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Screws are characterized by a helical ridge known as a thread wrapped around a cylindrical shaft. They are commonly used as fasteners to hold objects together or to transmit power and motion in machines. One type of screw that is particularly useful for transmitting power is the square-threaded screw.
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In wood construction, fasteners are essential for securing components together, with the connection strength largely dependent on the direct bearing between members. Various types of fasteners are employed, each suited to specific applications and structural requirements.
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Steel Fastening Techniques01:17

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Steel sections can be joined together through various fastening techniques including riveting, bolting, and welding, each suitable for different structural requirements and conditions.
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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.
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Screw: Problem Solving01:21

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In mechanical engineering, the interaction between a threaded screw shaft and a plate gear involves analyzing the resisting torque on the plate gear that can be overpowered when a specific torsional moment is applied to the shaft. To better comprehend this concept, consider a generic situation with a threaded screw shaft with a given mean radius and lead and a plate gear with a specified mean radius. The coefficient of static friction between the screw and gear is also provided.
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Related Experiment Video

Updated: Jul 30, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Robust Fastener Detection Based on Force and Vision Algorithms in Robotic (Un)Screwing Applications.

Paul Espinosa Peralta1, Manuel Ferre1, Miguel Ángel Sánchez-Urán1

  • 1Centre for Automation and Robotics (CAR) UPM-CSIC, Universidad Politécnica de Madrid, 28006 Madrid, Spain.

Sensors (Basel, Switzerland)
|May 13, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a robust robotic vision system for detecting fastener position and rotation in industrial tasks. The system achieves high accuracy in various conditions, improving manufacturing and maintenance applications.

Keywords:
fastener detectionrobot visionsegmentation neural networkssocket wrenchunfastening process

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Area of Science:

  • Robotics and Industrial Automation
  • Computer Vision
  • Manufacturing Technology

Background:

  • Industrial maintenance and manufacturing sectors face challenges in automated fastener handling.
  • Accurate detection of fastener position and rotation is crucial for robotic (un)screwing tasks.
  • Existing vision systems may struggle with varying fastener conditions and lighting.

Purpose of the Study:

  • To develop a novel and robust robotic vision system for detecting fastener center and rotation.
  • To enable robots to perform (un)screwing tasks on parallel surfaces accurately.
  • To evaluate the system's performance across different fastener conditions and lighting environments.

Main Methods:

  • A vision system incorporating an industrial camera with dynamic exposure, a tunable liquid crystal lens (TLCL), and near-infrared (NIR) illumination.
  • Development of segmentation neural networks (SNN) and vision algorithms for fastener detection.
  • Integration with a collaborative robot featuring a force sensor and a virtual program logic controller (PLC) for control and validation.
  • Mathematical analysis based on international standards (ISO) and patent reviews for error tolerance determination.

Main Results:

  • The system achieves high accuracy, with 99.86% for vertex detection and 100% for center detection.
  • Processing time for the SNN and algorithms is 73.91 ms on an Intel Core I9 CPU.
  • Translation tolerances increase with fastener size (0.75 for M6 to 2.50 for M24), while rotation tolerance decreases (5.5° for M6 to 3.5° for M24).
  • The methodology effectively handles outliers and distorted masks caused by non-constant illumination.

Conclusions:

  • The proposed vision system offers a robust and accurate solution for fastener detection in industrial robotic applications.
  • The system demonstrates adaptability to various fastener conditions (flawless, worn, rusty) and lighting.
  • This development contributes significantly to advancing industrial robotics and automation in manufacturing and maintenance.