MATEC Web of Conferences, open access proceedings in Materials science, Engineering and Chemistry

This study developed a three-axis vibrational haptic wearable device (RCWS) utilizing Linear Resonant Actuators (LRAs) to simulate grinding vibrations. The implementation of RCWS is described in detail. By recording the normal force during manual grinding with a load cell and converting it into a series of PWM commands, the LRA on the RCWS was controlled in open-loop mode using these PWM commands. Three methods were tested for force-to-PWM conversion, two of which showed a linear correlation (>0.7) with raw data. In the correlation between PWM commands and generated acceleration, all three methods exhibited a high linearity of at least 0.85. This wearable RCWS offers a promising approach for users to experience the machining force from the versatile and critical remote machining process with a finger vibratory sensation.

Cutting parameters can to be properly adjusted to increase machining precision, to enhance cutting efficiency, and to reduce cost. These parameters are generally chosen according to the recommended data from the tool maker’s technical manual with the synergy of the operator’s experiences. However, inappropriate parameters selection might not only damage cutter and the machine tool, but also worsen the surface roughness of the workpiece. Researches on improving surface roughness has been studied over the years, for example, on the optimization of cutting conditions, vibration measurement and analysis, and cutting forces measurement and analysis, to name only a few. However, most of them must have sensors installed on the machine tool for data collection before the analysis. This will increase the cost. Furthermore, the measurement of cutting force needs to have dynamometer which is not only expensive but is also not appropriate to be installed on a small-sized machine tool. This research focused on establishing the relationship between the cutting force and the surface roughness for the workpiece machining in a small-sized machine tool. In order to calculate the cutting force, cutting force coefficients were obtained based on the cutting test on the other machine tool rather than the small-sized machine. Tapping test was also conducted to obtain the frequency response function of the machine. The cutting forces were calculated based on the cutting force coefficients and the frequency response function. The calculated cutting forces were then converted through the proposed methodologies into eleven characteristic values. Finally, two commonly used machine learning algorithms, multivariate linear regression and generalized regression neural network were adopted for regression analysis to establish the relationship between the cutting forces and the surface roughness. The results have shown that the prediction by generalized regression neural network is better than that of multivariate linear regression. The major contribution of this paper is that appropriate algorithms have been studied, compared and verified so that the relationship between cutting force and surface roughness can be established. This means that surface roughness can be estimated based on the calculated cutting forces incorporating the machine tool’s frequency response function.

In theoretically traditional practice, the cutting force calculation/prediction based on the cutting force coefficients (CFCs) will not be affected by the properties of the machine tool such as the structural dynamics and the Frequency Response Function (FRF), etc. It means the CFCs are related only to the paired cutting tool and workpiece, but not the machine. However, it seems to be not the same in practice. This paper proposes an alternative method by conducting the cutting test experiment on the other machines that have enough working space for a dynamometer to be installed for the cutting test, and then applying it to a small working space machine. A series of experiments had been designed for cutting test and tapping test to verify the proposed method. This paper then focused on the exploration of varieties that influence the cutting forces. Finally, a new stable machining method has been successfully developed and verified with a case study by using a real seven-axis mill-turn machine tool (MTM) different from the machine tools used for the cutting test.

This paper describes the development of a Cloud-based solid cutting simulation service system for 3D toolpath verification before it is to be uploaded to a CNC machine tool for machining operation. A user can access the system through common WWW browsers such as Internet Explorer and/or Google Chrome in either a PC running Microsoft Windows, a smart phone running Android, or an iPad running iOS. The system can perform real time solid cutting simulation based on the toolpath (NC program). The system provides user with standard views such as “ISO View”, “Front View”, “Right View”, and “Top View”; dynamic customized 3D view could be controlled through the mouse operation on the client side to ease the visibility during the simulation. The proposed system has been successfully implemented on a public cloud system Microsoft Azure with expected performance and is expected to assist a user in verifying the correctness of the toolpath and to enhance the efficiency in a geographically dispersed networked environment