Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0

TLDR

Additive manufacturing requires integrated networking, embedded controls, and cloud computing to improve efficiency, yet no ready cloud‑based system exists. This study aims to develop a cyber additive manufacturing framework that integrates an expert system with the Internet of Things. The framework employs an ANN‑based expert system trained on CAD data and a knowledge base to classify part designs and recommend optimal AM processes, while a Node‑RED IoT interface and API query machine availability for real‑time predictions. The two‑stage ANN model achieved over 90% prediction accuracy, and the research establishes a foundation for a cyber additive design system that dynamically allocates digital designs to different AM techniques across the network.

Abstract

Additive Manufacturing (AM) requires integrated networking, embedded controls and cloud computing technologies to increase their efficiency and resource utilisation. However, currently there is no readily applicable system that can be used for cloud-based AM. The objective of this research is to develop a framework for designing a cyber additive manufacturing system that integrates an expert system with Internet of Things (IoT). An Artificial Neural Network (ANN) based expert system was implemented to classify input part designs based on CAD data and user inputs. Three ANN algorithms were trained on a knowledge base to identify optimal AM processes for different part designs. A two-stage model was used to enhance the prediction accuracy above 90% by increasing the number of input factors and datasets. A cyber interface was developed to query AM machine availability and resource capability using a Node-RED IoT device simulator. The dynamic AM machine identification system developed using an application programme interface (API) that integrates inputs from the smart algorithm and IoT interface for real-time predictions. This research establishes a foundation for the development of a cyber additive design for manufacturing system which can dynamically allocate digital designs to different AM techniques over the cyber network.