Virtual Modelling and Simulation of a CNC Machine Feed Drive System

This paper deals with the virtual modelling and simulation of a complex CNC machine tool feed drive system. The first phase of the study was the modelling of a very complex structure of the feed drive which consists of many elements (position, velocity and current control regulators, actuators, mechanical transmission elements, etc.). All these elements have great influence on important parameters of the machine tool such as movement stability, positioning accuracy and dynamic stiffness. For the modelling of the system the Matlab-SIMULINK and Matlab-Sim Scape Toolbox software was used. The Matlab-Sim Scape Toolbox allowed us to use the complete CAD model of the geometry of the machine tool, automatically calculating the selected properties. The influence of changing and optimizing several feed drive parameters (position loop gain Kv, proportional gain Kp of the velocity controller, integral gain of velocity controller-Tn, electrical drive time constant Te, total moving mass m, sampling period Ts, etc.) on the positioning accuracy and the dynamic stiffness was simulated, tested and validated. The finished Matlab-Simulink and Sim Scape models were initially visualized in the Matlab program. They were very simplified, comparing with their later visualization in the Virtual Reality EON Studio program

The Incidence of Perioperative Hypotension in Patients Undergoing Major Abdominal Surgery with the Use of Arterial Waveform Analysis and the Hypotension Prediction Index Hemodynamic Monitoring—A Retrospective Analysis

Intraoperative hypotension (IH) is common in patients receiving general anesthesia and can lead to serious complications such as kidney failure, myocardial injury and increased mortality. The Hypotension Prediction Index (HPI) algorithm is a machine learning system that analyzes the arterial pressure waveform and alerts the clinician of an impending hypotension event. The purpose of the study was to compare the frequency of perioperative hypotension in patients undergoing major abdominal surgery with different types of hemodynamic monitoring. The study included 61 patients who were monitored with the arterial pressure-based cardiac output (APCO) technology (FloTrac group) and 62 patients with the Hypotension Prediction Index algorithm (HPI group). Our primary outcome was the time-weighted average (TWA) of hypotension below p = 0.000009). In the FloTrac group, the average time of hypotension was 27.9 min vs. 8.1 min in the HPI group (p = 0.000023). By applying the HPI algorithm in addition to an arterial waveform analysis alone, we were able to significantly decrease the frequency and duration of perioperative hypotension events in patients who underwent major abdominal surgery