Plug Connection Quality Verification via Force-Time Profiling
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Solution Overview
Problem
The quality of plug connections during assembly is dependent on the assembler's action and lacks a reliable method for consistent quality checking.
Innovation Solution
A method using a force sensor on a glove to determine the force-time profile of a plug connection, which is analyzed to extract relevant features and classified using a machine-learned classifier, such as a random forest classifier, to differentiate between correct and faulty connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual assembly by assembler is used, then ease of operation is improved, but reliability of plug connection quality deteriorates
Solution Approach 1:
The system implements feedback by measuring the force-time profile during plug insertion and comparing it against learned patterns from correct assemblies. The force sensor provides real-time data about the assembly process, and the machine-learned classifier gives feedback on whether the plug connection is correct or faulty, enabling quality control while maintaining manual assembly operations
Solution Approach 2:
The patent replaces mechanical quality inspection methods with a sensor-based measurement system. Instead of manual visual inspection or mechanical gauges, a force sensor measures the force-time profile during insertion, and a machine-learned classifier automatically determines connection quality, substituting mechanical judgment with electronic sensing and computational analysis
2Measurement precision
If force sensor measurement and machine learning classification are implemented, then measurement precision of plug connection quality is improved, but device complexity increases
Solution Approach 1:
The system changes parameters by measuring force as a function of time during plug insertion, creating a force-time profile as a new measurement parameter. This temporal parameterization of the assembly process enables precise characterization of correct versus faulty connections, transforming a simple binary check into a multi-dimensional measurement that captures the dynamics of proper assembly
Solution Approach 2:
The system performs preliminary action by collecting training data from correct plug assemblies before actual production use. The machine-learned classifier is trained in advance on force-time profiles from properly assembled plugs, enabling it to automatically recognize correct assembly patterns during manufacturing without requiring complex real-time decision logic
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables reliable and efficient identification of correct or faulty plug connections by accounting for multiple features, including engagement points and principal components, improving quality assurance in assembly processes.
Implementation Method 1
A force sensor on a glove of the assembler can be used for this purpose. The force-time profile can thus indicate the force (as a function of time) applied by the assembler
Data Source
AI summary
A method checks a plug connection, in which a first plug part is connected to a second plug part. The method determines a force-time curve of a force applied by an assembler during an assembly process of a plug connection. In addition, the method determines characteristic values of a plurality of characteristics of the force-time curve. The method also classifies the plug connection by use of a machine-learned classifier on the basis of the characteristic values of the plurality of characteristics.