738 research outputs found

    A Data Driven Sequential Learning Framework to Accelerate and Optimize Multi-Objective Manufacturing Decisions

    Full text link
    Manufacturing advanced materials and products with a specific property or combination of properties is often warranted. To achieve that it is crucial to find out the optimum recipe or processing conditions that can generate the ideal combination of these properties. Most of the time, a sufficient number of experiments are needed to generate a Pareto front. However, manufacturing experiments are usually costly and even conducting a single experiment can be a time-consuming process. So, it's critical to determine the optimal location for data collection to gain the most comprehensive understanding of the process. Sequential learning is a promising approach to actively learn from the ongoing experiments, iteratively update the underlying optimization routine, and adapt the data collection process on the go. This paper presents a novel data-driven Bayesian optimization framework that utilizes sequential learning to efficiently optimize complex systems with multiple conflicting objectives. Additionally, this paper proposes a novel metric for evaluating multi-objective data-driven optimization approaches. This metric considers both the quality of the Pareto front and the amount of data used to generate it. The proposed framework is particularly beneficial in practical applications where acquiring data can be expensive and resource intensive. To demonstrate the effectiveness of the proposed algorithm and metric, the algorithm is evaluated on a manufacturing dataset. The results indicate that the proposed algorithm can achieve the actual Pareto front while processing significantly less data. It implies that the proposed data-driven framework can lead to similar manufacturing decisions with reduced costs and time

    Transitioning an Alkaliphilic and Photosynthetic Microbial Consortium from Laboratory to Outdoor Demonstration Scale

    Get PDF
    The 21st century’s challenges—climate change, growing population, resource decline, habitat and species loss—mean that current practices must be replaced, redesigned, and improved. Phytoplankton, reliant on water, light, nutrients, and CO2, offer versatile applications in nutritional supplements, agricultural feed, bioplastics, wastewater treatment, and bioenergy production. Currently, the most successful commercial ventures center on select taxa like Spirulina and Chlorella and produce high-value products for human consumption. Expanding the scope of viable commercial taxa and their applications hinges on overcoming critical challenges in cultivation, notably biomass productivity, robustness, and resource use. Inspiration can be drawn from natural environments where phytoplankton flourish, like alkaline soda lakes. These lakes are characterized by elevated pH and high carbonate alkalinity. Growing phytoplankton in high pH (10+), high carbonate alkalinity medium (0.5 M) increases the driving force for CO2 capture into solution and helps exclude competitors and predators which can cause biomass instability. This thesis chronicles the transition of biomass from alkaline soda lakes, dominated by the cyanobacterium Sodalinema alkaliphilum, from laboratory to large-scale outdoor demonstration. Chapter 2 explores the microbes inhabiting such lakes and their societal applications. Chapter 3 describes the design, construction, and operation of laboratory photobioreactors with programmable lighting and online growth measurements. Chapter 4 follows outdoor biomass cultivation in a 1,000 L photobioreactor, demonstrating sustained growth at a pH sufficient for CO2 capture from air. In Chapter 5, cultivation in a 3,000 L open raceway pond (ORP) reports long-term medium re-use, water requirements, and CO2 capture from air, although optimisation is necessary. Operational seasons ranged 70–140 days—160 being the maximum possible in Calgary’s temperate climate. Average daily yields were ∼ 3–4 g/m2/day (ash-free) with modeling predicting productivity could reach 6 g/m2/day by reducing biomass density. Finally, Chapter 6 quantifies ORP biomass losses, with stable isotope probing unveiling insights into S. alkaliphilum physiology and ecology. In conclusion, this research explores the feasibility of growing S. alkaliphilum biomass at scale for extended durations and has generated baseline data and operational insights which can be used to inform and refine the sustainability and productivity of future iterations of this technology

    A review

    Get PDF
    Funding Information: Radu Godina acknowledges Fundação para a Ciência e a Tecnologia ( FCT - MCTES) for its financial support via the project UIDP/00667/2020 and UIDB/00667/2020 (UNIDEMI). JPO acknowledges funding by national funds from FCT - Fundação para a Ciência e a Tecnologia, I.P., in the scope of the projects LA/P/0037/2020 , UIDP/50025/2020 and UIDB/50025/2020 of the Associate Laboratory Institute of Nanostructures, Nanomodelling and Nanofabrication – i3N. This activity has received funding from the European Institute of Innovation and Technology (EIT) – Project Smart WAAM: Microstructural Engineering and Integrated Non-Destructive Testing . This body of the European Union receives support from the European Union's Horizon 2020 research and innovation programme. Publisher Copyright: © 2023 The AuthorsGrowing consciousness regarding the environmental impacts of additive manufacturing (AM) processes has led to research focusing on quantifying their environmental impacts using Life Cycle Assessment (LCA) methodology. The main objective of this paper is to review the state of the art of the existing LCA studies of AM processes. In this paper, a systematic literature review is carried out where a total of 77 papers focusing on LCA, including social-Life Cycle Assessment (S-LCA), are analyzed. Accordingly, the application of LCA methodology to different AM technologies was studied and different research themes such as the goal and scope of LCA studies, life cycle inventory data for different AM technologies, AM part quality and mechanical properties, the environmental, economic, and social performances of various AM technologies, and factors affecting AM´s sustainability potential were analyzed. Based on the critical analysis of the existing research, five major shortcomings of the existing research are realized: (i) some AM technologies are under studied; (ii) more focus only on the environmental sustainability dimension of AM, neglecting its economic and social dimensions; (iii) exclusion of AM pat quality and its mechanical performance from the sustainability assessment; (iv) not enough focus on the life cycle stages after product manufacture by AM; (v) effect of different product variables on AM´s sustainability not studied extensively. Lastly, based on these shortcomings realized, the following research directions for future works are suggested: (i) inclusion of new AM materials and technologies; (ii) transition to a triple-bottom-line sustainability assessment considering environmental, economic, and social dimensions of AM; (iii) extending the scope of LCA studies to post-manufacture stages of AM products; (iv) development of predictive environmental impact and cost models; (v) integration of quality and mechanical characterization with sustainability assessment of AM technologies.publishersversionpublishe

    Energy-aware coordination of machine scheduling and support device recharging in production systems

    Get PDF
    Electricity generation from renewable energy sources is crucial for achieving climate targets, including greenhouse gas neutrality. Germany has made significant progress in increasing renewable energy generation. However, feed-in management actions have led to losses of renewable electricity in the past years, primarily from wind energy. These actions aim to maintain grid stability but result in excess renewable energy that goes unused. The lost electricity could have powered a multitude of households and saved CO2 emissions. Moreover, feed-in management actions incurred compensation claims of around 807 million Euros in 2021. Wind-abundant regions like Schleswig-Holstein are particularly affected by these actions, resulting in substantial losses of renewable electricity production. Expanding the power grid infrastructure is a costly and time-consuming solution to avoid feed-in management actions. An alternative approach is to increase local electricity consumption during peak renewable generation periods, which can help balance electricity supply and demand and reduce feed-in management actions. The dissertation focuses on energy-aware manufacturing decision-making, exploring ways to counteract feed-in management actions by increasing local industrial consumption during renewable generation peaks. The research proposes to guide production management decisions, synchronizing a company's energy consumption profile with renewable energy availability for more environmentally friendly production and improved grid stability

    Modeling and Simulation in Engineering

    Get PDF
    The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering

    Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

    Get PDF
    While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments

    Post-Growth Geographies: Spatial Relations of Diverse and Alternative Economies

    Get PDF
    Post-Growth Geographies examines the spatial relations of diverse and alternative economies between growth-oriented institutions and multiple socio-ecological crises. The book brings together conceptual and empirical contributions from geography and its neighbouring disciplines and offers different perspectives on the possibilities, demands and critiques of post-growth transformation. Through case studies and interviews, the contributions combine voices from activism, civil society, planning and politics with current theoretical debates on socio-ecological transformation

    Otimização robusta multivariada no fresamento de topo do aço inoxidável duplex UNS S32205

    Get PDF
    Duplex stainless steel pertains to a class of materials with low machinability due to its right rate of hardening, low thermal conductivity and high ductility. This characteristic represents a significant challenge in the manufacture of components, especially in the end milling process. Optimization is a viable alternative to determine the best process parameters and obtain higher production values with sustainability and quality. The presence of noise variables is an additional complicating factor during material machining of this material, and their presence causes an increase in variability during the process, and their effect can be mitigated by employing robust modelling methods. This thesis presents the robust multivariate optimization in the end milling of duplex stainless steel UNS S32205. The tests were carried out using a central composite design combining the input variables (cutting speed, feed per tooth, milled width and depth of cut) and the noise variables (tool flank wear, fluid flow and overhang length). The concept of robust parameter design, response surface methodology, factor analysis, optimization of the multivariate mean square error for robust factors and the normal boundary intersection were applied. The combination of all these methodologies gave rise to the EQMMFR-NBI method. As a result of the factor analysis, the response variables were grouped into 3 latent variables, the first referring to the roughness Ra, Rq, Rt and Rz (quality indicator); the second to the electricity consumption and CO2 emissions (sustainability indicator) and the third to the material removal rate (productivity indicator). Multivariate robust optimization was performed considering sustainability and productivity indicators, while quality was used as a constraint to the nonlinear optimization problem. By applying the EQMMFR-NBI method, Pareto optimal solutions were obtained and an equispaced frontier was constructed. Confirmation tests were performed using Taguchi's L9 arrangement. The results showed that the optimal setups found were able to neutralize the influence of noise variables on the response variables, proving the good adequacy of proposal and the application of the method.O aço inoxidável duplex pertence a uma classe de materiais com baixa usinabilidade por apresentar alta taxa de encruamento; baixa condutividade térmica e alta ductilidade, o que representa um grande desafio na fabricação de componentes, principalmente no processo de fresamento de topo. A otimização é uma alternativa viável para determinar os melhores parâmetros do processo e obter maiores valores de produtividade com sustentabilidade e qualidade. A presença de variáveis de ruído é um complicador adicional durante a usinagem desse material. Sua presença provoca um aumento de variabilidade durante o processo, porém, seu efeito pode ser atenuado ao se empregar métodos de modelagem robusta. Esta tese tem como objetivo apresentar a otimização robusta multivariada no fresamento de topo do aço inoxidável duplex UNS S32205. Os ensaios foram realizados seguindo um planejamento composto central combinando as variáveis de entrada (velocidade de corte, avanço por dente, largura fresada e profundidade de corte) e as variáveis de ruído (desgaste do flanco da ferramenta, vazão de fluido e comprimento em balanço). Foi aplicado o conceito de projeto parâmetro robusto, metodologia de superfície de resposta, análise fatorial, a otimização do erro quadrático médio multivariado para fatores robustos e o método da interseção normal à fronteira. A combinação de todas essas metodologias deu origem ao método EQMMFR-NBI. Como resultado da análise fatorial, as variáveis de resposta foram agrupadas em 3 variáveis latentes, sendo a primeira referente às rugosidades Ra, Rq, Rt e Rz (indicador de qualidade); a segunda ao consumo de energia elétrica e emissão de CO2 (indicador de sustentabilidade) e a terceira à taxa de remoção de material (indicador de produtividade). A otimização robusta multivariada foi realizada considerando os indicadores de sustentabilidade e produtividade, enquanto o de qualidade foi usado como restrição ao problema de otimização não linear. Ao aplicar o método EQMMFR-NBI, soluções ótimas de Pareto foram obtidas e uma fronteira equiespaçada foi construída. Os ensaios de confirmação foram realizados utilizando o arranjo L9 de Taguchi. Os resultados mostraram que os setups ótimos encontrados foram capazes de neutralizar a influência das variáveis de ruído nas variáveis de resposta, comprovando uma boa adequação da proposta e da aplicação do método

    A data-driven approach for a project management methodology for R&D Projects.

    Get PDF
    267 p.The thesis is based on the proposal of an R&D project management methodology based on the Earned Quality Method (EQM) and data analysis to improve the efficiency of R&D projects in a near-real production environment in a TRL 5 to 7. The thesis relies upon published papers that propose measuring and improving the management of R&D projects. The methodology leans on the formulation and gradual and recurrent evaluation of quality criteria as a performance indicator of the work carried out. The thesis stands on the concept that quality is a measurable quantity that accumulates throughout the project. The proposed project management methodology is built on three main aspects: Collaboration between the University and Industry; The correct interpretation of the TRL where research projects are developed; The study of different metrics for project management, such as the measurement of the success of projects, the KPIs of a project-based organisation, and the EQM. The methodology has been tested with three actual use cases with different characteristics in terms of project size, funding and team members; and validated on an R&D Centre in Advanced Manufacturing in Aeronautics. The pillars of the thesis are focused on the analysis of the mentioned components and their integration for the development of a methodology to improve the efficiency in the use of resources and the quality of obtained results in the R&D projects' framework. The key findings of these studies demonstrate the effectiveness of using quality criteria for measuring progress in the management of R&D projects, as well as providing a better understanding of several critical aspects of the realisation of these projects
    corecore