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Related Concept Videos

Electro-mechanical Systems01:19

Electro-mechanical Systems

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Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...
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Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
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Transmission Shafts: Problem Solving01:09

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Designing a solid shaft that transmits power from a motor to a machine tool involves a series of calculations to ensure the shaft can withstand the stresses applied by bending moments and torques. First, calculate the torque exerted on the gear, considering the power transmitted by the shaft and its rotational speed. Following this, compute the tangential forces acting on the gears, which directly relate to the torque and the gear radius.
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Magnetic forces on wires carrying current are most frequently applied in motors. A DC motor is a device that converts electrical energy into mechanical work. In motors, wire loops are enclosed in a magnetic field. When current flows through the loops, the magnetic field applies torque, which causes the shaft to rotate. The direction of the current is reversed once the loop's surface area is lined up with the magnetic field, causing a constant torque on the loop. During the process,...
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Multimachine Stability01:25

Multimachine Stability

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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Wind Turbine Machine Models01:24

Wind Turbine Machine Models

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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Enhancing Manufacturing Precision: Leveraging Motor Currents Data of Computer Numerical Control Machines for

Lucijano Berus1,2, Jernej Hernavs1, David Potocnik1

  • 1Intelligent Manufacturing Laboratory, Production Engineering Institute, Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia.

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Summary

This study introduces an indirect measurement method using Computer Numerical Control (CNC) machine motor current data and machine learning to predict part geometric accuracy in real-time, reducing production time and costs.

Keywords:
CNC controller datadata-driven manufacturinggeometrical accuracymachine learning algorithmssmart production machines

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Area of Science:

  • Manufacturing Engineering
  • Machine Learning Applications
  • Metrology

Background:

  • Direct geometric verification of machined parts typically requires post-machining inspection, increasing production time and costs.
  • Current methods like Coordinate Measuring Machines (CMMs) and optical scanners are sequential, creating bottlenecks in the manufacturing process.

Purpose of the Study:

  • To develop a novel indirect measurement method for real-time prediction of geometric accuracy during CNC machining.
  • To reduce reliance on post-machining inspections, thereby optimizing production efficiency and cost-effectiveness.

Main Methods:

  • Utilized motor current data from CNC machine controllers as indirect measurement signals.
  • Applied machine learning algorithms including Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT) for predictive modeling.
  • Employed Tsfresh and ROCKET for feature extraction from motor current data to correlate with geometric features.

Main Results:

  • Successfully predicted three geometric features of a mounting rail with a Mean Absolute Percentage Error (MAPE) below 0.61% (learning) and 0.64% (testing).
  • Demonstrated the feasibility of real-time geometric accuracy prediction directly from machining operation data.

Conclusions:

  • The proposed indirect measurement method significantly reduces the need for subsequent CMM or optical scanning inspections.
  • This approach offers a pathway to substantial reductions in manufacturing time and costs while upholding quality standards.