Additive Manufacturing and the Rise of Digital Inventory

Abstract

The chapter discusses how additive manufacturing (AM) is helping speed the shift of the physical stockpile to a digital inventory on-demand, file-based production as close as possible to the point of need. This is transforming the coming times, reducing logistics costs, enhancing customization, and servicing of products. Approved, repeat bespoke design data, manufacturing parameters and data streams, in situ sensor, and processing transfers can be used to enable approved repeat manufacturing on digital inventories. But this poses an increase in the attack surface; thus, intellectual property theft, tampering files, and compromising supply chain are the considerations to be made. In this regard, provenance tracking, secure version control, and digital watermarking are critical elements of reliable AM processes that will deal with such risks. With the use of machine learning models, there is an increased use of machine learning to optimize the print strategies, predict defects, and change parameters in real time on the basis of layer-wise monitoring. New machine learning algorithms inspired by quantum computing and actual quantum machine learning suggest possible speedups in organization building planning of combinations using high-dimensional datasets and anomaly detection. The problem of interoperability between computer-aided design (CAD), computer-aided manufacturing (CAM), product lifecycle management (PLM), and manufacturing execution systems (MES) systems indicate that standard schemas and application programming interfaces (APIs) adapted to portable digital inventories are required. Highly regulated industries, which include aerospace, medical equipment, and energy industries, demand checkable process histories between design intent and the material performance. Privacy-preserving analytics, which uses multisite operations, is implemented by federated learning and secure enclaves; this can cause sharing of insights without disclosure of proprietary information. Zero-trust architectures, heavily encrypted data, and hardware-integrated roots of trust are needed to address the risk of internal and external threat to integrity in the build features. Lifecycle analyses suggest that digital inventories have the capacity to decrease the waste and transportation-related emissions in case of prioritizing the renewable energy sources and sustainable materials. The policy frameworks and certification procedures should evolve so that digitally native components and software-defined manufacture processes can formally be identified. All in all, this chapter reveals to us that integrating AM with secure and well-managed digital inventories will make the supply chains resilient, adaptive, and sustainable.