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.

Safety violations in programmable logic controllers (PLCs), caused either by faults or attacks, have recently garnered significant attention. However, prior efforts at PLC code vetting suffer from many drawbacks. Static analyses and verification cause significant false positives and cannot reveal specific runtime contexts. Dynamic analyses and symbolic execution, on the other hand, fail due to their inability to handle real-world PLC programs that are event-driven and timing sensitive. In this paper, we propose VetPLC, a temporal context-aware, program analysis-based approach to produce timed event sequences that can be used for automatic safety vetting. To this end, we (a) perform static program analysis to create timed event causality graphs in order to understand causal relations among events in PLC code and (b) mine temporal invariants from data traces collected in Industrial Control System (ICS) testbeds to quantitatively gauge temporal dependencies that are constrained by machine operations. Our VetPLC prototype has been implemented in 15K lines of code. We evaluate it on 10 real-world scenarios from two different ICS settings. Our experiments show that VetPLC outperforms state-of-the-art techniques and can generate event sequences that can be used to automatically detect hidden safety violations.

The most notable trend in manufacturing over the past five years is probably the move towards networks at all levels. At lower levels in the factory infrastructure, networks provide higher reliability, visibility, and diagnosability, and enable capabilities such as distributed control, diagnostics, safety, and device interoperability. At higher levels, networks can leverage internet services to enable factory-wide automated scheduling, control, and diagnostics; improve data storage and visibility; and open the door to e-manufacturing. This paper explores current trends in the use of networks for distributed, multilevel control, diagnostics, and safety. Network performance characteristics such as delay, delay variability, and determinism are evaluated in the context of networked control applications. This paper also discusses future networking trends in each of these categories and describes the actual application of all three categories of networks on a reconfigurable factory testbed (RFT) at the University of Michigan. Control, diagnostics, and safety systems are all enabled in the RFT utilizing multitier networked technology including DeviceNet, PROFIBUS, OPC, wired and wireless Ethernet, and SafetyBUS p. This paper concludes with a discussion of trends in industrial networking, including the move to wireless for all categories, and the issues that must be addressed to realize these trends

PROFINET is the industrial Ethernet standard devised by PROFIBUS International for "Ethernet on the plant floor". PROFINET allows to implement a comprehensive communications solution on Ethernet which includes peer-to-peer communication between controllers, distributed I/O, machine safety, motion control and data acquisition. In this paper an analysis is conducted on the peer-to-peer interlocking performance based on PROFINET specification. Tests were performed to determine the performance of the peer-to-peer communication mechanism, to evaluate the impact of switches on the system, and to measure the impact of data size on peer-to-peer communication performance. The paper summarizes the test results.

The improvement in Ethernet based technologies has resulted in its use in real-time applications on the factory floor. Manufacturers, vendors and end users of automation devices are aiming at the economical and technical benefits of Ethernet based communication. Many Ethernet based protocols have been developed due to the promotion by several companies and organizations. There has been a considerable amount of research on whether these Ethernet based systems are able to fulfill the real-time requirements. To this end, this paper presents an evaluation of EtherNet/IP and its applicability for peer-to-peer communications on the factory floor.

The choice of implementing a safety system on a dedicated network or on a shared network (with a control system) rests solely on the system cost involved. To determine which option leads to a lower overall system cost, a two-tiered normalized weighted cost calculator approach that evaluates the trade-offs is presented. Applying the calculator to the decision process shows that either solution could be determined to be optimal, depending on the weights applied to specific cost factors as a result of the application environment.

When sensory and actuation devices in a control system are exchanging data through one common communication medium, the sharing of communication bandwidth will induce unavoidable data latency and might degrade the control performance. Hence, the utilization of communication resource and the requirement of control specification should be analyzed and properly designed when implementing a control system over a network architecture. In this paper, we analyze the performance of information sharing of multiple cooperative agents over one communication network, and propose design methodologies of guaranteeing acceptable control and communication performance in a networked control system. In particular, we study the relationship between the sampling rates of a control system,and the transmission rates of a communication network, and then utilize an integrated networked control design chart to help select design parameters and visualize overall system performance at different sampling and transmission rates. Based on the design parameters selected, the communication modules by utilizing deadband control and state estimation are presented for guaranteeing both control and communication performance. Simulation studies are conducted in a network-and-control simulation tool that is developed on the Matlab/Simulink platform and is used to demonstrate the proposed design methodologies. Both the analysis and simulation results illustrate the characteristics of designing mechanisms between control and communication performance and show the improvement of implementing the proposed communication modules.

A static control system architecture cannot meet the demand for continually introducing new parts into manufacturing systems while maintaining high quality and throughput for existing parts; a reconfigurable control infrastructure is needed. Control authority in the factory is typically allocated to three levels: the machine, cell, and system. The machine controllers must be reconfigurable to process different parts, the cell controllers must be reconfigurable to adapt to different part flows, and the system level controller must be reconfigurable to adapt to new parts in the system. In this paper, we describe the reconfigurable control infrastructure that we have developed at the University of Michigan's reconfigurable factory testbed. We then show through example how this infrastructure enables the real-time reconfiguration of the controllers at the machine and cell levels to adapt to quality problems and unexpected faults

Many real world systems and applications require information management components that provide support for managing imprecise data. There have thus been several proposals for extending relational database systems in order to represent as well as query such imprecise data. Little work, however, has been done in modeling uncertainty at the conceptual schema level and in developing design methodologies for developing fuzzy relational databases (FRDBs). To fill this gap, a design methodology for FRDBs is proposed. This methodology contains extensions for representing the imprecision of data in the entity-relationship (ER) data model, and a set of steps for the derivation of a FRDB from this extended ER model. As a case study, this methodology has been applied to the design of a control database for semiconductor manufacturing.>