Dishwasher Washware Recognition Using Neural Network Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing dishwashers require users to manually select pre-set washing programs based on their knowledge of washware type and material, leading to potential mistreatment, energy inefficiency, and damage to items.
Innovation Solution
A dishwasher equipped with a washware recognition system using image capturing devices and a neural network to automatically identify washware characteristics, determining a suitable treatment cycle without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually select pre-set washing programs, then the dishwasher can treat washware, but the treatment may be suboptimal leading to damage or repeated washing
Solution Approach 1:
The dishwasher automatically identifies washware type and material using image capturing devices and neural networks, then autonomously selects the optimal washing program without requiring user knowledge or manual selection. The system serves itself by making intelligent decisions based on detected washware characteristics.
Solution Approach 2:
The manual selection process is replaced with an automated optical detection system using image capturing devices and neural network algorithms. The mechanical action of user selection is substituted with electronic image processing and automated program selection based on detected washware properties.
2Loss of energy
If users select inappropriate pre-set programs, then energy and materials are wasted, but automatic identification adds system complexity
Solution Approach 1:
The replacement of manual program selection with automated optical detection and neural network-based identification enables precise matching of washing programs to actual washware characteristics. This substitution eliminates energy waste from inappropriate program selection while the added complexity is confined to the detection and processing subsystems.
Solution Approach 2:
The system changes the parameter of program selection from user-defined to system-determined based on detected washware parameters (type, material). This parameter transformation enables optimized energy consumption by matching washing intensity, temperature, and duration to the actual washware requirements rather than user assumptions.
3Measurement precision
If multiple image capturing devices are used to detect washware characteristics, then identification accuracy improves, but device complexity increases
Solution Approach 1:
The detection system is segmented into multiple specialized image capturing devices, each potentially optimized for specific detection tasks. The neural network then processes these segmented data streams to achieve high identification accuracy. This segmentation allows each component to focus on specific aspects of washware detection.
Solution Approach 2:
Multiple image capturing devices are merged into a unified detection system where their data is combined and processed by a single neural network. This merging approach achieves high measurement precision through multiple data sources while consolidating the processing logic in one computational module, thereby managing system complexity.
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
Ensures optimal treatment of washware, reduces energy consumption, minimizes wear, and prevents damage by automatically selecting the appropriate washware treatment program based on type and material.
Implementation Method 1
a first image capturing device capturing at least one first image of the washware in the rack, a second image capturing device capturing at least one second image
Implementation Method 2
heating the washware in the rack using a heating medium at a predetermined temperature
Implementation Method 3
heating the washware in the rack using a heating medium at a predetermined temperature
Data Source
Figure 1a~1b
Figure 2a~2b
Figure 3a~3d
AI summary
A dishwasher (100) and a method for treating washware, where the dishwasher (100) comprises a washing cavity (110) configured to receive washware (200), a rack (120) removably inserted in the washing cavity (110), and a washware recognition system (300) configured to detect a washware characteristics (310) for each washware (200) in the washing cavity (110). The washware recognition system (300) comprises two image capturing devices (320, 330) capturing at least one first, second and third image (321, 331, 332) of the washware in the rack (120), a neural network (340) configured to, based on the at least one first (321), second (331) and third images (332), determine a washware characteristics (310), and a control unit (360) configured to evaluate the washware characteristics (310) and determine a washware treatment cycle (350) based on the washware characteristics (310).