Mastering Uncertainty in Mechanical Engineering.

• Superior document: Springer Tracts in Mechanical Engineering
• Author: Pelz, Peter F.
• TeilnehmendeR: Groche, Peter.; Pfetsch, Marc E.; Schaeffner, Maximilian.
• Year of Publication: 2021
• Language: English
• Series: Springer Tracts in Mechanical Engineering
• Physical Description: 1 online resource (483 p.)
• Notes: Description based upon print version of record.

This open access book reports on innovative methods, technologies and strategies for mastering uncertainty in technical systems. Despite the fact that current research on uncertainty is mainly focusing on uncertainty quantification and analysis, this book gives emphasis to innovative ways to master...

Table of Contents

• Intro
• Preface
• Acknowledgements
• Contents
• 1 Introduction
• 1.1 Motivation
• 1.2 Holistic Control of Uncertainty over the Phases of the Product Life Cycle
• 1.3 Components are Represented in Models
• 1.4 Data and Data Sources
• 1.5 Component Structures
• 1.6 Sustainable Systems Design-The Extended Motivation for This Book
• 1.7 Outlook on the Following Book Structure
• References
• 2 Types of Uncertainty
• 2.1 Data Uncertainty
• 2.1.1 Introduction
• 2.1.2 Stochastic Data Uncertainty
• 2.1.3 Incertitude
• 2.2 Model Uncertainty
• 2.2.1 Functional Relations, Scope and Complexity of Mathematical Models
• 2.2.2 Approaches to Detect, Quantify, and Master Model Uncertainty
• 2.3 Structural Uncertainty
• References
• 3 Our Specific Approach on Mastering Uncertainty
• 3.1 Beyond Existing Approaches
• 3.2 Uncertainty Propagation Through Process Chains
• 3.3 Five Complementary Methods for Mastering Uncertainty in Process Chains
• 3.4 Time-Variant, Dynamic and Active Processes
• 3.5 Strategies for Mastering Uncertainty-Robustness, Flexibility, Resilience
• 3.6 Exemplary Technical System Mastering Uncertainty
• 3.6.1 Modular Active Spring-Damper System
• 3.6.2 Active Air Spring
• 3.6.3 3D Servo Press
• References
• 4 Analysis, Quantification and Evaluation of Uncertainty
• 4.1 Identification of Uncertainty During Modelling of Technical Processes
• 4.1.1 Analysis of Data Uncertainty Using the Example of Passive and Active Vibration Isolation
• 4.1.2 Bayesian Inference Based Parameter Calibration for a Mathematical Model of a Load-Bearing Structure
• 4.1.3 Model-Based Analysis of Uncertainty in Chained Machining Processes
• 4.2 Data-Induced Conflicts
• 4.2.1 Dealing with Data-Induced Conflicts in Technical Systems
• 4.2.2 Data-Induced Conflicts for Wear Detection in Hydraulic Systems
• 4.2.3 Fault Detection in a Structural System
• 4.3 Analysis, Quantification and Evaluation of Model Uncertainty
• 4.3.1 Detection of Model Uncertainty via Parameter Estimation and Optimum Experimental Design
• 4.3.2 Detection of Model Uncertainty in Mathematical Models of the 3D Servo Press
• 4.3.3 Assessment of Model Uncertainty for the Modular Active Spring-Damper System
• 4.3.4 Model Uncertainty in Hardware-in-the-loop Tests
• 4.3.5 Identification of Model Uncertainty in the Development of Adsorption Based Hydraulic Accumulators
• 4.3.6 Uncertainty Scaling-Propagation from a Real Model to a Full-Scale System
• 4.3.7 Improvement of Surrogate Models Using Observed Data
• 4.3.8 Uncertainty Quantification with Estimated Distribution of Input Parameters
• 4.4 Representation and Visualisation of Uncertainty
• 4.4.1 Ontology-Based Information Model
• 4.4.2 Visualisation of Geometric Uncertainty in CAD Systems
• 4.4.3 Digital Twin of Load Carrying Structures for the Mastering of Uncertainty
• References
• 5 Methods and Technologies for Mastering Uncertainty