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相关概念视频

Electro-mechanical Systems01:19

Electro-mechanical Systems

915
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...
915
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

177
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.
In this model, each generator is connected to a...
177
Transmission Shafts: Problem Solving01:09

Transmission Shafts: Problem Solving

210
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.
Next, use bending moment diagrams for the shaft to...
210
Force On A Current Loop In A Magnetic Field01:17

Force On A Current Loop In A Magnetic Field

3.2K
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

140
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:
140
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

100
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.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
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相关实验视频

Updated: Jun 3, 2025

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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提高制造精度:利用电机电流数据 计算机数控机器通过机器学习进行几何精度预测.

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.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
概括

本研究介绍了一种使用计算机数控 (CNC) 机器电机电流数据和机器学习的间接测量方法,以实时预测零件的几何精度,减少生产时间和成本.

关键词:
在CNC控制器数据控制器数据.数据驱动的制造数据驱动的制造在几何学准确度上.机器学习算法的算法智能生产机器是一种智能生产机器.

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Operation of the Collaborative Composite Manufacturing CCM System
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科学领域:

  • 制造业 工程 制造工程
  • 机器学习应用 机器学习应用
  • 计量学 计量学 计量学

背景情况:

  • 机械加工零件的直接几何验证通常需要加工后检查,增加生产时间和成本.
  • 目前的方法,如坐标测量机 (CMM) 和光学扫描仪是连续的,在制造过程中造成瓶.

研究的目的:

  • 开发一种新的间接测量方法,用于在CNC加工过程中实时预测几何精度.
  • 减少对加工后检查的依赖,从而优化生产效率和成本效益.

主要方法:

  • 利用来自数控机器控制器的电机电流数据作为间接测量信号.
  • 应用机器学习算法,包括随机森林 (RF),k-最近邻居 (k-NN) 和决策树 (DT),用于预测建模.
  • 使用 Tsfresh 和 ROCKET 来从电机电流数据中提取特征,以与几何特征相关联.

主要成果:

  • 成功预测了安装轨道的三个几何特征,平均绝对百分比误差 (MAPE) 低于0.61% (学习) 和0.64% (测试).
  • 从加工操作数据直接证明了实时几何精度预测的可行性.

结论:

  • 拟议的间接测量方法大大减少了后续CMM或光学扫描检查的需要.
  • 这种方法为大幅减少制造时间和成本提供了一条途径,同时保持质量标准.