Dr Damien Watkins1, Lachlan Hetherton1, Nerolie Oakes, David Thomas, Damien Watkins, Nirupama Sankaranarayanan
1CSIRO, Clayton, Australia
Industry 4.0 (a.k.a. “fourth industrial revolution”) refers to the amalgamation of automation & robotics, Internet of Things (IoT), network communications, cloud/cluster computing, artificial intelligence and human-computer interaction in manufacturing systems. Many of the benefits and challenges in achieving Industry 4.0 are common to the “digital transformation” of other domains and industries. Scientific Workflow Systems (SWSs) have been used both within and between many scientific domains to facilitate such digital transformations. Currently CSIRO is using its own SWS, Workspace, to prototype elements of an Industry 4.0 architecture and test the usefulness of using an SWS as the backbone of an Industry 4.0 platform. In this paper we introduce the concepts of Industry 4.0, SWSs and Workspace, and describe the suitability of the Workspace framework for creating applications and workflows compatible with Industry 4.0 requirements.
Industry 4.0 (CSIRO Manufacturing (2018), German Trade and Invest (2018), Wikipedia 2018) is driven by a number of factors including: increasing computational power and data volumes, advances in connectivity, advancing analytics and machine/business-intelligence, new forms of human-machine interaction and improvements in transferring digital instructions to the physical world (Department of Industry, Innovation and Science 2018). Industry 4.0 systems often create a “Digital Twin” of a real manufacturing environment to support monitoring and facilitate decision making – either by the systems themselves or by human operators. These manufacturing environments are not limited to a single physical site: indeed, they may span many traditional borders such as companies, countries, computer systems and technology domains. This means that they are inherently heterogeneous and complex. Platforms that simplify the development and/or deployment of such systems will add to their attractiveness to industry.
A SWS is a workflow system which allows the composition and execution of a sequence of computational steps in a scientific application. Many SWSs support distributed development by multiple scientists and as such they normally provide support for: storing the workflow description in a generic format (e.g. XML), execution on multiple operating systems (e.g. Linux, MAC, Windows), the use of multiple programming languages, interfaces for calling different execution environments (e.g. interactive, batch, PBS, SLURM, AWS, etc.), visualisation capabilities and so forth. The depth of any such support varies greatly between SWS (Deelman 2009, Gil 2007).
Workspace (Cleary et al. 2014, Cleary et al. 2015, Cleary et al. 2017, Workspace, 2014, Watkins 2017) is a SWS that supports the creation of scientific workflows and applications for commercial and research purposes. Under development at CSIRO since 2005, Workspace has been used in a number of different scientific domains. The Workspace framework provides a single, cross-platform environment to develop and execute scientific software tools and libraries that can be easily accessed by a wide range of users. Examples of Workspace-based workflows and applications in different scientific domains would include: ArcWeld (Murphy and Thomas, 2014, 2018), Amicus (Sullivan et al. 2013), Dive Mechanic (Cohen et al. 2018), HelioSim (Potter 2018) and Spark (Miller et al. 2015). Many of these applications support the development of a Digital Twin of a real world system augmented with CSIRO’s advanced modelling capabilities.
Workspace and Industry 4.0
As can be seen from the descriptions above, an SWS attempts to address many of the challenges of an Industry 4.0 system, providing support for interoperability, digital twining, visualization, cluster/cloud execution and so on. A key feature of Workspace and some other SWSs is the capability of users to extend the inbuilt functionality via an extensible plugin architecture. This flexibility allows individuals and teams to easily add their own data types, algorithms and GUI components into the framework to use and share with others. In the case of Workspace the plugin architecture has been used to expose a number of popular scientific libraries, (such as OpenCV (Open Source Computer Vision Library), PCL (Point Cloud Library) and VTK (Visualization Toolkit) and languages (such as Python, R and MATLAB). One key advantage of Workspace is that it facilitates the creation of standalone applications with custom GUIs that hide the underlying workflows. This makes complex scientific software easy to use in an industrial setting – end users on the factory floor often require a simple GUI application that only exposes access to information and controls necessary for their task at hand. ArcWeld, Dive Mechanic, and SPARK are examples of rich underlying workflows that have been packaged into user intuitive applications for use in situ by the end user.
Industry 4.0 demonstration facility
An Industry 4.0 demonstration lab is currently under construction at CSIRO Clayton. The system uses a number of devices including DSLR Cameras (Nikon), Time of Flight (ToF) Cameras (ODOS Swift), Projectors (Casio), Kinects (Microsoft) and other devices. The system makes use of a number of computational modelling capabilities developed by the Computational Modelling and Simulation Group of CSIRO. The system has a number of integrated libraries such as CSIRO’s Stereo Depth Fusion functionality. Numerous Workspace-based application/workflows are being developed for tasks such as: individual device control, communications, and visualisation. Mixed Reality output devices (i.e. Hololens, Meta 2) will soon be integrated into the system.
Figure 1: Logical view of the CSIRO Clayton Mixed Reality Laboratory
Industry 4.0 and SWSs offer similar benefits while addressing similar challenges. Evaluating the suitability of using an SWS such as Workspace in an Industry 4.0 environment using a purpose-built testbed should provide valuable insights into the applicability of the approach. Currently, although this is a work in progress, initial results have been positive and we expect to have more insights to share as the deployment of our testbed continues.
Cleary, P., Bolger, B., Hetherton, L., Rucinski, C., Thomas, D., Watkins, D. (2014), Workspace: A Platform for Delivering Scientific Applications”, Proc. eResearch 2014, Melbourne, Australia, 27-31 October.
Cleary, P.W., Thomas, D., Bolger, M., Hetherton, L., Rucinski, C., and Watkins, D., (2015), Using Workspace to automate workflow processes for modelling and simulation in engineering, MODSIM 2015, Gold Coast, Australia, December 2015.
Cleary, P. W., Watkins, D., Hetherton, L., Bolger, M. and Thomas, D., (2017), Opportunities for workflow tools to improve translation of research into impact, 22nd International Congress on Modelling and Simulation (MODSIM 2017), Hobart, Tasmania, Australia, 3-8th December 2017.
Cohen, R. C. Z., Harrison, S. M., and Cleary P. W., (2018), Dive Mechanic: Bringing 3D virtual experimentation to elite level diving using the Workspace workflow engine, submitted to special issue: Mathematics and Computers in Simulation.
CSIRO Manufacturing (2018), Advanced Manufacturing Roadmap https://www.csiro.au/en/Do-business/Futures/Reports/Advanced-manufacturing-roadmap
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Department of Industry, Innovation and Science (2018), Industry 4.0 URL https://industry.gov.au/industry/Industry-4-0/Pages/default.aspx.
German Trade and Invest (2018) INDUSTRIE 4.0 https://www.gtai.de/GTAI/Navigation/EN/Invest/Industries/Industrie-4-0/Industrie-4-0/industrie-4-0-what-is-it.html
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Miller, C., Hilton J., Sullivan A. and Prakash M., (2015), SPARK–A Bushfire Spread Prediction Tool, R. Denzer et al. (Eds.), Environmental Software Systems. Infrastructures, Services and Applications, 448, 262–271.
Murphy, T., Thomas, D., (2014), A user-friendly predictive model of arc welding of aluminium, Proc. 4th IIW Welding Research & Collaboration Colloquium, Wollongong, Australia, 5-6 November 2014, pp. 47.
Murphy. A. B., and Thomas, D. G., (2018), A computational model of arc welding – from a research tool to a software product, submitted to special issue: Mathematics and Computers in Simulation.
Potter, D. F., Khassapov, A., Pascual, R., Hetherton, L., and Zhang, Z., (2018), Heliosim: A Workspace-driven application for the optimisation of solar thermal power plants, submitted to special issue: Mathematics and Computers in Simulation.
Sullivan, A., Gould, J., Cruz, M., Rucinski, C., and Prakash, M., (2013), Amicus: A national fire behaviour knowledge base for enhanced information management and better decision making, 20th International Congress on Modelling and Simulation, Adelaide, Australia, 1–6 December 2013.
Watkins, D., Thomas, D., Hetherton, L., Bolger, M. and Cleary, P.W., (2017), Workspace – a Scientific Workflow System for enabling Research Impact, 22nd International Congress on Modelling and Simulation (MODSIM 2017), Hobart, Tasmania, Australia, 3-8th December 2017.
Wikipedia 2018, Industry 4.0: https://en.wikipedia.org/wiki/Industry_4.0.
Dr Damien Watkins is the Research Team Lead for the Computational Software Engineering and Visualisation team at Data61/CSIRO. His team is responsible for the development of Workspace, a scientific workflow platform used on projects across CSIRO and a number of Workspace-based applications. Workspace has been available for external usage since 2014.