Knowledge-based feedback of product information into product design

This paper introduces a knowledge-based feedback system for product design, using XML and CSV files to integrate product usage data. Through data mining, this data is combined with existing knowledge to support design teams. The knowledge base, created using methods like rule-based systems, Bayesian networks, neural networks, and knowledge graphs, supports decision-making. The knowledge graph, implemented in Neo4j, is characterized by integrating diverse knowledge sources. The concept was validated using simulated data from 3D printers, demonstrating its effectiveness, and anticipates customer needs and optimizes product generations, fostering the creation of customized products.