Smart Industry Group
Smart Industry Group (SIG) is an interdisciplinary research group featuring expertise from computer science and mechanical engineering. SIG's focus is making production and products in industry smart using cyber-physical systems, that is, systems that intertwine software and physical components.
The advent of sensory technologies (e.g., Internet of Things; IoT) combined with the possibilities of the new communication networks like 5G is creating the necessary infrastructure for the Cyber-Physical Systems (CPS). In CPS, software and physical components are deeply intertwined. Such a convergence of the cyber and physical worlds offers boundless opportunities to all areas of industry – manufacturing, logistic, building, energy, health, etc – by becoming the cornerstone of future Industry 4.0.
The research group Smart Industry Group (SIG) is an interdisciplinary partnership between the Department of Computer Science and Media Technology and the Department of Mechanical Engineering, bringing together expertise in the cyber and physical world, respectively.
SIG is interested in leveraging data-driven techniques for making production and products smart, i.e., context-aware, self-aware, and adaptive. For example, a production system can automatically adjust to the desired production level with optimal logistics and flow at the lowest cost, whereas a product can automatically reconfigure with respect to operation and/or human factors.
The integration of CPS elements in production systems and products allows for offering new appealing functionality to the different stakeholders, i.e., operators and end-users, respectively. At the same time, the integration of CPS elements also increases the complexity of production systems and products, and seriously complicates the assessment and assurance of key quality aspects. Such aspects may be safety, security, performance, sustainability (i.e., near-zero power consumption), maintainability, and usability.
To this end, the Smart Industry Group aims at investigating:
- Data-driven discovery techniques for inferring production/products models from data.
- Data-driven analysis techniques for inferring production/products quality aspects from models and data.
Industrial applications
The Smart Industry Group also aims at serving as a catalyst for new collaborations and projects between the Department of Computer Science and Media Technology, the Department of Mechanical Engineering, and industry. We are especially interested in the sharing and transfer of methodologies, methods, and tools where the solutions from one field can be reapplied in another one.
SIG aims at applying the research to different areas, including, but not limited to:
- Production systems – automatically adjust the desired production with optimal logistics and flow at the lowest cost.
- Automatic reconfiguration of machines with respect to operation, loads, wear, service, material flow, logistics, human factors, etc. (E.g., changing stiffness with frequency to avoid resonance, changing the degree of anisotropy to adjust for asymmetric loads, and changing the thermal conductivity to adjust for varying temperature fields.)
- Adaption of maintenance management, condition monitoring, and condition-based maintenance.
- Physical machines, etc., ready for smart CPS.
Projects
Ongoing projects
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Doctoral project: Application of AI in the software development process This project aims to streamline the software system development process by leveraging AI.
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Doctoral project: Digital Twin as a Service (DTaaS) This doctoral project targets to work on Digital Twin, with integration to data intensive sources.
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Doctoral project: Realising smarter organization by developing Digital Twin of the Organization A Digital Twin of an Organization (DTO) as a software component proposes a live model of an…
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Project expert competence: Smart Industry, phase 2 The goal of the project is to develop courses at advanced level linked to Smart Industry based on the skills needs of industry. The project's target…
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Project: Aligning Architectures for Digital Twin of the Organization (Aladino) This project aims at establishing a set of sound engineering methodologies, methods and tools for modeling, evaluating,…
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Project: In-line visual inspection using unsupervised learning The purpose of this project is to introduce and improve machine learning- assessment of the quality of massproduced industrial (steel)…
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Project: IoT lab for SME 2.0 The aim of this project is to develop a well-established network of companies in the Linnaeus region that can benefit from each other's expertise and products within…
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Project: Smart Dat The goal of the project is to offer activities in automation and digitalization for small and medium-sized companies in the manufacturing sector in the Linnaeus region, strengthen…
Concluded projects
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Seed project: IoT for ships – an untapped data resource The main objective for this seed project within Linnaeus University Centre for Data Intensive Sciences and Applications (DISA) is to establish…
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Seed project:Automated Assembly/Disassembly Instructions (ADDITION) The project aims at building a consortium interested in laying the foundation for harnessing the power of AI and digitalization for…
Publications
Staff
- Andreas Linderholt Associate professor
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- Ares Argelia Gomez Gallegos Associate senior lecturer
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- Daniel Nilsson
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- Danny Weyns Professor
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- Diego Perez Palacin Senior lecturer
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- Fredrik Ahlgren Senior lecturer
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- Gaurav Garg
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- Gunnar Bolmsjö Professor
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- Hatem Algabroun Senior lecturer
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- Janka Kovacikova Senior lecturer
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- Jetro Kenneth Pocorni Senior lecturer
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- Jorge Luis Zapico Senior lecturer
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- Keegan Mbiyana Doctoral student
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- Lars Håkansson professor, head of department
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- Martin Kroon Professor
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- Mauro Caporuscio Professor
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- Mehdi Saman Azari Research assistant
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- Mirka Kans Associate Professor
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- Nadeem Abbas Senior lecturer
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- Nils Johansson
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- Per Lindström Lussi Senior lecturer
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- Per Ranstad Adjunct professor
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- Rammohan Kodakadath Premachandran Doctoral student
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- Samuele Giussani Doctoral student
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- Tobias Schauerte Senior lecturer
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