Screw-Locking Error Alarm Using Confidence-Interval Detection

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Solution Overview

Problem

Industrial automatic screw-locking devices face compatibility issues between hardware and command systems, leading to poor versatility and inefficiencies in screw-locking procedures due to closed, incompatible systems.

Innovation Solution

A computing device-based method for generating a confidence interval database of screw-locking error alarms, using APIs and data models to remotely acquire and process locking parameter data, including locking angle, torque, and speed, to identify normal and abnormal locking states and issue alerts through a network-connected system.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If a combination of PLC control and manual detection is used, then the screw-locking procedure can be automated, but the hardware systems and command systems become closed and incompatible, reducing versatility

Engineering Contradiction:
Improveautomatic screw-locking procedureVSAvoidcompatibility of hardware and command systems
Core Design Contradiction:
Extent of automationVSAdaptability or versatility

Solution Approach 1:

The patent implements a unified command system that can interface with multiple types of locking devices and detection systems through standardized protocols. The system architecture allows different hardware components (PLC, sensors, actuators) to communicate through a common interface layer, enabling the same control system to work with various screw-locking devices and detection methods without requiring separate dedicated systems for each component type.

Inventive Principle:
Principle #6Universality (Multi-functionality)

2Device complexity

If traditional manual detection methods are used, then system complexity is reduced, but the accuracy and reliability of locking status detection deteriorates

Engineering Contradiction:
Improvesystem structureVSAvoidlocking status detection accuracy
Core Design Contradiction:
Device complexityVSMeasurement precision

Solution Approach 1:

The patent replaces manual visual inspection and mechanical detection methods with automated optical sensors and machine vision systems. These sensors can detect screw locking status, torque application, and positional accuracy with high precision automatically. The system substitutes human operators' manual detection with electronic sensing systems that provide more consistent and accurate measurements while reducing the need for complex mechanical detection mechanisms.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If multiple separate systems are used for different screw-locking tasks, then each system can be optimized for its specific function, but the overall system complexity increases and compatibility decreases

Engineering Contradiction:
Improvefunction-specific optimizationVSAvoidnumber of separate systems
Core Design Contradiction:
ReliabilityVSDevice complexity

Solution Approach 1:

The patent combines multiple separate control systems into a single integrated platform that can manage various screw-locking operations. The unified system incorporates different detection methods (optical, mechanical, torque sensing) and control mechanisms under one architecture, allowing the system to handle different screw types, locking requirements, and detection needs through configurable parameters rather than requiring separate dedicated systems for each function.

Inventive Principle:
Principle #5Merging (Combining)

Data Source

PatentUS11372388B2Locking error alarm device and method
Publication Date: 2022.06.28 FOXCONN PRECISION ELECTRONICS TAIYUAN CO LTD
  • US11372388B2 patent drawing
  • US11372388B2 patent drawing
  • US11372388B2 patent drawing

AI summary

A method of alarm of a screw-locking error includes retrieving a confidence interval of screw-locking parameter data of different screw specifications, setting locking parameter data of a screw-locking process according to a range of the confidence intervals, acquiring locking parameter data in real time, collating the acquired locking parameter data, analyzing the collated locking parameter data to obtain normal locking parameter data and abnormal locking parameter data, and analyzing the abnormal locking parameter data to obtain an error type of each abnormal locking parameter data. The abnormal locking parameter data and the corresponding error type of the abnormal locking parameter data are reported.