| 1. |
- Kroon, Martin, et al.
(författare)
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Anisotropic Elastic-Viscoplastic Properties at Finite Strains of Injection-Moulded Low-Density Polyethylene
- 2018
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Ingår i: Experimental mechanics. - : Springer New York LLC. - 0014-4851 .- 1741-2765. ; 58:1, s. 75-86
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Tidskriftsartikel (refereegranskat)abstract
- Injection-moulding is one of the most common manufacturing processes used for polymers. In many applications, the mechanical properties of the product is of great importance. Injection-moulding of thin-walled polymer products tends to leave the polymer structure in a state where the mechanical properties are anisotropic, due to alignment of polymer chains along the melt flow direction. The anisotropic elastic-viscoplastic properties of low-density polyethylene, that has undergone an injection-moulding process, are therefore examined in the present work. Test specimens were punched out from injection-moulded plates and tested in uniaxial tension. Three in-plane material directions were investigated. Because of the small thickness of the plates, only the in-plane properties could be determined. Tensile tests with both monotonic and cyclic loading were performed, and the local strains on the surface of the test specimens were measured using image analysis. True stress vs. true strain diagrams were constructed, and the material response was evaluated using an elastic-viscoplasticity law. The components of the anisotropic compliance matrix were determined together with the direction-specific plastic hardening parameters. © 2017 The Author(s)
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| 2. |
- Olsson, Pär A T, 1981-, et al.
(författare)
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All-atomic and coarse-grained molecular dynamics investigation of deformation in semi-crystalline lamellar polyethylene
- 2018
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Ingår i: Polymer. - : Elsevier Ltd. - 0032-3861 .- 1873-2291. ; 153, s. 305-316
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Tidskriftsartikel (refereegranskat)abstract
- In the present work we have performed classical molecular dynamics modelling to investigate the effects of different types of force-fields on the stress-strain and yielding behaviours in semi-crystalline lamellar stacked linear polyethylene. To this end, specifically the all-atomic optimized potential for liquid simulations (OPLS-AA) and the coarse-grained united-atom (UA) force-fields are used to simulate the yielding and tensile behaviour for the lamellar separation mode. Despite that the considered samples and their topologies are identical for both approaches, the results show that they predict widely different stress-strain and yielding behaviours. For all UA simulations we obtain oscillating stress-strain curves accompanied by repetitive chain transport to the amorphous region, along with substantial chain slip and crystal reorientation. For the OPLS-AA modelling primarily cavitation formation is observed, with small amounts of chain slip to reorient the crystal such that the chains align in the tensile direction. This force-field dependence is rooted in the lack of explicit H-H and C-H repulsion in the UA approach, which gives rise to underestimated ideal critical resolved shear stress. The computed critical resolved shear stress for the OPLS-AA approach is in good agreement with density functional theory calculations and the yielding mechanisms resemble those of the lamellar separation mode. The disparate energy and shear stress barriers for chain slip of the different models can be interpreted as differently predicted intrinsic activation rates for the mechanism, which ultimately are responsible for the observed diverse responses of the two modelling approaches. © 2018 Elsevier Ltd
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| 3. |
- Munappy, Aiswarya Raj, 1990, et al.
(författare)
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From Ad-Hoc Data Analytics to DataOps
- 2020
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Ingår i: Proceedings - 2020 IEEE/ACM International Conference on Software and System Processes, ICSSP 2020. - New York, NY, USA : ACM. ; , s. 165-174
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Konferensbidrag (refereegranskat)abstract
- The collection of high-quality data provides a key competitive advantage to companies in their decision-making process. It helps to understand customer behavior and enables the usage and deployment of new technologies based on machine learning. However, the process from collecting the data, to clean and process it to be used by data scientists and applications is often manual, non-optimized and error-prone. This increases the time that the data takes to deliver value for the business. To reduce this time companies are looking into automation and validation of the data processes. Data processes are the operational side of data analytic workflow.DataOps, a recently coined term by data scientists, data analysts and data engineers refer to a general process aimed to shorten the end-to-end data analytic life-cycle time by introducing automation in the data collection, validation, and verification process. Despite its increasing popularity among practitioners, research on this topic has been limited and does not provide a clear definition for the term or how a data analytic process evolves from ad-hoc data collection to fully automated data analytics as envisioned by DataOps.This research provides three main contributions. First, utilizing multi-vocal literature we provide a definition and a scope for the general process referred to as DataOps. Second, based on a case study with a large mobile telecommunication organization, we analyze how multiple data analytic teams evolve their infrastructure and processes towards DataOps. Also, we provide a stairway showing the different stages of the evolution process. With this evolution model, companies can identify the stage which they belong to and also, can try to move to the next stage by overcoming the challenges they encounter in the current stage.
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| 4. |
- Green, Rolf, 1991, et al.
(författare)
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Autonomously Improving Systems in Industry : A Systematic Literature Review
- 2021
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Ingår i: Software Business. - Cham : Springer. - 9783030672911 - 9783030672928 ; 407, s. 30-45
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Konferensbidrag (refereegranskat)abstract
- A significant amount of research effort is put into studying machine learning (ML) and deep learning (DL) technologies. Real-world ML applications help companies to improve products and automate tasks such as classification, image recognition and automation. However, a traditional “fixed” approach where the system is frozen before deployment leads to a sub-optimal system performance. Systems autonomously experimenting with and improving their own behavior and performance could improve business outcomes but we need to know how this could actually work in practice. While there is some research on autonomously improving systems, the focus on the concepts and theoretical algorithms. However, less research is focused on empirical industry validation of the proposed theory. Empirical validations are usually done through simulations or by using synthetic or manually alteration of datasets. The contribution of this paper is twofold. First, we conduct a systematic literature review in which we focus on papers describing industrial deployments of autonomously improving systems and their real-world applications. Secondly, we identify open research questions and derive a model that classifies the level of autonomy based on our findings in the literature review.
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| 5. |
- Fisk, Martin, 1981-, et al.
(författare)
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Coupled electromagnetic-thermal solution strategy for induction heating of ferromagnetic materials
- 2022
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Ingår i: Applied Mathematical Modelling. - : Elsevier. - 0307-904X .- 1872-8480. ; 111, s. 818-835
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Tidskriftsartikel (refereegranskat)abstract
- Induction heating is used in many industrial applications to heat electrically conductive materials. The coupled electromagnetic-thermal induction heating process is non-linear in general, and for ferromagnetic materials it becomes challenging since both the electromagnetic and the thermal responses are non-linear. As a result of the existing non-linearities, simulating the induction heating process is a challenging task. In the present work, a coupled transient electromagnetic-thermal finite element solution strategy that is appropriate for modeling induction heating of ferromagnetic materials is presented. The solution strategy is based on the isothermal staggered split approach, where the electromagnetic problem is solved for fixed temperature fields and the thermal problem for fixed heat sources obtained from the electromagnetic solution. The modeling strategy and the implementation are validated against induction heating experiments at three heating rates. The computed temperatures, that reach above the Curie temperature, agree very well with the experimental results.
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| 6. |
- Dzhusupova, Rimman, et al.
(författare)
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The Goldilocks Framework: Towards Selecting the Optimal Approach to Conducting AI Projects
- 2022
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Ingår i: Proceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022. - New York, NY, USA : ACM. ; , s. 124-135
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Konferensbidrag (refereegranskat)abstract
- Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies' industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully.
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| 7. |
- Dzhusupova, Rimman, et al.
(författare)
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Using artificial intelligence to find design errors in the engineering drawings
- 2023
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Ingår i: Journal of Software: Evolution and Process. - : Wiley. - 2047-7481 .- 2047-7473. ; 35:12
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Tidskriftsartikel (refereegranskat)abstract
- Artificial intelligence is increasingly becoming important to businesses because many companies have realized the benefits of applying machine learning (ML) and deep learning (DL) in their operations. ML and DL have become attractive technologies for organizations looking to automate repetitive tasks to reduce manual work and free up resources for innovation. Unlike rule-based automation, typically used for standardized and predictable processes, machine learning, especially deep learning, can handle more complex tasks and learn over time, leading to greater accuracy and efficiency improvements. One of such promising applications is to use AI to reduce manual engineering work. This paper discusses a particular case within McDermott where the research team developed a DL model to do a quality check of complex blueprints. We describe the development and the final product of this case—AI-based software for the engineering, procurement, and construction (EPC) industry that helps to find the design mistakes buried inside very complex engineering drawings called piping and instrumentation diagrams (P&IDs). We also present a cost-benefit analysis and potential scale-up of the developed software. Our goal is to share the successful experience of AI-based product development that can substantially reduce the engineering hours and, therefore, reduce the project's overall costs. The developed solution can also be potentially applied to other EPC companies doing a similar design for complex installations with high safety standards like oil and gas or petrochemical plants because the design errors it captures are common within this industry. It also could motivate practitioners and researchers to create similar products for the various fields within engineering industry.
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| 8. |
- Fabijan, Aleksander, et al.
(författare)
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The Online Controlled Experiment Lifecycle
- 2020
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Ingår i: IEEE Software. - : IEEE. - 0740-7459 .- 1937-4194. ; 37:2, s. 60-67
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Tidskriftsartikel (refereegranskat)abstract
- Online Controlled Experiments (OCEs) enable an accurate understanding of customer value and generate millions of dollars of additional revenue at Microsoft. Unlike other techniques for learning from customers, OCEs establish an accurate and causal relationship between a change and the impact observed. Although previous research describes technical and statistical dimensions, the key phases of online experimentation are not widely known, their impact and importance are obscure, and how to establish OCEs in an organization is underexplored. In this paper, using a longitudinal in-depth case study, we address this gap by (1) presenting the Experiment Lifecycle, and (2) demonstrating with four example experiments their profound impact. We show that OECs help optimize infrastructure needs and aid in project planning and measuring team efforts, in addition to their primary goal of accurately identifying what customers value. We conclude that product development should fully integrate the Experiment Lifecycle to benefit from the OCEs.
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| 9. |
- Figalist, Iris, et al.
(författare)
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Breaking the Vicious Circle : Why AI for software analytics and business intelligence does not take off in practice
- 2020
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Ingår i: 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). - : IEEE. - 9781728195322 - 9781728195339 ; , s. 5-12
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Konferensbidrag (refereegranskat)abstract
- In recent years, the application of artificial intelligence (AI) has become an integral part of a wide range of areas, including software engineering. By analyzing various data sources generated in software engineering, it can provide valuable insights into customer behavior, product performance, bugs and errors, and many more. In practice, however, AI for software analytics and business intelligence often gets stuck in a prototypical stage and the results are rarely used to make decisions based on data. To understand the underlying root causes of this phenomenon, we conduct both an explanatory case study and a survey on the challenges of realizing and utilizing artificial intelligence in the context of software-intensive businesses. As a result, we identify a vicious circle that prevents practitioners from moving from prototypical analytics to continuous and productively usable software analytics and business intelligence based on AI.
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| 10. |
- Figalist, Iris, et al.
(författare)
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Breaking the vicious circle: A case study on why AI for software analytics and business intelligence does not take off in practice
- 2022
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Ingår i: Journal of Systems and Software. - : Elsevier BV. - 0164-1212 .- 1873-1228. ; 184
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, the application of artificial intelligence (AI) has become an integral part of a wide range of areas, including software engineering. By analyzing various data sources generated in software engineering, it can provide valuable insights into customer behavior, product performance, bugs and errors, and many more. In practice, however, AI for software analytics and business intelligence often remains at a prototypical stage, and the results are rarely used to make decisions based on data. To understand the underlying causes of this phenomenon, we conduct an explanatory case study consisting of and interview study and a survey on the challenges of realizing and utilizing artificial intelligence in the context of software-intensive businesses. As a result, we identify a vicious circle that prevents practitioners from moving from prototypical AI-based analytics to continuous and productively usable software analytics and business intelligence solutions. In order to break the vicious circle in a targeted manner, we identify a set of solutions based on existing literature as well as the previously conducted interviews and survey. Finally, these solutions are validated by a focus group of experts.
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