Predictive maintenance on 3D printers

Additive Industries is a market leader in the field of additive manufacturing. The company is accelerating industrial additive manufacturing by offering a modular 3D printing system, MetalFAB1, together with an integrated information platform to satisfy the high demand their customers impose on the quality of the printed objects by MetalFAB1. Their clients are representatives from companies involved in aerospace, automotive, medical technology, and other high-tech equipment manufacturing. Typically, the MetalFAB1 is used to print 3D objects in high variety and low volume.MetalFAB1 is a modular highly complex device and the company's customers expect a high level of durability and reliability of this device. Therefore, Additive Industries has to optimize its processes and technologies continuously. The digital twin is an innovative generic concept to achieve that optimization and to enhance the analysis and the exploration of the MetalFAB1 digital footprint. Within the digital twin concept, the company creates a digital replica of the product or process in real-time, optimizes its performance and efficacy as well as detects physical issues, and predicts outcomes accurately.To gauge a better understanding of the project, the details about the company’s vision, mission and strategic objectives, products, different processes, and the current challenges are learnt. One of the main challenges faced by the case organization is the prediction of the breakdown of MetalFAB1.

In agreement with the company, the primary goal of the project is defined, which is related to designing an appropriate method to detect recoater jam to overcome the breakdown caused during the printing process. As a secondary goal, we aim to generate knowledge from the data created by the MetalFAB1 sensors regarding the recoater jamming phenomena and proper ways to process the corresponding sensory datasets.