Evaluation of the Relationship Among Dressing Conditions Using Prismatic Dresser, Dressing Resistance, and Grinding Characteristics

In the grinding process, the grinding wheel surface condition changes depending on the dressing conditions, which affects the ground surface roughness and grinding resistance. Several studies have been reported on the practical application of dressing using prismatic dressers in recent years. However, only a few studies that quantitatively evaluate the effects of differences in dressing conditions using prismatic dresser on the ground surface roughness and grinding resistance have been reported. Thus, this study aims to evaluate quantitatively the effect of the difference in dressing conditions using the prismatic dresser on the ground surface roughness and grinding resistance by focusing on the dressing resistance. In the experiment, dressing is performed by changing the dressing lead and the depth of dressing cut with a prismatic dresser, and the ground surface roughness and grinding resistance are measured. Consequently, by increasing the dressing lead and the depth of dressing cut, the ground surface roughness increased, and the grinding resistance decreased. This phenomenon was caused by the increase in dressing resistance when the dressing lead and the depth of dressing cut were increased, which caused a change in the grinding wheel surface condition. Furthermore, the influence of the difference in dressing conditions using the prismatic dresser on the ground surface roughness and grinding resistance can be quantitatively evaluated by using the dressing resistance.

Effect of the Abrasive Grain Distribution on Ground Surface Roughness

In order to reduce the grinding surface roughness, it is necessary to optimize the grinding conditions; this requires clear understanding of the relationship between the grinding conditions and ground surface roughness. Therefore, various studies have been carried out over the decades on the ground surface roughness and have proposed statistical grinding theory to define the relationship between the grinding conditions and ground surface roughness. However, the statistical grinding theory does not consider a few grinding conditions such as abrasive grain shape and distribution of abrasive grain, which affect the ground surface roughness. In this study, we construct a statistical grinding theory that considers the effect of abrasive grain distribution and improves the accuracy of the theoretical analysis of the ground surface roughness.

Wheel Wear Related Instability in Grinding of Quartz Glass

Grinding is a popular method for producing high-quality parts made of hard and brittle materials. A lot of researchers have focused on the impact of grinding parameters on surface quality. However, only a few studies discussed the surface quality instability caused by the grinding wheel wear during a long grinding process. In this paper, through wheel state monitoring and surface quality testing of ground samples, it is found that the relationship between ground surface roughness and theoretical undeformed chip thickness is significantly affected by the grinding wheel wear state, rather than maintain steady as described in most available models. By introducing the normal grinding force, a linearly relationship was found among normal grinding force, undeformed chip thickness and ground surface roughness. Besides, sensitivity analysis was conducted to guide the parameter adjustment to maintain the stability of ground surface roughness and grinding state. The mechanism of the effect of wheel wear on normal grinding force was also studied in detail. This study will help to further understand the mechanism of the influence of wheel wear on the grinding stability.

Trajectory Control of Designed Experimental Mobile Robot

Due to advancing technology; nowadays mobile robot applications in hospitals have been increased. For that reason, it is very important and necessary to analyze the trajectory of such helping robotic system. However; there are many types of mobile robots have been utilized in hospital applications such as helping nurses. In this simulation study; a designed and controlled mobile robot was controlled by using standard feedback controllers. On the other hand, the robot was also tested with disturbances of ground surface roughness. The simulation results were improved that standard PID controller has superior performance to overcome surface roughness of the robot trajectory.

First Steps through Intelligent Grinding Using Machine Learning via Integrated Acoustic Emission Sensors

The surface roughness of the ground parts is an essential factor in the assessment of the grinding process, and a crucial criterion in choosing the dressing and grinding tools and parameters. Additionally, the surface roughness directly influences the functionality of the workpiece. The application of artificial intelligence in the prediction of complex results of machining processes, such as surface roughness and cutting forces has increasingly become popular. This paper deals with the design of the appropriate artificial neural network for the prediction of the ground surface roughness and grinding forces, through an individual integrated acoustic emission (AE) sensor in the machine tool. Two models were trained and tested. Once using only the grinding parameters, and another with both acoustic emission signals and grinding parameters as input data. The recorded AE-signal was pre-processed, amplified and denoised. The feedforward neural network was chosen for the modeling with Bayesian backpropagation, and the model was tested by various experiments with different grinding and neural network parameters. It was found that the predictions presented by the achieved network parameters model agreed well with the experimental results with a superb accuracy of 99 percent. The results also showed that the AE signals act as an additional input parameter in addition to the grinding parameters, and could significantly increase the efficiency of the neural network in predicting the grinding forces and the surface roughness.