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- 2017
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Ingår i: Physical Review D. - 2470-0010 .- 2470-0029. ; 96:2
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Shankar, K. V., et al.
(författare)
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Solutionising Temperature Influence on the Morphological and Mechanical Characteristics of Al–Si–Mg–Ni Hypoeutectic Alloys
- 2021
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Ingår i: Journal of The Institution of Engineers (India): Series D. - : Springer Nature. - 2250-2122 .- 2250-2130. ; 102:1, s. 131-148
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Tidskriftsartikel (refereegranskat)abstract
- The current work is focused to ascertain the impact on the mechanical and morphological characteristics of hypoeutectic alloy Al–Ni with a range of solution treatment temperatures. Al, Ni, Si, and Mg of necessary weight percentages were melted in a crucible (make–clay graphite) and were cast. The cast alloys were then solutionised for 8 h from 450 to 550 °C, quenched and was aged for 12 h at 170 °C. The fractography, intermediate phase and the elementary composition of alloy was determined. It was observed from the investigation that a rise in solutionising temperature caused grain refinement in the developed hypoeutectic alloys. A surge in the value of hardness was observed with respect to the rise in solutionising temperature. It was also noticed from the analysis that the value of tensile strength, yield strength, ductility and impact resistance of the hypoeutectic alloys enhanced with temperature rise from 480 to 510 °C and then declined from 510 to 550 °C.
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| 3. |
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| 4. |
- Gopalakrishnan, Maheshwaran, 1987, et al.
(författare)
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Data Analytics in Maintenance Planning – DAIMP
- 2019
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Manufacturing industry plays a vital role in the society, which is evident in current discussions on industrialization agendas. Digitalization, the Industrial Internet of Things and their connections to sustainable production are identified as key enablers for increasing the number of jobs in Swedish industry. To implement digitalized manufacturing achieving high maintenance performance becomes utmost necessity. A substantial increase in systems availability is crucial to enable the expected levels of automation and autonomy in future production. Maintenance organizations needs to go from experiences based decision making in maintenance planning to using fact based decision making using Big Data analysis and data-driven decision support. Currently, there is lack of maintenance-oriented research based on empirical data, which hinders the increased use of engineering methods within the area. The DAIMP project addresses the problem with insufficient availability and robustness in Swedish production systems. The main challenges include limited productivity, challenges in capability of introducing new products, and challenges in implement digital production. The DAIMP project connects data collection from a detailed machine level to system level analysis. DAIMP project aimed at reaching a system level analytics to detect critical equipment, differentiate maintenance planning and prioritize the most important equipment in real-time. Furthermore, maintenance organizations will also be supported in moving from descriptive statistics of historical data to predictive and prescriptive analytics. The main goals of the project are: Agreed data parameters and alarm structures for analyses and performance measures Increased back-office maintenance planning using predictive and prescriptive analysis Increased use of dynamic and data-driven criticality analysis Increased prioritization of maintenance activities The goals were further divided into specific goals and six work packages were designed to execute the project. WP1 focused on the purchase phase and getting data structures and collaboration with equipment vendors correct from start. WP2 focused on the ramp-up phase of new products and production lines when predictive and prescriptive analytics are important to handle unknown disturbances. WP3 focused on the operational phase and to provide data-driven decision support for directing maintenance efforts to the critical equipment from a systems perspective. WP4 focused on designing maintenance packages for different equipment with inputs from WP3, including both reactive, preventive, and improving activities. WP5 focused on the evaluation and demonstration for different project results WP6 focused on coordination project management In WP1, models were developed to understand the missing element for the capability assessment from initiation of the machine tool procurement to the end of lifecycle. The information exchange and process of machine tool procurement from the end-users perspective was assessed. Additionally, the alarm structure is created using the capability framework and the ability model. In WP2, diagnostic, predictive and prescriptive algorithms were developed and validated. The algorithms were developed using manufacturing execution system (MES) data to provide system level decision making using data analytics. Improved quality of decisions by data-driven algorithms. Moved from experienced based decision to algorithmic based decisions. Identified the required amount data sets for developing machine learning algorithm. In WP3, data-driven machine criticality assessment framework was developed and validated. MES and computerised maintenance management system (CMMS) data were used to assess criticality of machines. It serves as data-driven decision support for maintenance planning and prioritization. It provided guidelines to achieve systems perspective in maintenance organization. In WP4, a component classification was developed. It provides guidelines for designing preventive maintenance programs based on the machine criticality. It uses CMMS data for component classification. In WP6, three demonstrator cases were performed at (i) Volvo Cars focusing on system level decision support at ramp up phase, (ii) Volvo GTO focusing on global standardization and (iii) a test-bed demo of data-driven criticality assessment at Chalmers. Lastly, as part of WP6, an international evaluation was conducted by inviting two visiting professors. The outcomes of the DAIMP project showed a strong contribution to research and manufacturing industry alike. Particularly, the project created a strong impact and awareness regarding the value maintenance possess in the manufacturing companies. It showed that maintenance will have a key role in enabling industrial digitalization. The project put the maintenance research back on the national agenda. For example, the project produced world-leading level in MES data analytics research; it showed how maintenance can contribute to productivity increase, thereby changing the mind-set from narrow-focused to having an enlarged-focus; showed how to work with component level problems to working with vendors and end-users.
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| 5. |
- Meijer, Mandy, et al.
(författare)
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Epigenomic priming of immune genes implicates oligodendroglia in multiple sclerosis susceptibility
- 2022
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Ingår i: Neuron. - : Elsevier. - 0896-6273 .- 1097-4199. ; 110:7, s. 1193-1210
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Tidskriftsartikel (refereegranskat)abstract
- Multiple sclerosis (MS) is characterized by a targeted attack on oligodendroglia (OLG) and myelin by immune cells, which are thought to be the main drivers of MS susceptibility. We found that immune genes exhibit a primed chromatin state in single mouse and human OLG in a non-disease context, compatible with transitions to immune-competent states in MS. We identified BACH1 and STAT1 as transcription factors involved in immune gene regulation in oligodendrocyte precursor cells (OPCs). A subset of immune genes presents bivalency of H3K4me3/H3K27me3 in OPCs, with Polycomb inhibition leading to their increased activation upon interferon gamma (IFN-g) treatment. Some MS susceptibility single-nucleotide polymorphisms (SNPs) overlap with these regulatory regions in mouse and human OLG. Treatment of mouse OPCs with IFN-g leads to chromatin architecture remodeling at these loci and altered expression of interacting genes. Thus, the susceptibility for MS may involve OLG, which therefore constitutes novel targets for immunological based therapies for MS.
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| 6. |
- Subramaniyan, Mukund, 1989, et al.
(författare)
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An algorithm for data-driven shifting bottleneck detection
- 2016
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Ingår i: Cogent Engineering. - : Informa UK Limited. - 2331-1916. ; 3:1, s. 1-19
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Tidskriftsartikel (refereegranskat)abstract
- Manufacturing companies continuously capture shop floor information using sensors technologies, Manufacturing Execution Systems (MES), Enterprise Resource Planning systems. The volumes of data collected by these technologies are growing and the pace of that growth is accelerating. Manufacturing data is constantly changing but immediately relevant. Collecting and analysing them on a real-time basis can lead to increased productivity. Particularly, prioritising improvement activities such as cycle time improvement, setup time reduction and maintenance activities on bottleneck machines is an important part of the operations management process on the shop floor to improve productivity. The first step in that process is the identification of bottlenecks. This paper introduces a purely data-driven shifting bottleneck detection algorithm to identify the bottlenecks from the real-time data of the machines as captured by MES. The developed algorithm detects the current bottleneck at any given time, the average and the non-bottlenecks over a time interval. The algorithm has been tested over real-world MES data sets of two manufacturing companies, identifying the potentials and the prerequisites of the data-driven method. The main prerequisite of the proposed data-driven method is that all the states of the machine should be monitored by MES during the production run.
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| 7. |
- Subramaniyan, Mukund, 1989, et al.
(författare)
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Real-Time data-driven average active period method for bottleneck detection
- 2016
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Ingår i: International Journal of Design and Nature and Ecodynamics. - 1755-7445 .- 1755-7437. ; 11:3, s. 428-437
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Tidskriftsartikel (refereegranskat)abstract
- Prioritising improvement and maintenance activities is an important part of the production management and development process. Companies need to direct their efforts to the production constraints (bottlenecks) to achieve higher productivity. The first step is to identify the bottlenecks in the production system. A majority of the current bottleneck detection techniques can be classified into two categories, based on the methods used to develop the techniques: Analytical and simulation based. Analytical methods are difficult to use in more complex multi-stepped production systems, and simulation-based approaches are time-consuming and less flexible with regard to changes in the production system. This research paper introduces a real-Time, data-driven algorithm, which examines the average active period of the machines (the time when the machine is not waiting) to identify the bottlenecks based on real-Time shop floor data captured by Manufacturing Execution Systems (MES). The method utilises machine state information and the corresponding time stamps of those states as recorded by MES. The algorithm has been tested on a real-Time MES data set from a manufacturing company. The advantage of this algorithm is that it works for all kinds of production systems, including flow-oriented layouts and parallel-systems, and does not require a simulation model of the production system.
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