3D-Printed Button Interfaces for Function Identification
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Solution Overview
Problem
User interfaces with generic buttons lacking physical markings or indicators pose a challenge for new operators who are unfamiliar with the device, as they cannot determine the function of the buttons.
Innovation Solution
A method involving operation monitoring, machine learning, and additive manufacturing is used to record interactions with the original interface, determine the function using a trained predictive model, and fabricate a new interface with functional indicators through 3D printing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If generic buttons without physical markings are used in the user interface, then the device structure is simplified and manufacturing cost is reduced, but new operators cannot determine the function of the buttons
Solution Approach 1:
The system performs preliminary action by capturing operator interactions with generic buttons and using machine learning to determine button functions before fabricating customized buttons with indicators. This allows the buttons to be customized with appropriate visual indicators based on learned functionality, resolving the contradiction between manufacturing simplicity and operational clarity.
Solution Approach 2:
The invention applies local quality by adding specific visual indicators to specific buttons based on their determined functions. Each button receives customized markings or indicators tailored to its specific function, while maintaining the overall simplicity of the generic button structure. This allows operators to easily identify button functions without complicating the fundamental button design.
2Ease of operation
If customized buttons with functional indicators are fabricated using additive manufacturing, then new operators can easily identify button functions, but the manufacturing process becomes more complex and time-consuming
Solution Approach 1:
The system uses copying by creating digital 3D models of customized buttons with indicators based on the determined button functions. These digital models are then used to guide additive manufacturing processes, allowing for automated production of customized buttons without requiring complex manual manufacturing procedures. This reduces the perceived complexity by leveraging digital replication and automated fabrication.
Solution Approach 2:
The invention applies parameter changes by modifying only the surface characteristics and visual indicators of the buttons through additive manufacturing, while maintaining the core button structure and dimensions. This allows for customized buttons to be produced with minimal changes to the overall manufacturing parameters, reducing process complexity while still achieving the desired functional identification.
3Measurement precision
If machine learning is used to determine button functions from interaction sequences, then accurate function identification is achieved, but the system requires complex data processing and modeling
Solution Approach 1:
The system implements feedback by continuously monitoring operator interactions with the device and using this feedback to refine machine learning models for button function determination. The captured interaction sequences provide feedback data that improves the accuracy of function identification over time, while the iterative nature of the process allows the system to adapt without requiring overly complex initial modeling.
Solution Approach 2:
The invention applies partial action by focusing the machine learning analysis on specific interaction patterns and sequences that are most indicative of button functions, rather than analyzing all possible device operations. This selective approach achieves accurate function determination while reducing the overall complexity of the data processing requirements.
Data Source
AI summary
Methods and systems for interface modulation include recording interactions by a device operator with an original interface of the device. A function of the original interface is determined using a trained predictive model that receives sequences of the interactions. A three-dimensional (3D) design is generated for a new interface that includes an indicator of the function. A new interface is fabricated according to the 3D design using an additive manufacturing process.