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Unsupervised ML and fuzzy logic for stl model optimization
An application of toy 3D mold design
Raw Data - Training
Raw Data - Testing/Production
Preprocessing - Training Data
Preprocessing - Testing/Production Data
Dataset - Training
Dataset - Testing/Production
Model Training
Trained Model
Model Testing
Tested Model
Motivation
The primary objective of AIMODO experiment is to develop an AI-based tool for optimizing the topology design of mold parts using computer graphics and evolutionary algorithm techniques. Reducing the number of different parts required for toy molding is crucial for enhancing toy quality, conserving resources, and lowering production and molding costs. Idea and the goal is to create an AI assistive tool to support human designer in redesigning toys to optimize for identical parts, thereby minimizing the variety of parts needed.
Objective
Main challenge of production by plastic injection is considered as molding design optimization due to both molding costs and the need of vast time to optimize the design to ensure high performance parts of the products to be manufactured with the minimum number of different parts which leads cost and resource efficiency.
Although computer aided design softwares assists human designers with integrated solutions including moldability analysis, those analysis mainly works on each piece of parts to be molded separately and gives a base only for compliance of different parts to each other instead of evaluating whole production together and slicing into moldable parts.
Our application's primary objective is to drive optimization within industrial processes of plastic injection molding and production. By leveraging its capabilities, industrial facilities can streamline product design during the initial phases. This optimization results in a reduced number of molds required for production, leading to significant economic benefits. Additionally, the application supports designers by saving time and enhancing efficiency, ultimately contributing to more cost-effective and time-efficient product development.
Reduction of production costs reflects affordable products for the end users along with the competitiveness of the company. Resource efficiency’s one of the critical benefits is decreased environmental pollution including carbon emissions which is another contribution to the society.
Use of AI
Aimed optimization has been achieved through the application of unsupervised machine learning algorithms and fuzzy logic principles.