Niclas Björsell

Currently, Niclas Björsell is involved in the following research projects:

AI- PRUN

More information about AI-PRUN

Prescriptive Quality in Cold Rolling processes

With data from production systems, complementary in-line quality measurements and real-time tools, provide decision support for intermediate products manufacturing to promote product quality and efficiency throughout the value chain.

Within the framework of the m3c graduate school, we are conducting a project with an industrial doctoral student from Alleima. Research – m3c industrial research school External link.

AI-CoMoVA

More information about the project

Predictive Maintenance from a System Perspective

Effective maintenance is critical to most operations; maintenance activities extend equipment lifetime, improve reliability, and prevent deterioration. Thereby it is defined to be one of inner most circles in a circular economy. Industrial maintenance is basically reactive, corrective and preventive, not taking into consideration the huge amount of data being generated on the shop floor, nor the available new digital technologies that have emerged in recent years. To be proactive and predictive, maintenance strategies can take advantage of emerging digitalized technologies such as advanced data analysis, machine learning, big data and cloud computing to collect, store and analyze the available data.

The project focuses on digitalized maintenance in the process industry in general and the steel industry in particular. These industries differ, to some extent, from the manufacturing industry, since it is common to have production units that gradually and continuously process raw materials through various interconnected sub-systems into the final products. Optimizing maintenance for an individual sub-system does not necessarily mean the optimum for a continuous process. In process industry a stop in a sub-system often results in a stop of the entire process chain. The
project adds a new way of thinking with a systems perspective that is extra important in process industries. Although analyzes of data are important, they will not be useful unless analyzes can be fed back to operators and decision-makers to make the right decision about action. Visualizations in 2D and 3D have been suggested previously as powerful tools in intelligent maintenance. Albeit, the system of system perspective means an increased complexity not least for those who have to make decisions based on the data generated. Due to the complexity of the data collected from the process and due to the intertwined nature of the involved sub-systems it is crucial that the data is visualized in an efficient way that enables comprehension by for operators and maintenance personnel enabling them to diagnose the status of the system and to make well-informed decisions.

This project involves three leading steel companies OVAKO, Sandvik SMT and SSAB together with ABB and the University of Gävle (HiG). By jointly developing and evaluating methods for predictive maintenance and visual decision support as well as implementing it on industrial control and monitoring systems, conditions are created for predictive maintenance of cooperating machines.

Innovative Digital Maintenace

The digitization of industry creates new opportunities for SMEs in several different areas. This project intends to focus on the possibilities for more efficient maintenance in existing production, new services for maintenance and maintenance planning, added value for products and to create an attractive workplace for a widened staff group. The overarching goal is that above all small and medium-sized companies within the region should increase competitiveness and employment. In the long term, it leads to positive effects on human capital, innovativeness, economic profitability, circular economy and regional attractiveness. The project begins with an inventory in order to identify where the company is in the digitization process today and where it wants to be in the future. Four work packages are then carried out: (i) An activity based on participant-driven competence development, (ii) using machine learning to produce digital twins which can be used for predictive maintenance. (iii) Predictive maintenance, where we estimate the remaining service life and thus the need for maintenance. (iv) The final work package consists of communicating results to an operator and/or maintenance planner. Within the project, a number of case studies will be carried out at participating SME companies.

Industrial Automation and Millimeter Waves

The project comprises four work packages, of which Dr. Björsell is responsible for (i) Radio communication in industrial environments and works actively in (ii) Process automation and wireless communication. The others are (iii) Measurement technology for components and (iv) Industrial measuring technology. In the field of Radio Communication in Industrial Environments, measurements in the 24 GHz band are carried out in industrial environments in order to develop channel models, measure the impact of disturbances and study doppler effects from moving objects. Process Automation and Wireless Communication aim to combine wireless communication and control technology. In addition, modeling techniques are used to create digital twins that can be used, for example, to detect a maintenance need.

Instrument for High-Speed Electron Tunneling Measurements

Realizing the tremendous potential of personalized medicine requires knowing the genome of individual patients. However, current DNA sequencing technologies are still too slow and expensive for sequencing genomes for healthcare practice on a large scale. There is, therefore a critical need for a new sequencing technology that has the potential to sequence the full human genome within 1 hour for less than US$100. DNA sequencing using electron tunneling detection is one of the post promising methods to achieve this goal. In this approach, molecular strands are decoded by moving them through nanogaps formed between pairs of nanoelectrodes embedded in nanopores while sending a tunneling current between the electrodes. However, to research this technology an instrument is needed that can read out the extremely small tunneling currents at very high speeds (10-100 MHz), which does not exist today. To address this need, we propose to develop an instrument for electron tunneling measurements, combining custom high-speed read-out electronics, signal processing, and electron tunneling sensors for biomolecule detection. This instrument development project will pave the way for exciting new research on next-generation high-throughput and low-cost sequencing of DNA, RNA and peptides, which has the potential to achieve significant advancements within biology, life-science and clinical practice, thereby having significant scientific, societal and commercial impacts.

Flexible Models for Smart Maintenance

Smart maintenance means that machines themselves identify and possibly carry out a maintenance need. The technology is available in new modern machines, but the need is great in existing plants. The project aims to develop models for smart maintenance of existing older equipment.

AXO-SUIT

This EU project (AAL Call 6: AXO-SUIT project) purpose is to develop an Exo skeleton to assist older persons with reduced mobility. The control of the Exo skeleton should enhance the movement the person intends to perform. Our part of the project has been to develop mathematical models that describe the movements of the shoulder and to detect which movement the user intends to perform.

Niclas Björsell supervises five doctoral students and teaches courses related to the research.