A control system and method for a machine for preparing and dispensing hot beverages by controlled infusion of a precursor substance in granular or powder form, based on the recognition and classification of a dose unit inserted in the machine
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Solution Overview
Problem
Existing machines struggle to accurately recognize and classify coffee tablets or other precursor substances in granular or powder form due to deterioration from storage conditions, environmental factors, and image acquisition issues, leading to misclassification.
Innovation Solution
A control system using machine-learning techniques, specifically a neural network, classifies dose units based on graphic recognition markings and synthetic images, correcting for image alterations through data augmentation and dynamic comparison thresholds, ensuring robust classification despite environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dose unit is inserted into the machine, then the beverage preparation process is initiated, but the system cannot reliably determine the type or characteristics of the dose unit, leading to incorrect preparation parameters
Solution Approach 1:
The patent applies color recognition technology to identify dose unit types. The system uses optical sensors to detect the color of the dose unit container, which encodes information about the beverage type or preparation parameters. This allows the system to automatically adjust preparation settings based on the detected color, achieving reliable dose unit recognition without complex mechanical or electronic identification mechanisms.
2Adaptability or versatility
If the system uses simple insertion detection, then the device remains simple, but it cannot classify different dose unit types or characteristics for proper beverage preparation
Solution Approach 1:
The system uses color as an encoding mechanism for dose unit classification. Different colors represent different beverage types, concentrations, or preparation parameters. The optical detection system reads the color information and automatically selects the appropriate preparation protocol, enabling versatile dose unit classification while maintaining relatively simple system architecture.
3Manufacturing precision
If the system manually verifies each dose unit parameter, then preparation accuracy is ensured, but the operation time and user effort increase significantly
Solution Approach 1:
The dose units are pre-coded with color information that encodes their type and preparation parameters. This preliminary encoding allows the system to automatically retrieve the correct preparation settings without requiring manual verification or user input during the preparation process, thus ensuring precision while minimizing time loss.
Solution Approach 2:
The system implements automatic feedback loops where the detected color information immediately triggers the corresponding preparation parameters. The system continuously monitors the preparation process and adjusts parameters based on the initial color-based identification, ensuring precision through automated feedback control without manual intervention.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides accurate and adaptable classification of dose units, independent of machine and environmental conditions, suitable for cost-effective household machines, maintaining precision over the machine's lifespan.
Implementation Method 1
the system uses an optical sensor to detect the color of the inserted dose unit
Data Source
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AI summary
A control system and method for a machine (1) for preparing and dispensing hot beverages by controlled infusion of a precursor substance in granular or powder form, based on the recognition and classification of a dose unit (8) inserted in the machine (1). A control system and method are described for a machine (1) for preparing and dispensing hot beverages by controlled infusion of a precursor substance in granular or powder form arranged in a dose unit (8), wherein an automatic image recognition and processing unit (84), of the machine-learning type, is arranged to classify a dose unit (8) received by the machine into one of a plurality of predetermined classes of dose units on the basis of a set of training images of dose units taken in a learning step. The set of training images of dose units comprises at least a plurality of primary images of dose units (8) including predetermined graphic recognition markings (70, 72) on a reference surface of the dose unit (8), indicative of corresponding classes of dose units, and a plurality of synthetic images obtained by alteration of the plurality of primary images.