Manufacturing Data Transformation for Position-Based Process Optimization
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Solution Overview
Problem
Existing manufacturing processes, such as glass manufacturing, face challenges in optimizing process parameters due to the complexity of data management and analysis, particularly in handling time-sequenced and positionally-dimensioned data structures.
Innovation Solution
A computer-implemented method and system that transforms time-sequenced manufacturing data into a positionally-dimensioned data structure, using algorithms to determine new process parameter values, and optimizes the manufacturing process based on these values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If time-sequenced data structure is used to store manufacturing data, then data can be collected from multiple data sources, but data analysis complexity increases
Solution Approach 1:
The patent segments the complex time-sequenced data into positionally-dimensioned data structures that organize data by physical location along the manufacturing line rather than by time. This segmentation transforms the data organization from temporal sequences to spatial arrangements, making it easier to analyze and correlate process parameters with quality outcomes at specific manufacturing positions.
Solution Approach 2:
The patent introduces a spatial dimension to the data structure by transforming time-sequenced data into positionally-dimensioned data. Instead of organizing data along a time axis, the system reorganizes it along the physical production line positions, adding a spatial dimension that simplifies the correlation between process inputs and quality outputs.
2Manufacturing precision
If process parameters are adjusted to improve product quality, then quality parameter values improve, but process stability may be affected
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors quality parameter values and process parameter values, correlates them using the positionally-dimensioned data structure, and automatically adjusts process parameters to maintain optimal quality while preserving process stability. The feedback loop uses historical data correlations to make informed adjustments rather than reactive changes.
Solution Approach 2:
The patent performs preliminary analysis of the relationship between process parameters and quality outcomes by building correlation models from historical positionally-dimensioned data. This preliminary work establishes baseline relationships that guide future parameter adjustments, allowing the system to predict quality outcomes before making changes and thus maintain process stability.
3Productivity
If data transformation from time-sequenced to positionally-dimensioned structure is performed, then data analysis efficiency improves, but processing time increases
Solution Approach 1:
The patent performs the data transformation from time-sequenced to positionally-dimensioned structure as a preliminary step that is executed once during system initialization or when new production line configurations are introduced. By performing this transformation upfront rather than in real-time during analysis, the system avoids repeated processing overhead while maintaining high analysis efficiency during operational use.
Data Source
AI summary
Provided are a system, method, and computer program product for optimizing a manufacturing process. The method includes generating a time-sequenced data structure associated with a manufacturing process and transforming the time-sequenced data structure to a positionally-dimensioned data structure by identifying a zone for each parameter of a plurality of parameters, determining a time delay factor for each zone, and generating the positionally-dimensioned data structure using a data matrix transformation based on the time-sequenced data structure, each zone, and each time delay factor. The method also includes identifying a set of empty entries in the time-sequenced data structure or the positionally-dimensioned data structure and imputing data. The method further includes determining a new value for a process parameter value based on the positionally-dimensioned data structure and at least one algorithm and optimizing the manufacturing process based on the new value.