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Related Concept Videos

Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

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.
However, in reality, no machine can be truly ideal, and all of them experience some...
Design of Transmission Shafts01:16

Design of Transmission Shafts

The design of a transmission shaft is governed by two primary specifications: the power it transmits and its rotational speed. These parameters guide the selection of the shaft's material and cross-sectional dimensions, ensuring that the material's maximum shearing stress remains within the elastic limit while transmitting the desired power at the given speed. The system's power is intrinsically linked to the applied torque. The torque applied to the shaft can be calculated by reconfiguring the...
Design of Transmission Shafts - Stress Analysis01:15

Design of Transmission Shafts - Stress Analysis

Designing a transmission shaft requires a thorough understanding of the stresses induced by bending moments and torques, especially in systems where power is transferred through gears. These forces create force-couple systems at the centers of the shaft's cross-sections, leading to both transverse and torsional loading. Although shearing stresses from transverse loads are typically smaller than those from torques and are often overlooked, the significant normal stresses from these loads...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Thin-Walled Hollow Shafts

In analyzing a thin-walled hollow shaft subjected to torsional loading, a segment with width dx is isolated for examination. Despite its equilibrium state, this segment faces torsional shearing forces at its ends. These forces are quantitatively described by the product of the longitudinal shearing stress on the segment's minor surface and the area of this surface, leading to the concept of shear flow. This shear flow is consistent throughout the structure, indicating a uniform distribution of...

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Related Experiment Video

Updated: May 11, 2026

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Intelligent hybrid optimization of sustainable machining parameters for Inconel 718 using ANN driven evolutionary and

Jasgurpreet Singh Chohan1,2, Rajat Yadav3, Kumel K Nagori4

  • 1Department of Mechanical Engineering, Faculty of Engineering & Technology, Marwadi University Research Center, Marwadi University, Rajkot, Gujarat, India.

Scientific Reports
|May 9, 2026
PubMed
Summary

Sustainable machining of Inconel 718 is optimized using advanced methods. Cryogenic CO₂ lubrication significantly enhances performance, reducing key responses by up to 43% compared to dry machining.

Keywords:
Artificial Neural NetworkGenetic AlgorithmHybrid optimizationInconel 718Particle Swarm OptimizationSustainable machining

Related Experiment Videos

Last Updated: May 11, 2026

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Sustainable Manufacturing

Background:

  • Inconel 718 is a high-performance alloy demanding advanced machining strategies.
  • Optimizing machining processes is crucial for efficiency and sustainability in aerospace and energy sectors.

Purpose of the Study:

  • To investigate sustainable machining performance of Inconel 718 under various lubrication conditions.
  • To develop and optimize machining parameters for improved cutting force, tool wear, surface roughness, and temperature.
  • To evaluate hybrid optimization frameworks for multi-objective machining parameter selection.

Main Methods:

  • Taguchi L16 orthogonal array design for experimental planning.
  • Artificial Neural Network (ANN) for predicting machining responses.
  • Hybrid optimization using ANN with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).
  • Evaluation of dry, minimum quantity lubrication (MQL), Nano-MQL, and cryogenic CO₂ lubrication.

Main Results:

  • ANN model achieved high prediction accuracy (R² > 0.97).
  • Cryogenic CO₂ machining reduced key responses by up to 43% compared to dry machining.
  • ANN-GA model showed superior performance (86.7% success rate), while ANN-PSO offered faster convergence.
  • The study identified optimal machining parameters for sustainable Inconel 718 processing.

Conclusions:

  • Hybrid optimization frameworks (ANN-GA, ANN-PSO) effectively optimize sustainable machining of Inconel 718.
  • Cryogenic CO₂ lubrication presents a highly effective strategy for enhancing machining performance and environmental responsibility.
  • The proposed methodology offers a pathway to efficient and sustainable manufacturing practices for challenging alloys.