| 1. |
- Phillips, Helen R. P., et al.
(author)
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Global distribution of earthworm diversity
- 2019
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In: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 366:6464, s. 480-
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Journal article (peer-reviewed)abstract
- Soil organisms, including earthworms, are a key component of terrestrial ecosystems. However, little is known about their diversity, their distribution, and the threats affecting them. We compiled a global dataset of sampled earthworm communities from 6928 sites in 57 countries as a basis for predicting patterns in earthworm diversity, abundance, and biomass. We found that local species richness and abundance typically peaked at higher latitudes, displaying patterns opposite to those observed in aboveground organisms. However, high species dissimilarity across tropical locations may cause diversity across the entirety of the tropics to be higher than elsewhere. Climate variables were found to be more important in shaping earthworm communities than soil properties or habitat cover. These findings suggest that climate change may have serious implications for earthworm communities and for the functions they provide.
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| 2. |
- Vidner, Olle, 1990-
(author)
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On Multi-Disciplinary Optimization in Engineer-to-Order Product Configuration
- 2023
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Licentiate thesis (other academic/artistic)abstract
- Customized products are becoming increasingly common, and increasingly important for maintaining a competitive advantage in certain industries. Being able to quickly and accurately respond to unique customer requirements can provide a competitive edge or even be the only path to survival. In practice, configurators are commonly used to manage the customization process, gathering the customer’s requirements and suggesting feasible solutions to the customer’s problem.Fostering and maintaining a viable product customization offering is not easy. A particularly challenging category of products is one where an extensive engineering effort might be needed to even produce a reliable estimate of the product’s price. These products are usually referred to as engineer-to-order (ETO) products.Prior work has pointed out the potential of using optimization as part of configuration solutions for ETO products, but the literature is limited in its extent and does not clearly prescribe how to structure and approach such solutions.This thesis outlines a conceptual and technical architecture for implementing optimization-based configuration solutions. Reusable primitives for supporting the routines involved in this architecture are provided. These findings are verified through application and evaluation within two industrial case studies, also yielding important industrial needs to cover in the future research and development of the proposed framework. By examining three additional case studies, common issues in the development and deployment of design automation (DA) systems are identified.Successful implementation of the proposed framework for optimization-based configurators can lead to two main benefits. First, engineering configurator prototypes can be developed rapidly, to test the viability of configurator projects – a category of projects prone to expensive failures. Second, optimization-based configurators can be used to support rapid design space exploration in early product development stages, leading to enhanced product knowledge in a critical phase, and in turn, increased product value.
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| 3. |
- Aeddula, Omsri, 1993-, et al.
(author)
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AI-driven Ossification Assessment in Knee MRI : A Product-Service System Development for Informed Clinical Decision-Making
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Other publication (other academic/artistic)abstract
- Background: Traditionally, assessing the degree of ossification in the epiphyseal plate for growth plate development relies on manual evaluation, which can be inefficient due to the complexities of the distal femoral epiphysis anatomy. Existing methods lack efficient detection techniques.Method: This study proposes an AI-based decision support system, designed within a product-service system (PSS) framework, to automate ossification assessment and detection of the distal femoral epiphysis in knee magnetic resonance imaging (MRI) data. The system leverages advanced machine learning techniques, specifically two Convolutional Neural Networks (CNNs), combined with computer vision techniques. This intelligent system analyzes MRI slices to predict the optimal slice for analysis and identify variations in the degree of ossification within individual datasets.Results: The proposed method's effectiveness is demonstrated using a set of T2-weighted gradient echo grayscale knee MRI data. The system successfully detects the complex anatomy of the distal femoral epiphysis, revealing variations in the degree of ossification ranging from completely closed/open to fully open/closed regions.Conclusions: This study presents a robust and efficient AI-based method, integrated within a PSS framework, for measuring the degree of ossification in the distal femoral epiphysis. This approach automates ossification assessment, providing valuable insights for clinical decision-making by clinicians and forensic practitioners. The PSS framework ensures seamless integration of the AI technology into existing workflows.
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| 4. |
- Aeddula, Omsri, 1993-
(author)
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Data-Driven Decision Support Systems for Product Development - A Data Exploration Study Using Machine Learning
- 2021
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Licentiate thesis (other academic/artistic)abstract
- Modern product development is a complex chain of events and decisions. The ongoing digital transformation of society, increasing demands in innovative solutions puts pressure on organizations to maintain, or increase competitiveness. As a consequence, a major challenge in the product development is the search for information, analysis, and the build of knowledge. This is even more challenging when the design element comprises complex structural hierarchy and limited data generation capabilities. This challenge is even more pronounced in the conceptual stage of product development where information is scarce, vague, and potentially conflicting. The ability to conduct exploration of high-level useful information using a machine learning approach in the conceptual design stage would hence enhance be of importance to support the design decision-makers, where the decisions made at this stage impact the success of overall product development process.The thesis aims to investigate the conceptual stage of product development, proposing methods and tools in order to support the decision-making process by the building of data-driven decision support systems. The study highlights how the data can be utilized and visualized to extract useful information in design exploration studies at the conceptual stage of product development. The ability to build data-driven decision support systems in the early phases facilitates more informed decisions.The thesis presents initial descriptive study findings from the empirical studies, showing the capabilities of the machine learning approaches in extracting useful information, and building data-driven decision support systems. The thesis initially describes how the linear regression model and artificial neural networks extract useful information in design exploration, providing support for the decision-makers to understand the consequences of the design choices through cause-and-effect relationships on a detailed level. Furthermore, the presented approach also provides input to a novel visualization construct intended to enhance comprehensibility within cross-functional design teams. The thesis further studies how the data can be augmented and analyzed to extract the necessary information from an existing design element to support the decision-making process in an oral healthcare context.
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| 5. |
- Aeddula, Omsri, 1993-
(author)
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Navigating Data Challenges: AI-Driven Decision Support for Product-Service System Development
- 2024
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Doctoral thesis (other academic/artistic)abstract
- Solution providers are transitioning from product-centric models to service-oriented solutions. This shift has led to the rise of Product-Service Systems (PSS), which offer a holistic approach by integrating physical products with associated services. However, the inherent complexity and collaborative nature of PSS development present a significant challenge: information gathering, analysis, and knowledge building. This is further amplified in the early stages of PSS development due to data challenges such as uncertainty, ambiguity, and complexity. This complicates informed decision-making, potentially leading to the risk of sub-optimal outcomes and impacting the success of final offerings.This research proposes an AI-powered data analysis approach to address these data challenges and augment the decision-making process of PSS development. The focus is on supporting early-stage decision-making, as decisions made at this stage greatly impact the success of final solutions. The research investigates how data can be utilized and visualized to extract actionable insights, ultimately facilitating informed decision-making.The presented research demonstrates that AI-powered data analysis effectively supports informed decision-making in early-stage PSS development. By extracting actionable insights from complex data, handling data limitations, and enabling informed strategic decisions, knowledge sharing, and collaboration are facilitated among stakeholders. Furthermore, integrating AI with visualization tools fosters knowledge building and a deeper understanding of system behavior, ultimately leading to more successful PSS solutions. The efficacy of AI-powered data analysis for handling diverse data types across application domains is demonstrated, potentially leading to benefits such as a deeper understanding of system behavior and proactive solution strategies. These advancements contribute to developing decision support systems specifically for PSS development.Overall, this research demonstrates the efficacy of AI-powered data analysis in overcoming data challenges and empowering decision-makers in early-stage PSS development. This translates to more informed choices, leading to the creation of successful and efficient PSS solutions.
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| 6. |
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| 7. |
- Byström, Johan, et al.
(author)
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A numerical study of the convergence in stochastic homogenization
- 2004
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In: Journal of Analysis and Applications. - 0972-5954. ; 2:3, s. 159-171
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Journal article (peer-reviewed)abstract
- This note makes the link between theoretical results on stochastic homogenization and effective computation of averaged coefficients for diffusion operators in random media. Examples of how to construct relevant random media and numerical results on the effective coefficients are given.
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| 8. |
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| 9. |
- Pilthammar, Johan, 1987-, et al.
(author)
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Framework for Simulation-Driven Design of Stamping Dies Considering Elastic Die and Press Deformations
- 2017
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In: PROCEEDINGS OF THE 20TH INTERNATIONAL ESAFORM CONFERENCE ON MATERIAL FORMING (ESAFORM 2017). - : American Institute of Physics (AIP). - 9780735415805
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Conference paper (peer-reviewed)abstract
- Sheet metal forming (SMF) simulations are used extensively throughout the development phase of industrialstamping dies. In these SMF simulations, the die and press are normally considered as rigid. Previous research has howevershown that elastic deformation in these parts has a significant negative impact on process performance. This paperdemonstrates methods for counteracting these negative effects, with a high potential for improved production support anda reduced lead time through a shorter try-out process. A structural finite element model (FE-model) of a simplified die isstudied. To account for elastic deformation, the blankholder surfaces are first virtually reworked by adjusting the nodalpositions on the die surfaces attaining a pressure distribution in accordance to the design phase SMF simulations with rigidsurfaces. The elastic FE-model with reworked surfaces then represents a stamping die in running production. The die isnow assumed to be exposed to changed process conditions giving an undesired blankholder pressure distribution. Thechanged process conditions could for example be due to a change of press line. An optimization routine is applied tocompensate the negative effects of the new process conditions. The optimization routine uses the contact forces acting onthe shims of the spacer blocks and cushion pins as optimization variables. A flexible simulation environment usingMATLAB and ABAQUS is used. ABAQUS is executed from MATLAB and the results are automatically read back intoMATLAB. The suggested optimization procedure reaches a pressure distribution very similar to the initial distributionassumed to be the optimum, and thereby verifying the method. Further research is needed for a method to transform thecalculated forces in the optimization routine back to shims thicknesses. Furthermore, the optimization time is relativelylong and needs to be reduced in the future for the method to reach its full potential.
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| 10. |
- Tatipala, Sravan, 1993-, et al.
(author)
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Introductory study of sheet metal forming simulations to evaluate process robustness
- 2018
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In: IOP Conference Series: Materials Science and Engineering. - : Institute of Physics Publishing (IOPP).
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Conference paper (peer-reviewed)abstract
- The ability to control quality of a part is gaining increased importance with desires to achieve zero-defect manufacturing. Two significant factors affecting process robustness in production of deep drawn automotive parts are variations in material properties of the blanks and the tribology conditions of the process. It is imperative to understand how these factors influence the forming process in order to control the quality of a formed part. This paper presents a preliminary investigation on the front door inner of a Volvo XC90 using a simulation-based approach. The simulations investigate how variation of material and lubrication properties affect the numerical predictions of part quality. To create a realistic lubrication profile in simulations, data of pre-lube lubrication amount, which is measured from the blanking line, is used. Friction models with localized friction conditions are created using TriboForm and is incorporated into the simulations. Finally, the Autoform-Sigmaplus software module is used to create and vary parameters related to material and lubrication properties within a user defined range. On comparing and analysing the numerical investigation results, it is observed that a correlation between the lubrication profile and the predicted part quality exists. However, variation in material properties seems to have a low influence on the predicted part quality. The paper concludes by discussing the relevance of such investigations for improved part quality and proposing suggestions for future work.
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