Università degli studi di Modena e Reggio Emilia

Smart factories are complex systems where many different components need to interact and cooperate in order to achieve common goals. In particular, devices must be endowed with the skill of learning how to react in front of evolving situations and unexpected scenarios. In order to develop these capabilities, we argue that systems will need to build an internal, and possibly shared, representation of their operational world that represents causal relations between actions and observed variables. Within this context, digital twins will play a crucial role, by providing the ideal infrastructure for the standardisation and digitisation of the whole industrial process, laying the groundwork for the high-level learning and inference processes. In this paper, we introduce a novel hierarchical architecture enabled by digital twins, that can be exploited to build logical abstractions of the overall system, and to learn causal models of the environment directly from data. We implement our vision through a case study of a simulated production process. Our results in that scenario show that Bayesian networks and intervention via do-calculus can be effectively exploited within the proposed architecture to learn interpretable models of the environment. Moreover, we evaluate how the use of digital twins has a strong impact on the reduction of the physical complexity perceived by external applications.

In this paper, we present the re-engineering process of an assembly line that features speed reducers and multipliers for agricultural applications. The “as-is” line was highly inefficient due to several issues, including the age of the machines, a non-optimal arrangement of the shop floor, and the absence of process standards. The assembly line issues were analysed with Lean Manufacturing tools, identifying irregularities and operations that require effort (Mura), overload (Muri), and waste (Muda). The definition of the “to-be” line included actions to update the department layout, modify the assembly process, and design the line feeding system in compliance with the concepts of Golden Zone (i.e., the horizontal space more ergonomically and easily accessible by the operator) and Strike Zone (i.e., the vertical workspace setup in accordance to ergonomics specifications). The re-engineering process identified a critical problem in the incorrect assembly of the oil seals, mainly caused by the difficulty in visually identifying the correct side of the component, due to different reasons. Convolutional neural networks were used to address this issue. The proposed solution resulted to be a Poka Yoke. The whole re-engineering process induced a productivity increase that is estimated from 46% to 80%. The study demonstrates how Lean Manufacturing tools together with deep learning technologies can be effective in the development of smart manufacturing lines.