Neural Toolholder for Low-Noise Cutting Condition Detection
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Solution Overview
Problem
The use of smart toolholders with sensors in cutting processes is hindered by significant noise in sensor signals due to data shuttling in hostile machining environments, degrading the quality of tool condition determination.
Innovation Solution
Integrating a crossbar array of resistive memory devices with a neural network within the toolholder, eliminating the need for data shuttling and enabling on-site condition determination, using a power source to power the neural network and sensors, and employing communication devices like radio frequency or infrared transmitters to transmit processed data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is shuttled between sensors and external control device, then tool condition determination can be performed, but significant noise is introduced reducing signal quality
Solution Approach 1:
The patent merges the neural network processing capability directly into the toolholder, combining sensing, processing, and determination functions in one integrated unit. This eliminates the need for data shuttling between separate sensors and external control devices, thereby removing the primary source of noise and improving signal quality while reducing system complexity.
Solution Approach 2:
The toolholder acts as an intermediary device that receives sensor data, processes it through an integrated neural network, and outputs tool condition determinations. This intermediary processing eliminates the need for direct data transmission to external devices, blocking the introduction of external noise while maintaining determination capability.
2Power
If conventional CPU is used for neural network processing, then processing capability is sufficient, but power consumption is too high for toolholder operation
Solution Approach 1:
The patent changes the fundamental parameters of the processing unit by replacing conventional CPU architecture with a specialized neural network processor designed for low power consumption. This parameter change enables the toolholder to perform autonomous neural network processing while operating within the limited power budget available in machining environments.
Solution Approach 2:
The patent substitutes the mechanical/electronic CPU-based processing system with a specialized neural network processing system that is optimized for low power consumption. This substitution maintains or enhances processing capability while dramatically reducing power requirements to levels suitable for toolholder integration.
3Reliability
If multiple sensors are deployed for comprehensive monitoring, then measurement coverage is improved, but data transmission volume and noise increase
Solution Approach 1:
The patent segments the data processing function by performing initial neural network processing locally at the toolholder for each sensor input. This segmentation allows multiple sensors to be deployed for comprehensive monitoring while each sensor's data is independently processed and filtered, preventing the accumulation and transmission of large volumes of raw data and associated noise.
Solution Approach 2:
The patent performs preliminary neural network processing and filtering of sensor data directly at the toolholder before any external transmission. This preliminary action removes noise and extracts only essential tool condition information, thereby enabling comprehensive multi-sensor monitoring while minimizing the volume of data that needs to be transmitted externally.
Data Source
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AI summary
The invention is related to a toolholder (100) for use with a cutting element (200) in a cutting process, wherein the toolholder (100) comprises at least one sensor (114) for detecting an environmental condition; wherein the toolholder (100) comprises a control unit (116) operatively connected to the at least one sensor (114), wherein the control unit (116) comprises a crossbar array (118) of resistive memory devices, and a power source (120) operatively connected to the crossbar array (118), wherein the crossbar array (18) comprises a neural network stored thereon, and wherein the neural network is adapted to determine a condition of the cutting element (200) or of the cutting process based on the environmental condition detected by the at least one sensor (114). The invention also relates to a cutting tool (300) and to a cutting tool assembly (500).