Among the tenets of Smart Manufacturing (SM) or Industry 4.0 (I4.0), digital twin (DT), which represents the capabilities of virtual representations of components and systems, has been cited as the biggest technology trend disrupting engineering and design today. DTs have been in use for years in areas such as model-based process control and predictive maintenance, however moving forward a framework is needed that will support the expected pervasiveness of DT technology in the evolution of SM or I4.0. A set of requirements for a DT framework has been derived from analysis of DT definitions, DTs in use today, expected DT applications in the near future, and longer-term DT trends and the DT vision in SM. These requirements include elements of re-usability, interoperability, interchangeability, maintainability, extensibility, and autonomy across the entire DT lifecycle. A baseline framework for DT technology has been developed that addresses many aspects of these requirements and enables the addressing of the requirements more fully through additional specification. The baseline framework includes a definition of a DT and an object-oriented (O-O) architecture for DTs that defines generalization, aggregation and instantiation of DT classes. Case studies using and extending the baseline framework illustrate its advantages in supporting DT solutions and trends in SM.

Smart manufacturing (SM) is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries, thus there is an opportunity to look to other industries to determine solution and roadmap paths for industries such as biochemistry or biology. The big data evolution affords an opportunity for managing significantly larger amounts of information and acting on it with analytics for improved diagnostics and prognostics. The analytics approaches can be defined in terms of dimensions to understand their requirements and capabilities, and to determine technology gaps. The semiconductor manufacturing industry has been taking advantage of the big data and analytics evolution by improving existing capabilities such as fault detection, and supporting new capabilities such as predictive maintenance. For most of these capabilities: (1) data quality is the most important big data factor in delivering high quality solutions; and (2) incorporating subject matter expertise in analytics is often required for realizing effective on-line manufacturing solutions. In the future, an improved big data environment incorporating smart manufacturing concepts such as digital twin will further enable analytics; however, it is anticipated that the need for incorporating subject matter expertise in solution design will remain.

Smart manufacturing (SM) systems utilize run-time data to improve productivity via intelligent decision-making and analysis mechanisms on both machine and system levels. The increased adoption of cyber-physical systems in SM leads to the comprehensive framework of cyber-physical manufacturing systems (CPMS) where data-enabled decision-making mechanisms are coupled with cyber-physical resources on the plant floor. Due to their cyber-physical nature, CPMS are susceptible to cyber-attacks that may cause harm to the manufacturing system, products, or even the human workers involved in this context. Therefore, detecting cyber-attacks efficiently and timely is a crucial step toward implementing and securing high-performance CPMS in practice. This paper addresses two key challenges to CPMS cyber-attack detection. The first challenge is distinguishing expected anomalies in the system from cyber-attacks. The second challenge is the identification of cyber-attacks during the transient response of CPMS due to closed-loop controllers. Digital twin (DT) technology emerges as a promising solution for providing additional insights into the physical process (twin) by leveraging run-time data, models, and analytics. In this work, we propose a DT framework for detecting cyber-attacks in CPMS during controlled transient behavior as well as expected anomalies of the physical process. We present a DT framework and provide details on structuring the architecture to support cyber-attack detection. Additionally, we present an experimental case study on off-the-shelf 3D printers to detect cyber-attacks utilizing the proposed DT framework to illustrate the effectiveness of our proposed approach. Note to Practitioners —This work is motivated by developing a general-purpose and extensible digital twin-enabled cyber-attack detection framework for manufacturing systems. Existing works in the field consider specialized attack scenarios and models that may not be extensible in practical manufacturing scenarios. We utilize digital twin (DT) technology as a key enabler to develop a systematic and extensible framework where we identify the abnormality of a resource and detect if the abnormality is due to an attack or an expected anomaly. We provide several remarks on how our proposed framework can extend existing industrial control systems (ICS) and can accommodate further extensions. The presented DTs utilize data-driven machine learning models, physics-based models, and subject matter expert knowledge to perform detection and differentiation tasks in the context of expected anomalies and model-based controllers that control the manufacturing process between multiple setpoints. We utilize a model predictive controller on an off-the-shelf 3D printer to run the process, and stage anomalies and cyber-attacks that are successfully detected by the proposed framework.

In this paper we discuss the modelling and control of networked control systems (NCS) where sensors, actuators and controllers are distributed and interconnected by a common communication network. Multiple distributed communication delays as well as multiple inputs and multiple outputs (MIMO) are considered in the modelling algorithm. In addition, the asynchronous sampling mechanisms of distributed sensors are characterized to obtain the actual time delays between sensors and the controller. Due to the characteristics of a network architecture, piecewise constant plant inputs are assumed and discrete-time models of plant and controller dynamics are adopted to analyse the stability and performance of a closed-loop NCS. The analysis result is used to verify the stability and performance of an NCS without considering the impact of multiple time delays in the controller design. In addition, the proposed NCS model is used as a foundation for optimal controller design. The proposed control algorithm utilizes the information of delayed signals and improves the control performance of a control system encountering distributed communication delays. Several simulation studies are provided to verify the control performance of the proposed controller design.

Digital Twin (DT) is an emerging technology that has recently been cited as an underpinning element of the digital transformation. DTs are commonly defined as digital replicas of components, systems, products, and services that receive data from the field to support intelligent decision-making. Although several frameworks for DT application in manufacturing have been proposed, there is no systematic methodology in the literature that supports the development of scalable, reusable, interoperable, interchangeable, and extensible DT solutions, while taking into account specific manufacturing environment needs and conditions. This paper introduces a DT solution development methodology as a generic procedure for analyzing and developing DTs for manufacturing systems. The methodology is based on the well-known System Development Life Cycle (SDLC) process and takes into consideration: (1) the specificity of DT characteristics and requirements, (2) an understanding of the manufacturing context in which the DTs will operate, and (3) the object-oriented aspects required to achieve DT capabilities of scalability, reusability, interoperability, interchangeability, and extensibility. A case study illustrates the advantages of the proposed methodology in supporting manufacturing DT solutions.

As requirements on data volumes, rates, quality, merging, and analytics increase exponentially in the digital universe, semiconductor manufacturers are faced with a need for new approaches to data management and use across the Fab. These are often termed “big data” challenges. In our industry big data solutions will be key to scaling advanced process control (APC) solutions to finer levels of control and diagnostics. However the main impact will be to better enable more effective predictive technologies such as predictive maintenance (PdM), virtual metrology and yield prediction, all of which utilize data from traditional APC capabilities that include fault detection and classification and run-to-run control. PdM represents one area where big data solutions are generating significant benefits across a variety of process types. Moving to big data solutions involves addressing the aforementioned requirements either with enhancements of existing systems or moving to more big data friendly platforms. Big data friendly platforms applied to APC systems provide quantifiable cost-of-ownership and speed improvements, thereby better enabling high quality prediction solutions. Initially, big data solutions will largely be delegated to off-line and on-time critical tasks; over the longer term these big data solutions will increasingly be leveraged for time critical and real-time capabilities.