Using Fuzzy Control for Feed Rate Scheduling of Computer Numerical Control Machine Tools

In industrial processing, workpiece quality and processing time have recently become important issues. To improve the machining accuracy and reduce the cutting time, the cutting feed rate will have a significant impact. Therefore, how to plan a dynamic cutting feed rate is very important. In this study, a fuzzy control system for feed rate scheduling based on the curvature and curvature variation is proposed. The proposed system is implemented in actual cutting, and to verify the data an optical three-dimensional scanner is used to measure the cutting trajectory of the workpiece. Experimental results prove that the proposed fuzzy control system for dynamic cutting feed rate scheduling increases the cutting accuracy by 41.8% under the same cutting time; moreover, it decreases the cutting time by 50.8% under approximately the same cutting accuracy.

Digitized Stress Function-Based Feed Rate Scheduling for Prevention of Mesoscale Tool Breakage during Milling Hardened Steel

In this article, a digitized stress function-based feed rate scheduling algorithm is formulated for the prevention of tool breakage while having an optimum material removal rate in mesoscale rough milling of hardened steel. Instead of setting limits to the cutting forces and material removal rates, the presented method regulates the tool’s stresses. A 3D coupled Eulerian-Lagrangian finite element method (FEM) model is used to simulate a 3D chip flow-based stress according to the mesoscale tool’s rotation during cutting of hardened steel. Maximum uncut chip thickness and tool engaging angle of the uncut chip is identified as the fundamental driving factors of tool breakage in down milling configuration. Furthermore, a multiple linear regression model is formed to digitize the stress with two major factors for digitized feed scheduling. The optimum feed rates for each segment along the tool path can be obtained through finite element models and a multiple linear regression model. The feed rate scheduling method is validated through cutting experiments with tool paths of linear and arc segments. In a series of experimental validations, the algorithm demonstrated the capability of reducing the machining time while eliminating cutting tool breakages.

Learning Rate Scheduling Policies

With the availability of high processing capability hardwares at less expensive prices, it is possible to successfully train multi-layered neural networks. Since then, several training algorithms have been developed, from algorithms which are statically initialized to algorithms which adaptively change. It is observed that to improve the training process of neural networks, the hyper-parameters are to be fine tuned. Learning Rate, Decay rate, number of epochs, number of hidden layers and number of neurons in the network are some of the hyper-parameters in concern. Of these, the Learning rate plays a crucial role in enhancing the learning capability of the network. Learning rate is the value by which the weights are adjusted in a neural network with respect to the gradient descending towards the expected optimum value. This paper discusses four types of learning rate scheduling which helps to find the best learning rates in less number of epochs. Following these scheduling methods, facilitates to find better initial learning rate value and step-wise updation during the later phase of the training process. In addition the discussed learning rate schedules are demonstrated using COIL-100, Caltech-101 and CIFAR-10 datasets trained on ResNet. The performance is evaluated using the metrics, Precision, Recall and F1-Score. The results analysis show that, depending on the nature of the dataset, the performance of the Learning Rate Scheduling policy varies. Hence the choice of the scheduling policy to train a neural network is made, based on the data.