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Related Concept Videos

Machines01:19

Machines

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
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Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Machines: Problem Solving I01:22

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
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Mechanical Efficiency of Real Machines01:14

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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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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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Related Experiment Video

Updated: Jan 29, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Determining Material Removal and Electrode Wear in Electric Discharge Machining with a Generalist Machine Learning

Jorge M Cortés-Mendoza1, Agnieszka Żyra2, Andrei Tchernykh3,4

  • 1Cloud Competency Centre, National College of Ireland, Mayor Street, IFSC, D01 K6W2 Dublin, Ireland.

Materials (Basel, Switzerland)
|January 28, 2026
PubMed
Summary

Machine learning models, specifically Random Forest and Artificial Neural Networks, accurately predict Electric Discharge Machining (EDM) performance. These models significantly reduce experimental costs and improve predictive accuracy for Material Removal Rate (MRR) and Electrode Wear Rate (EWR).

Keywords:
cryogenic treatmentelectric discharge machiningelectrode wear ratemachine learningmaterial removal rate

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Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Computational Science

Background:

  • Electric Discharge Machining (EDM) is crucial for complex part fabrication from hard materials, but parameter optimization is challenging and costly.
  • The stochastic nature of EDM and expensive experimental validation limit understanding process dynamics.
  • Machine Learning (ML) offers a cost-effective solution for predictive modeling in EDM.

Purpose of the Study:

  • To develop a generalizable ML framework for analyzing EDM input-output correlations.
  • To investigate the impact of cryogenic electrode treatment on EDM performance.
  • To compare the predictive capabilities of various ML models for EDM parameters.

Main Methods:

  • Implemented four ML models, including Random Forest (RF) and Artificial Neural Networks (ANNs).
  • Utilized independent variables: electrode material, cryogenic conditions (11 levels), pulse current, and pulse duration.
  • Assessed performance metrics: Material Removal Rate (MRR) and Electrode Wear Rate (EWR).

Main Results:

  • RF and ANNs demonstrated superior predictive performance over other models.
  • ANN improved R2 for EWR from 0.973 to 0.9956; RF improved R2 for MRR from 0.980 to 0.9943.
  • Achieved high predictive accuracy (R2: 0.9936–0.9979) and significantly reduced prediction errors (e.g., ANN reduced EWR MSE from 5.79 to 0.68).

Conclusions:

  • The proposed ML framework effectively predicts EDM performance with high accuracy.
  • Cryogenic electrode treatment, analyzed via ML, offers insights into optimizing EDM processes.
  • RF and ANN models provide a reliable, less experimental approach to understanding EDM dynamics.