Assembly Error Correction With Adaptive Instructions
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Solution Overview
Problem
Conventional assembly-line workflows rely heavily on human monitoring and expertise, leading to high likelihoods of undetected errors being propagated downstream, with limited electronic monitoring and no mechanism to learn from mistakes or provide on-the-fly adjustments to compensate for errors in upstream steps, resulting in inefficiencies and product variations.
Innovation Solution
Implementing a system with image capture devices and an assembly instruction module that uses machine-learning models to detect errors, evaluate deviations, and automatically adjust assembly instructions in real-time, providing feedback to operators through dynamic visual or other formats, such as video or augmented reality, to minimize deviations and improve product quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human monitoring and expertise are used for detecting manufacturing errors, then error detection capability is improved, but the likelihood of undetected errors increases and errors are propagated downstream
Solution Approach 1:
The patent replaces human monitoring and expertise with electronic monitoring systems including image capture devices, sensors, and machine learning models. This substitution eliminates human limitations such as fatigue, inconsistency, and narrow training, providing continuous, standardized detection across all assembly steps while maintaining high error detection capability.
Solution Approach 2:
The system implements real-time feedback loops where detected errors trigger automatic corrections at downstream steps. The electronic monitoring system continuously provides feedback to adjust assembly instructions and compensate for upstream errors, preventing error propagation and maintaining product quality consistency.
2Speed
If electronic monitoring is implemented to detect errors, then error detection speed is improved, but the ability to provide on-the-fly adjustments and learn from mistakes is limited
Solution Approach 1:
The system implements real-time feedback loops where detected errors trigger automatic corrections at downstream steps. The electronic monitoring system continuously provides feedback to adjust assembly instructions and compensate for upstream errors, preventing error propagation and maintaining product quality consistency.
Solution Approach 2:
The system performs preliminary actions by pre-planning corrective steps for detected errors. When an error is identified, the system automatically determines and prepares the sequence of corrective actions needed at downstream steps before execution, enabling rapid response without delays.
Solution Approach 3:
The system dynamically adapts assembly instructions based on real-time error detection. Machine learning models continuously learn from detected errors and adjust monitoring parameters, detection thresholds, and corrective actions to optimize performance, enabling the system to evolve and improve over time.
3Reliability
If human operators are trained to perform narrow sets of tasks, then task expertise is improved, but the ability to recognize and modify workflows to rectify errors is reduced
Solution Approach 1:
The patent replaces human operators with robotic assembly systems that execute standardized instructions with high precision. This substitution maintains task execution accuracy while eliminating the limitations of human training and expertise, as the robotic system can be programmed with comprehensive knowledge of all assembly variations and error corrections.
Solution Approach 2:
The system dynamically adapts assembly instructions based on real-time error detection. Machine learning models continuously learn from detected errors and adjust monitoring parameters, detection thresholds, and corrective actions to optimize performance, enabling the system to evolve and improve over time.
4Reliability
If corrective action is taken on individual human nodes, then local error correction is improved, but system-wide learning and consistent improvement are prevented
Solution Approach 1:
The system implements real-time feedback loops where detected errors trigger automatic corrections at downstream steps. The electronic monitoring system continuously provides feedback to adjust assembly instructions and compensate for upstream errors, preventing error propagation and maintaining product quality consistency.
Solution Approach 2:
The system performs self-correction by automatically identifying errors and implementing corrective actions without human intervention. The electronic monitoring system and machine learning models continuously self-optimize by learning from detected errors, enabling the system to improve overall efficiency while maintaining local correction capabilities.
Data Source
AI summary
Aspects of the disclosed technology provide a computational model that utilizes machine learning for detecting errors during a manual assembly process and determining a sequence of steps to complete the manual assembly process in order to mitigate the detected errors. In some implementations, the disclosed technology evaluates a target object at a step of an assembly process where an error is detected to a nominal object to obtain a comparison. Based on this comparison, a sequence of steps for completion of the assembly process of the target object is obtained. The assembly instructions for creating the target object are adjusted based on this sequence of steps.