Gob Forming Parameter Control Using Upstream Sensor AI
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Achieving consistent gob shape and size in glass container manufacturing is challenging due to variations in molten glass viscosity, equipment wear, raw material composition, and environmental factors, relying heavily on human operator expertise and manual adjustments.
Innovation Solution
A machine learning model processes upstream sensor data from the gob forming subsystem to determine process parameters, adjusting feeder settings for consistent gob formation, reducing dependency on human operators and enhancing process reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustments by specialist operators are used to maintain gob formation, then gob characteristics can be adjusted based on experience, but the process is time-consuming and dependent on human expertise
Solution Approach 1:
The system enables self-service by implementing an automated feedback loop where sensors continuously monitor gob characteristics and the controller automatically adjusts feeder parameters without requiring manual intervention from operators, allowing the system to self-correct and maintain optimal gob formation
Solution Approach 2:
The system implements real-time feedback by using sensors to monitor gob characteristics (such as gob weight, dimensions, or shape) and feeding this information back to the controller, which then automatically adjusts feeder parameters to maintain consistent gob formation despite variations in raw materials or environmental conditions
2Adaptability or versatility
If manual adjustments are made to account for variations in viscosity, equipment wear, and environmental factors, then gob formation can be adapted, but the process reliability depends on operator availability and skill
Solution Approach 1:
The system achieves self-service by automatically detecting variations in manufacturing conditions through sensors and autonomously adjusting feeder parameters through the controller, eliminating dependency on operator skill and availability while maintaining consistent adaptation to changes in viscosity, equipment wear, and environmental factors
Solution Approach 2:
The feedback mechanism continuously monitors gob characteristics and manufacturing conditions, allowing the system to automatically adapt to variations in raw material composition, equipment wear, and environmental factors, thereby improving process reliability by removing human variability from the adjustment process
3Reliability
If automated systems are implemented to reduce manual intervention, then process reliability and consistency improve, but system complexity increases
Solution Approach 1:
The feedback-based automated system improves reliability by continuously monitoring gob characteristics and automatically adjusting feeder parameters, ensuring consistent gob formation while managing complexity through a closed-loop control architecture that integrates sensors, controllers, and actuators in a coordinated manner
Data Source
AI summary
A system for and method of determining process parameters for a gob forming subsystem are disclosed. Upstream sensor data, representing information captured by one or more sensors installed upstream of a feeder of the gob forming subsystem, are obtained. One or more gob forming process parameters are determined as a result of executing an artificial intelligence (AI) model that takes, as input, the upstream sensor data and that generates, as output, process parameter data representing the one or more gob forming process parameters.