Neural Network Sewing Machine Control for Adaptive Stitching
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Solution Overview
Problem
Existing sewing machines lack advanced control systems that can adapt to various sewing tasks and user interactions, leading to inefficiencies and potential errors in stitch formation and material manipulation.
Innovation Solution
A sewing machine equipped with a data gathering device, data storage, and a processor that utilizes a neural network to process data from multiple sources, including the machine, environment, and user interactions, enabling intelligent control of the sewing operation and user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a neural network control system is added to the sewing machine, then the adaptability and intelligence of the sewing machine is improved, but the device complexity increases
Solution Approach 1:
The neural network control system serves multiple functions: it processes data from sensors, identifies sewing tasks and materials, adjusts machine parameters, and provides user feedback. This single multi-functional system replaces what would otherwise require multiple separate control modules, achieving adaptability across various sewing tasks while managing complexity through functional integration.
Solution Approach 2:
The neural network enables the sewing machine to autonomously adjust its operation by processing sensor data and generating control decisions without continuous human intervention. The system self-regulates sewing parameters, identifies materials, and adapts to different tasks automatically, reducing the need for manual configuration and improving adaptability.
2Measurement precision
If data gathering devices and neural network processing are integrated into the sewing machine, then the measurement precision and control accuracy are improved, but the device complexity and cost increase
Solution Approach 1:
The patent integrates data gathering devices, neural network processing, and control functions into a unified system. The sensor data acquisition, neural network analysis, and actuator control are merged into a single integrated control architecture, improving measurement precision and control accuracy while avoiding the complexity of multiple separate systems.
Solution Approach 2:
The patent replaces traditional mechanical control systems with an intelligent software-based neural network system. Instead of using complex mechanical mechanisms for task identification and parameter adjustment, the system uses neural network algorithms to process sensor data and generate control decisions, achieving high precision with simpler physical hardware.
3Reliability
If the neural network continuously processes data from multiple sensors, then the reliability and error reduction are improved, but the energy consumption increases
Solution Approach 1:
The neural network processes sensor data at periodic intervals rather than continuously, analyzing inputs at key moments in the sewing cycle. This periodic processing maintains reliability by regularly monitoring sewing operations while significantly reducing energy consumption compared to continuous real-time processing of all sensor data.
Solution Approach 2:
The system processes only the most critical sensor data and parameters through the neural network, focusing computational resources on key decision points. By selectively processing partial data sets rather than all available sensor information, the system maintains sufficient reliability for accurate sewing control while minimizing energy consumption.
Data Source
AI summary
An exemplary sewing machine includes a sewing head, a needle bar holding a needle, a motor for moving the needle during a sewing operation, a user interface, a data gathering device, a data storage device, and a processor. The data gathering device gathers data related to at least one of the sewing machine, an environment surrounding the sewing machine, a sewing material, the sewing operation performed by the sewing machine, and one or more interactions of a user with the sewing machine. The data storage device stores gathered data and data related to a neural network. The processor processes the gathered data through the neural network to generate processed data. Based on the processed data, the processor controls at least one of the user interface, the data storage device, and the motor.