Digital Twins and Dependency/Constraint-Aware AI for Digital Manufacturing

JULY 2023 | VOL. 66 | NO. 7 | COMMUNICATIONS OF THE ACM 87 I M A G E B Y M A C R O V E C T O R complex physical industrial machine, products, and people in production. Although the Industry 4.0 vision and directions are supported by major manufacturing companies and technology providers (for example, Siemens, Bosch, and IBM), its technology baseline is not mature enough to address related computing needs. DTs are still in the early stages of development supporting only limited cases of machine monitoring/ visualization and process design/testing. Similarly, existing ML-based solutions for predicting and fixing production problems typically suffer from high inaccuracy, which is due to the complex data dependencies and constraints imposed by the process and the machines, products, and people involved, as related relationships, dependencies, and constraints between them; and digital threads, which are communication channels between the two. DTs provide cyber-physical representation of the physical entities in manufacturing, support their integration in DM solutions, and enable interactions with the physical entities involved. Novel research outcomes include the knowledge graph-based models of the manufacturing entities, model-based creation of DTs, and the management of the DT life cycle.1,2 ˲ Novel dependency/ constraint-aware ML (dcML) models for DM that predict production outcomes (for example, the end-to-end production and also insufficient training data bound by achievable production runs. We have been developing, testing, and deploying DM solutions in food, steel, and composite manufacturing plants that incorporate the following novel research outcome in computing technologies for DM: ˲ Novel DTs for DM that digitally represent the main physical entities in manufacturing. Each DT includes a physical twin, which a complex industrial machine, a product, or a person in production; a virtual twin involves knowledge graph-based representation of the physical entity including its parts, input/output and other variables, as well I N CRE A SIN G PRODUCTIV ITY IN manufacturing has been an elusive goal despite significant advances in factory automation technology and robotics. There are four main challenges currently facing manufacturers: low production efficiency; product defects and inconsistent quality; unforeseen machine maintenance; and high energy use and waste costs. The fourth industrial revolution— also referred to as Industry 4.0—sets out critical technological directions for addressing these grand challenges via data-driven digital manufacturing (DM) solutions incorporating novel computing technology that combines AI/machine learning (ML) and digital twins (DTs)4 for digitally representing Digital Twins and Dependency/Constraint-Aware AI for Digital Manufacturing