Communications of the ACM
Digital Twins and Dependency/Constraint-Aware AI for Digital Manufacturing
Credit: Macrovector
Increasing productivity 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 complex physical industrial machine, products, and people in production.
While digital manufacturing powered by digital twins and dependency/constraint-aware ML is still in early stages, it has shown its potential in improving manufacturing productivity by 20%-30%.
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, and also insufficient training data bound by achievable production runs.
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