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عدد المساهمات : 19001 التقييم : 35505 تاريخ التسجيل : 01/07/2009 الدولة : مصر العمل : مدير منتدى هندسة الإنتاج والتصميم الميكانيكى
| موضوع: كتاب Mastering Uncertainty in Mechanical Engineering الأربعاء 19 يناير 2022, 1:55 am | |
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أخواني في الله أحضرت لكم كتاب Mastering Uncertainty in Mechanical Engineering Editors Peter F. Pelz, Peter Groche, Marc E. Pfetsch and Maximilian Schaeffner
و المحتوى كما يلي :
Contents 1 Introduction Peter F. Pelz 2 Types of Uncertainty Peter F. Pelz, Marc E. Pfetsch, Sebastian Kersting, Michael Kohler, Alexander Matei, Tobias Melz, Roland Platz, Maximilian Schaeffner and Stefan Ulbrich 3 Our Speciϐic Approach on Mastering Uncertainty Peter F. Pelz, Robert Feldmann, Christopher M. Gehb, Peter Groche, Florian Hoppe, Maximilian Knoll, Jonathan Lenz, Tobias Melz, Marc E. Pfetsch, Manuel Rexer and Maximilian Schaeffner 4 Analysis, Quantiϐication and Evaluation of Uncertainty Maximilian Schaeffner, Eberhard Abele, Reiner Anderl, Christian Bölling, Johannes Brötz, Ingo Dietrich, Robert Feldmann, Christopher M. Gehb, Felix Geßner, Jakob Hartig, Philipp Hedrich, Florian Hoppe, Sebastian Kersting, Michael Kohler, Jonathan Lenz, Daniel Martin, Alexander Matei, Tobias Melz, Tuğrul Öztürk, Peter F. Pelz, Marc E. Pfetsch, Roland Platz, Manuel Rexer, Georg Staudter, Stefan Ulbrich, Moritz Weber and Matthias Weigold 5 Methods and Technologies for Mastering Uncertainty Peter Groche, Eberhard Abele, Nasssr Al-Baradoni, Sabine Bartsch, Christian Bölling, Nicolas Brötz, Christopher M. Gehb, Felix Geßner, Benedict Götz, Jakob Hartig, Philipp Hedrich, Daniel Hesse, Martina Heßler, Florian Hoppe, Laura Joggerst, Sebastian Kersting, Hermann Kloberdanz, Maximilian Knoll, Michael Kohler, Martin Krech, Jonathan Lenz, Michaela Leštáková, Kevin T. Logan, Daniel Martin, Tobias Melz, Tim M. Müller, Tuğrul Öztürk, Peter F. Pelz, Roland Platz, Andrea Rapp, Manuel Rexer, Maximilian Schaeffner, Fiona Schulte, Julian Sinz, Jörn Stegmeier, Matthias Weigold and Janine Wendt 6 Strategies for Mastering Uncertainty Marc E. Pfetsch, Eberhard Abele, Lena C. Altherr, Christian Bölling, Nicolas Brötz, Ingo Dietrich, Tristan Gally, Felix Geßner, Peter Groche, Florian Hoppe, Eckhard Kirchner,Hermann Kloberdanz, Maximilian Knoll, Philip Kolvenbach, Anja Kuttich-Meinlschmidt, Philipp Leise, Ulf Lorenz, Alexander Matei, Dirk A. Molitor, Pia Niessen, Peter F. Pelz, Manuel Rexer, Andreas Schmitt, Johann M. Schmitt, Fiona Schulte, Stefan Ulbrich and Matthias Weigold 7 Outlook Peter F. Pelz, Peter Groche, Marc E. Pfetsch and Maximilian Schaeffner Glossary Acceptability Active system Actuator Adaptive system Aleatoric uncertainty Algorithm Anticipating availability Black-box model Buffering capacity Glossary One of three dimensions of a system’s quality besides effort and availability. Formal acceptability is reached by conformity with explicit legal constraints or any implicit conventions; informal acceptability is fostered by functional quality and minimal social costs. system, active An energy converter generating potential inϐluence on a process. system, adaptive uncertainty, aleatoric Finite sequence of (computer-)instructions to solve a problem. Predictive process (and system) change with the aim of reducing uncertainty. Anticipating is one of four abilities/functions of a resilient system. One of three dimensions of a system’s quality besides effort and acceptability. Availability measures the relative usability of a technical system in time. model, black-box Metric for evaluating the resilience of a technical system. The buffering capacity of a technical system measures the amount of structural change for which the fulϐilment of a predetermined required minimum of functional performance can still be guaranteed. Depending on the context, the buffering capacity can attain continuous or integer values. (Example: In case of integer values, it describes the maximum number of components that can fail while still maintaining the required minimum of functional performance.) A system has a buffering capacity of k if it guarantees the required minimum of functional performance within a predetermined range of inϐluencing factors for all possible failure scenarios of up to k components.Component Conϔlict, data-induced Constraint Data Data management Data uncertainty Data-induced conϔlict Design Design point Design space Synonym for an assembly or single item. A data-induced conϐlict exists when the interpretation and usage of uncertain data from more than one source leads to contradictory statements about the appropriate design of processes or products. Requirement for the design and usage/operation of a sociotechnical system. Generic term for a quantiϐiable system value. Data shall be ϐindable, accessible, interoperable, reusable (FAIR). As such, FAIR data management fosters transparency and hence acceptability. It is the prerequisite for quality key performance indicators (quality KPI). The nature of data uncertainty depends on the form in which data are available. If data are stochastically distributed, there is stochastic uncertainty. If they are known to be within limits, but not stochastically distributed, there is incertitude. Unnoticed or ignored uncertainty occurs when there is neither stochastic uncertainty nor incertitude. conϔlict, data-induced Methodical procedure from the ϐirst idea through planning, conception and development to the virtual elaboration of a (loadbearing) product. A technical system is designed for a speciϐic design point. If the system designer strives for a robust design, considerations not only comprise one design point, but also an uncertainty area around it. The function of a system can only be realised within a certain design space. The design space is limited by physical laws as well as by the available resources or resource materials, components and technologies. The design space can be expanded by innovations or restricted by banning technologies, e.g. by the requirement for carbonfree energy supply. Systems that enable the same function usually differ in quality. The task of Sustainable Systems Design is to select from these competing systems one with an optimal quality within the design space.Diagnosis Disturbance Effort Epistemic uncertainty Flexibility Function Functional requirement Gracefulness Grey-box model Hardware-in-the-Loop A diagnosis is used to ϐind the cause of disturbances. If a disturbance is not directly observable but only its effect on the system, only the symptoms of the disturbance can be observed. The diagnosis allows conclusions from these symptoms on the cause. In particular, it serves to ϐind causes in data-induced conϔlicts. A disturbance leads to unexpected, unauthorised deviations of at least one system value. This can lead to a malfunction or failure of the system. One of three dimensions of a system’s quality besides acceptability and availability. Effort measures the investment costs and social costs given by energy and material consumption to achieve a desired system function. uncertainty, epistemic A ϐlexible system is characterised by the fact that it fulϐils multiple predeϐined functions with accepted functional quality. Flexibility is used as a strategy to master uncertainty during the product life cycle of technical systems. Desired relationship between a system’s input and output with the aim of fulϐilling a task. Between stakeholders agreed and predeϐined function of a system. Metric for evaluating the resilience of a technical system. Its behaviour may be described as “graceful degradation” at the boundary of its performance range towards the loss of the required minimum degree of the functional performance. Mathematically, it is deϐined by the directional derivative of the functional performance curve in the direction of a given inϐluencing factor (or a vector of multiple inϐluencing factors). In the case of non-differentiability, it is given by the limit from the direction of the design point. model, grey-box Hardware-in-the-Loop (HiL) tests investigate the behaviour of real components connected to real-time simulated systems and allow the stepwise integration of a technical module or component into a real system by combining cyber world and real world.Ignorance Incertitude Information Irrelevant reality Learning Margin Model Model, black-box Model, grey-box Model, white-box Disregarded but relevant reality. The effect of uncertainty is unknown or only suspected. Ignorance is associated with model uncertainty and with ignored possible manifestations of a product, system or process. No statement can be made about the probability distributions of an unfolding uncertain property. Limit values of an emerging uncertain product characteristic can be assumed. Furthermore, no probability distributions have to be presumed. There are known or estimated membership functions in fuzzy analysis or intervals in interval analysis. The variability is uncertain. Information is derived from the interpretation of data and serves as the basis for decisions. Interpretation may be performed within models. reality, irrelevant Reduction of model uncertainty and data uncertainty through permanent model identiϐication and model adaptation during the product life cycle. Learning is one of four abilities/functions of a resilient system. Metric for evaluating the resilience of a technical system. The margin of a technical system is the distance of the actual functional performance to the system’s required minimum of functional performance. Abstract image of an object in form of a mathematical model or other, such as imaginary on the basis of intuition. A mathematical model is substantiated in axiomatic (white-box model) or empirical (black-box model) terms or both (grey-box model). Mathematical models represent a functional relationship between input and output data, model parameters and internal variables, like states. Model derived from measurements of a process or the experience of a user. In the ϐirst case, these models are called datadriven models today. In the second case, the deposited model is part of an expert system. Model that combines axiomatic and empirically derived relationships as well as (expert) user experience.Model horizon Model uncertainty Module Monitoring Object Objective Operator Parameter (model) Passive system Performance range Model derived by deduction from axioms. Model uncertainty in whitebox models arises from an impermissible model structure or impermissible simpliϐications, e.g. an assumption of quasi-stationary system behaviour, inadmissible constitutive equations as well as inadmissible initial and boundary conditions. Boundary of the relevant reality represented by the model. Model uncertainty arises from an incomplete mapping of the object. Parts of the relevant reality are ignored. In the case of model uncertainty, the functional relationship is suspected, unknown, incomplete or ignored—ignorance prevails in all cases. Function-oriented group of components of a technical entity or algorithm; each with clear interfaces. Sensing a process by means of data acquisition and data analysis via models to obtain information. Monitoring is the one of four abilities/functions of a resilient system. Generic term for product, system or process. Target for the design and usage/operation of a sociotechnical system. An operator provides an effective quantity to be able to carry out a process. The effective quantity is the purpose of the operator and thus causes the desired change of state. In production, for example, the operator comprises machines and the necessary auxiliary material for the production process of the load-bearing structure. In the process of usage, however, the operator refers, for example, to the used load-bearing system. Model parameters are brought into a functional relationship in a model. They are a data component. Model parameters are derived from empirical data, literature or model analysis. system, passive Basis for assessing the resilience of a technical system. The performance range describes the range of inϐluencing factors in which a technical system is able to achieve a predeϐined required minimum functional performance. The performance rangeProcess Process, time-invariant Process, time-variant Process chain Process chain, resilient Process model Product Product life cycle Product properties Production Quality can be mathematically expressed by the so-called “superlevel set” of the functional performance curve at the level of the required minimum functional performance. A process transforms a primary state into a ϐinal state. The process is associated with an individual process or a process chain. A process started at a time always shows the same behaviour. It can start at any time without a change of result. The parameters of its mathematical description and transfer functions of a controller are for example invariable in time (invariant). A process started at a time shows different behaviour over time, see time-invariant process. A process chain is the combination of individual processes. They transform a primary state into a ϐinal state, with the operand going through various intermediate states. Process chains can be modelled across life cycle phases. A process chain can also be used to represent a component structure. In a resilient process chain, monitoring, responding, learning and anticipating internal and external disturbances (for example machine failure, manufacturing uncertainty, slump in demand, and uncertainty in product usage) can be used to address ignorance. Common and applicable mode of communication for the various areas of expertise to deϐine a process chain incorporating systematically and transparently the individual processes and the resulting uncertainty. A product is an object that did not originate naturally, but is produced by man himself for other people, and that is used or consumed in the context of purpose-oriented action in usage processes. The product life cycle describes the process chain: sourcing, production, usage and reuse/recycling. Properties of products or systems are divided into function and quality. The process of making products, components or systems.Radius of performance Reality, irrelevant Reality, relevant Reliability Resilient process chain Resilient system Responding Risk analysis Robust Design Measures—in the tradition of Taguchi—the effort with which a function is achieved. The effort is measured in economic and social costs. In addition, there is the availability and the acceptability. Metric for evaluating the resilience of a technical system. It is connected to the technical system’s performance range. The radius of performance measures the minimum distance of the design point to the speciϐic value of an inϐluencing variable for which the required minimum level of functional performance is no longer reached. The part of reality that is not necessary to answer a question. The part of reality necessary to answer a question. The feature of a product to not fail with a certain probability under stated functional and environmental conditions during a speciϐied period of time. process chain, resilient system, resilient Process intervention based on information with the aim of reducing uncertainty. Responding is one of four abilities/functions of a resilient system. Speciϐic operational measures to deal with uncertainty at the real process and product level. Risk analysis is limited to the identiϐication and description of risks. The deϐinition and application of speciϐic measures are covered by risk management. Robust Design is an engineering design methodology also known as Taguchi methods. In Robust Design (i) uncertainty is replaced by stochastic uncertainty using the concept of quality loss functions; (ii) sourcing, design and production phases are hollistically treated by the concept of off-line quality control. The basis of off-line quality control is ϐirstly the Design of Experiments (DoE) and secondly the robust optimisation in the so-called parameter design. Taguchi developed the methodology based on previous works of Ronald Fisher on DoE. In modern Robust Design the concept of perceived quality, i.e. customer experience, and social costs as quality measures are already anticipated.Robust optimisation Robustness Semi-active system Socio-technical system Soft sensor State variable Stochastic uncertainty Structural uncertainty Structure Sustainable Systems Design A product is designed and optimised in such a way that, even with unavoidable inϐluence of disturbances and variations of input variables within the model horizon, the user expectations are completely fulϐilled. A robust system proves to be insensitive or only insigniϐicantly sensitive to deviations in system properties or varying usage. Robustness is used as a strategy to master uncertainty from the different perspectives of mathematical optimisation, product or system design and production. system, semi-active In contrast to technical systems or technoeconomic systems that only include technical components and their interaction regarding the ϐlux of energy, material or information including money, socio-technical systems take into account the human being and the technical components. The effects of the interaction of humans and technology play a decisive role in the analysis of sociotechnical systems. Model-based acquisition of information at the component and/or system level. The target value is not measured directly, but determined based on a model. As such, soft sensors are familiar with state observers and Kalman ϐilters based on Bayesian methods. The state variables, according to the state space representation of system theory, describe the current state of a system, regardless of its origin, e.g. force, speed, etc. uncertainty, stochastic Only a part of all possible structures of the design space is evaluated, i.e. the remaining part of the design space is ignored. Combination of functions (functional structure), components (component structure) or process chains to fulϐil a function. Engineering design methodology representing the design process as a constraint optimisation process: functional requirements and design space form the constraints, qualitySystem System, active System, adaptive System, passive System, resilient System, semi-active Techno-economic system dimensions give the objectives. The three quality dimensions are minimal effort, maximal availability and maximal acceptability. The system describes the totality of all elements considered. Setting a system boundary deϐines the object or product, respectively, the objects or products. An active system is characterised by the supply of external energy to inϐluence a process. The external energy always inϐluences the process via the operator. The term external energy does not include energy that is available to fundamentally necessary operations within the process, in particular no supply energy. A technical system that can be adjusted to the particularities of various situations, due to its technical characteristics. Adaptivity is the prerequisite for a resilient system. A passive system is characterised by the fact that external energy is only provided for the processes that are fundamentally necessary in the process, i.e. in particular as supply energy. A resilient technical system guarantees a predetermined minimum of functional performance even in the event of disturbances or failure of system components and a subsequent possibility of recovering at least the setpoint function. Resilience can be increased by adjusting the system state via monitoring, responding, learning and/or anticipating, as well as by systematically designing the system topology. A semi-active system is characterised by the supply of external energy to inϐluence the operator. In this case, any properties of the operator can be inϐluenced by the external energy. The process itself is only affected indirectly by the external energy. The term external energy does not include any energy that is available to fundamentally necessary operations within the process, in particular no supply energy. In contrast to technical systems that only include technical components and their interaction, techno-economic systems take into account the ϐlux of money and economic measures such as proϐit.Time-invariant process Time-variant process Uncertainty Uncertainty, aleatoric Uncertainty, epistemic Uncertainty, stochastic Usage Validation Veriϔication Vulnerability White-box model process, time-invariant process, time-variant Uncertainty occurs when the usage properties and process characteristics of a system cannot, or can only be partially determined. Natural, random and irreducible uncertainty. Uncertainty due to incomplete scientiϐic knowledge. Epistemic uncertainty can be reduced by new insights. Partial to complete details on probability distributions of an emerging uncertain product characteristic are available. There are known or estimated probability density functions; the variability is always determined. Usage or operation of a component, product, system or process. Analysis to what extent a model after calibration is suitable for the description of a relevant functional relationship by comparison of reality and model. Furthermore, evaluation to what extent a product meets the predeϐined quality and functional constraints and to what extent a product is accepted by the customer and the society. Review whether the model is consistent and has been correctly solved. Furthermore, evaluation to what extent the design and production methods and technologies are selected correctly. A system’s vulnerability or violability. model, white-box
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