Diagnostic System of Drill Condition in Laminated Chipboard Drilling Process

The paper presents an on-line automatic system for recognition of the drill condition in a laminated chipboard drilling process. Two states of the drill are considered: the sharp enough (still able to drill holes acceptable for processing quality) and worn out (excessive drill wear, not satisfactory from the quality point of view of the process). The automatic system requires defining the diagnostic features, which are used as the input attributes to the classifier. The features have been generated from 5 registered signals: feed force, cutting torque, noise, vibration and acoustic emission. The statistical parameters defined on the basis of the auto regression model of these signals have been used as the diagnostic features. The sequential step-wise feature selection is applied for choosing the most discriminative set of features. The final step of recognition is done by support vector machine classifier working in leave one out mode. The results of numerical experiments have confirmed good quality of the proposed diagnostic system

A data-driven predictive model of the grinding wheel wear using the neural network approach

Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. Thus, to model the state of tool wear and next to predict its remaining useful life (RUL) significantly increases the sustainability of manufacturing processes. there are many approaches, methods and theories applied to predictive model building. the proposed paper investigates an artificial neural network (ANN) model to predict the wear propagation process of grinding wheel and to estimate the RUL of the wheel when the extrapolated data reaches a predefined final failure value. The model building framework is based on data collected during external cylindrical plunge grinding. Firstly, usefulness of selected features of the measured process variables to be symptoms of grinding wheel state is experimentally verified. Next, issues related to development of an effective MLP model and its use in prediction of the grinding wheel RUL is discussed

Botulinum toxin: An effective treatment for prosthesis-related hyperhidrosis in patients with traumatic amputations

Hyperhidrosis-related to prosthesis use in patients who have suffered a traumatic limb amputation presents itself as a barrier to comfort, prosthesis use and overall quality of life. This review intends to encourage dermatologists to consider the use of botulinum toxin A or B for the treatment of hyperhidrosis in the residual limb and may serve as a stimulus for a modern, in-depth, and more comprehensive study. A review of the literature was conducted using the PubMed database, focusing on hyperhidrosis treatment after traumatic limb amputation. Articles discussing hyperhidrosis treatment for amputations secondary to chronic medical conditions were excluded. Seven case studies published over the last 12 years have demonstrated positive outcomes of this treatment strategy. Overall, there is little data examining this topic and current publications focus primarily on small case series. A larger, double-blind, placebo-controlled study would likely benefit veterans, service members, and civilians