Wireless communication is becoming crucial to advanced manufacturing. Industry 4.0 and Smart Manufacturing depend on networked industrial automation systems. The term Industrial Internet of Things (IIoT) has been used to describe the deployment of interconnected machines, sensors, and actuators within modernized factories. The adoption of wireless systems is essential to these IIoT deployments. Wireless automation significantly reduces capital investment costs including conduit, cables, networking equipment, and labor of installation. To enable the adoption of wireless systems at the factory-floor level, wireless requirements must be established to realize the benefits of wireless communication systems within those factories. One challenge is that existing wireless standards lack technical specifications that support low-latency and high-reliability communication for factory applications. Additionally, requirements for such capabilities are published or advertised without validation of said requirements. Often, requirements published by standards development organizations appear excessively strict and unvalidated by empirical study. Moreover, those requirements ignore the capabilities of the applications to use their own intelligence to compensate for lost reliability in the network. This report analyzes existing wireless user requirements stated by industry organizations and it produces a combined perspective on wireless user requirements for the factory workcell with supporting rationale.
The slowness of widespread adoption of wireless technologies in cyber-physical systems (CPS) is partly due to not fully understanding the detailed impact of wireless deployment on the physical processes especially in the cases that require low latency and high reliability communications. In this paper, we introduce an approach to integrate wireless network traffic data and physical processes data to evaluate the impact of wireless communications on the performance of manufacturing factory work-cell. The proposed approach is introduced through the discussion of an engineering use case. A testbed that emulates a robotic manufacturing factory work-cell is constructed using two collaborative-grade robot arms, machine emulators, and wireless communication devices. All network traffic data is collected and physical process data, including the robots and machines states and various supervisory control commands, is also collected and synchronized to the network data. The data is then integrated where redundant data is removed and correlated activities are connected in a graph database. A data model is proposed, developed, and elaborated; the database is then populated with events from the testbed, and the resulting graph is presented. Moreover, we detail the way by which this approach is used to study the impact of wireless communications on the physical processes and illustrate the impact of various wireless network parameters on the performance of the emulated manufacturing work-cell. This approach can be deployed as a building block for various descriptive and predictive wireless analysis tools for CPS.
Current product composition and quality test methods for the paper and pulp industry are mainly based on manual ex-situ wet-bench chemistry techniques. For example, the standard method for determining the furnish of paper, TAPPI T 401 “Fiber analysis of paper and paperboard,” relies on the experience and visual acuity of a specially trained analyst to determine the individual plant species present and to quantify the amount of each constituent fiber type in a sheet of paper. Thus, there is a need for a fast, nondestructive analytical technique that leverages intrinsic attributes of the analytes. In this paper, we demonstrate an application of dielectric spectroscopy (DS) as a potential metrology to differentiate between nonwood pulp and wood pulp fibers. This in-situ, noncontact and nondestructive assessment method has inherent forensic capabilities and is also amiable to quality assurance techniques such as gauge capability studies and real-time statistical process control (SPC). Application: The dielectric spectroscopy results presented in this paper can nondestructively determine the amount of lignin in paper products and are in principle comparable to the performance specifications of the TAPPI Standard Test Method T 401 and should enable the sources of printing substrates to be both authenticated and validated in real time in a paper testing laboratory environment.
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