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Related Concept Videos

Mechanical Characteristics of Steel01:18

Mechanical Characteristics of Steel

The mechanical characteristics of steel are assessed through various tests that evaluate its strength, toughness, and flexibility. These tests include tension, torsion, impact, bending, and hardness assessments, each providing crucial information about steel's suitability for specific applications.
The tension test is fundamental for determining tensile strength. In this test, a steel specimen is stretched using a gripping device until it breaks. The data collected during this test are used to...
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...
Transmission Shafts: Problem Solving01:09

Transmission Shafts: Problem Solving

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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Machines01:19

Machines

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...
Mechanical Systems01:22

Mechanical Systems

Mechanical systems are analogous to to electrical networks where springs and masses play similar roles to inductors and capacitors, respectively. A viscous damper in mechanical systems functions similarly to a resistor in electrical networks, dissipating energy. The forces acting on a mass in such systems include an applied force in the direction of motion, counteracted by forces from the spring, a viscous damper, and the mass's acceleration. This interplay of forces is mathematically described...

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Related Experiment Video

Updated: Jul 16, 2026

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

Intelligent Forging Driven by Mechanism-Data-Knowledge Fusion: A Review.

Haitao Wang1, Guozheng Quan1,2,3, Yichou Lin4

  • 1Chongqing Key Laboratory of Advanced Mold Intelligent Manufacturing, School of Materials Science and Engineering, Chongqing University, Chongqing 400044, China.

Materials (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This review explores intelligent forging by fusing mechanism, data, and knowledge. It assesses current methods and future directions for advanced manufacturing, focusing on validated, explainable systems.

Keywords:
adaptive controldigital twinintelligent forgingintelligent manufacturingmechanism–data–knowledge fusionprocess–structure–property relationshipquality prediction

Related Experiment Videos

Last Updated: Jul 16, 2026

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Intelligent forging systems are crucial for high-performance components but rely on empirical methods and fragmented data.
  • Current approaches lack integration of mechanism-based understanding, data-driven learning, and domain knowledge.

Purpose of the Study:

  • To review and critically assess intelligent forging technologies from a mechanism-data-knowledge fusion perspective.
  • To identify validated methods and future research directions for industrial deployment.

Main Methods:

  • Structured literature search (1996-2026) across major scientific databases.
  • Organization of literature around key intelligent forging concepts: perception, modeling, learning, knowledge representation, digital twins, and control.
  • Analysis of representative case studies across various forging types and materials.

Main Results:

  • Consolidated technologies include FEM simulation and die optimization; methods under validation include hybrid digital twins and adaptive control.
  • Model validation strategies are diverse, encompassing experiments, benchmarks, industrial data, and transferability tests.
  • Large-model-assisted forging shows promise for information retrieval and decision support.

Conclusions:

  • Intelligent forging requires a fusion of mechanism, data, and knowledge for robust process design, prediction, and control.
  • Explainable, validated, and deployable systems are essential for industrial adoption.
  • Future research should focus on hybrid modeling, robust validation, and engineer-supervised AI applications.