Multimodal machine learning framework for material selection in injection molding industry
Year: 2025
Type: article
Abstract: The design phase in injection molding is critical, as it directly influences product quality, production efficiency, and overall manufacturing costs. This paper introduces a multimodal machine learning framework to predict product quality during the design phase, whether for new products or modifications of existing ones. The framework integrates regression, classification, and probabilistic modeling to evaluate material characteristics and process parameters, providing dimensional and mechanical performance predictions for defined products. This methodology minimizes defect risks, optimizes process parameters, and refines production systems. The results highlight the framework’s potential to enhance efficiency, reduce waste, lower costs, and facilitate smarter, data-driven manufacturing aligned with Industry 4.0 principles.
Source: IFAC-PapersOnLine
Authors: Alexei Amato, Fabio Daniele, Lorenzo Agbomemewa, Matteo Confalonieri, Paolo Pedrazzoli
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414568468