Machining Tool Configuration Recommendation Using Hierarchical Probabilities

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current methods for selecting tool configurations in machining rely heavily on heuristic knowledge and experimental trials, failing to provide reliable recommendations for new customer requirements due to the complexity of machining physics and lack of predictive models.

Innovation Solution

A processor-implemented method and system using a hierarchical model to recommend tool configurations by extracting model input parameters from customer data, obtaining probability scores, and determining joint probabilities to suggest optimal configurations based on predefined criteria.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If rule-based systems with expert knowledge are used to select tool configurations, then existing tool selections can be obtained, but new customer requirements cannot be reliably recommended

Engineering Contradiction:

Improvereliability of tool configuration recommendationVSAvoidability to handle new customer requirements

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent replaces the mechanical expert system approach with a machine learning-based probabilistic model. The hierarchical model learns from historical data to predict tool configuration parameters, substituting rule-based mechanics with data-driven probabilistic predictions that can generalize to new requirements while maintaining reliability through learned patterns.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent transforms the deterministic rule-based parameter selection into probabilistic parameter predictions. By modeling tool configuration parameters as probability distributions rather than fixed values, the system can adapt to new requirements while maintaining reliability through the statistical relationships learned from historical data.

Inventive Principle:

Principle #35Parameter changes

2Manufacturing precision

If extensive experimental trials are conducted to validate tool configurations, then accurate selections can be achieved, but time and resource consumption increase significantly

Engineering Contradiction:

Improveaccuracy of tool configuration selectionVSAvoidtime for experimental trials

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary action by training the hierarchical model on historical tool configuration data before actual tool selection is needed. This pre-learning phase captures the complex relationships between machining parameters and optimal tool configurations, enabling accurate predictions without requiring extensive experimental trials for each new selection.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates a probabilistic model that copies the knowledge embedded in historical successful tool configurations. Instead of repeating expensive experimental trials, the system replicates the decision-making patterns learned from past data, providing accurate recommendations by copying proven configurations and adapting them to new requirements.

Inventive Principle:

Principle #26Copying

3Ease of operation

If heuristic knowledge from expert experience is used for tool selection, then existing cases can be handled, but the complexity of machining physics prevents reliable modeling

Engineering Contradiction:

Improveease of tool configuration selectionVSAvoidcomplexity of machining physics modeling

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent replaces the complex mechanical modeling of machining physics with a data-driven machine learning approach. The hierarchical model learns the underlying physics and relationships directly from historical data, substituting the need for explicit mathematical modeling of complex machining phenomena while maintaining ease of operation through automated predictions.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12165083B2Method and system for recommending tool configurations in machining

Publication Date: 2024.12.10 TATA CONSULTANCY SERVICES LTD

AI summary

This disclosure relates generally to recommending tool configurations in machining. The machining tool configuration selection involves the selection of several tool specification parameters concerning the material, geometry and composition of the machining tool. The state-of-the-art methods uses a rule and knowledge-based system to select tool configuration, however these methods do not recommend tool configurations which satisfy customer requirement. Embodiments of the present disclosure uses a hierarchical model which is trained to predict acceptable tool specification parameters for a given requirement by learning the patterns from past tool selection data. Further a probabilistic approach is used to predict the top set of recommendations of tool configurations with a probability score for each prediction. The disclosed method is used for recommending tool configurations in a cylindrical grinding wheel process.