time, on the amount of wear on the back side of the cutting part of the tool were studied according to ISO 3685, which was measured with a microscope. The results obtained were evaluated using the gray relational analysis method (GRA), in conjunction with the Taguchi method, and the significance of the factors was demonstrated using the analysis of variance (ANOVA) method.

Studies of Simultaneous Friction and Corrosive Processes in the Presence of Abrasive Particles

Providing high-quality machinery and equipment in technical terms is an activity aimed primarily at ensuring the high reliability of nodes. Reliability of machinery and equipment is mainly characterized by resistance to destructive processes. Mutual displacement of parts causes destructive friction phenomena, the intensity of which can be intensified by the interaction of specific technological environments. The article presents the results of research on ring-on-ring samples made of C45 steel in the non-heat-treated state, which were subjected to mechanical, corrosive, and abrasive wear and various combinations of them. The main purpose of the study was to determine the wear that results from the action of destructive friction and corrosive processes with the presence of abrasive material in the tribological node. The results supplement the knowledge of material wear under the simultaneous action of several destructive factors. Based on the study, it was noticed that the sum component of mechanical-abrasive-corrosive interactions is about 40-50% of the total wear. Mass loss resulting from simultaneous mechanical and abrasive interactions is equal about one-third of the total wear. In addition, it was observed that the effects of the interactions of friction (mechanical), corrosive and abrasive excitations are synergistic in nature, which lead to increases in the total wear of the tested samples made of steel. The results of the research are of practical importance and allow for wear-optimal selection of material in the friction node of modern machinery and equipment.

Effects of Process Cutting Parameters on the Ti-6Al-4V Turning with Monolithic Driven Rotary Tool

Machining with rotating tools appears to be an efficient method that employs a non-standard kinematic turning scheme. It is used in the machining of materials that we classify in the category of difficult to machine. The titanium alloy Ti-6Al-4V, which is widely used in industry and transportation, is an example of such material. Rotary tool machining of titanium alloys has not been the subject of many studies. Additionally, if researchers were dissatisfied with their findings, the reason may not be the kinematic machining scheme itself but rather the tool design and the choice of cutting parameters. When tools are constructed of several components, inaccuracies in production and assembly can arise, resulting in deviations in the cutting part area. A monolithic driven rotary tool eliminates these factors. In the machining process, however, it may react differently from multi-component tools. The presented work focuses on the research of the technology for machining titanium alloy Ti-6Al-4V using a monolithic driven rotary tool. The primary goal is to gather data on the impact of cutting parameters on the machining process. The cutting force and the consequent integrity of the workpiece surface are used to monitor the process. The speed of workpiece rotation has the greatest impact on the process; as it increases, the cutting force increases, as do the values of the surface roughness. In the experiment, lower surface roughness values were attained by increasing the feed parameter and the depth of cut. This may predetermine the inclusion of a kinematic scheme in highly productive technologies.