Meta-modeling for Manufacturing Processes

Auerbach, Thomas; Beckers, Markus; Buchholz, Guido; Eppelt, Urs; Gloy, Yves-Simon; Fritz, P.; Khawli, Toufik Al; Kratz, Stephan; Lose, Juliane; Molitor, Thomas; Reßmann, Axel; Thombansen, Ulrich; Veselovac, D.; Willms, Konrad; Gries, Thomas; Michaeli, Walter; Hopmann, Christian; Reisgen, Uwe; Schmitt, Robert; Klocke, Fritz

doi:10.1007/978-3-642-25489-5_20

Cited by 16 publications

(10 citation statements)

References 15 publications

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“…The conceptual design analysis is based on simulations that are performed on different parameter settings within the full design space. This allows for a complete overview of the solution properties that contribute well to the design optimization processes (Auerbach et al 2011). However the challenge rises when either the number of parameters increases or the time required for each single simulation grows.…”

Section: Meta-modelling Methodsmentioning

confidence: 99%

Meta-Modelling Techniques Towards Virtual Production Intelligence

Schulz

Khawli

2014

Lecture Notes in Production Engineering

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Decision making for competitive production in high-wage countries is a daily challenge where rational and irrational methods are used. The design of decision making processes is an intriguing, discipline spanning science. However, there are gaps in understanding the impact of the known mathematical and procedural methods on the usage of rational choice theory. Following Benjamin Franklin's rule for decision making formulated in London 1772, he called "Prudential Algebra" with the meaning of prudential reasons, one of the major ingredients of Meta-Modelling can be identified finally leading to one algebraic value labelling the results (criteria settings) of alternative decisions (parameter settings). This work describes the advances in Meta-Modelling techniques applied to multidimensional and multi-criterial optimization in laser processing, e.g. sheet metal cutting, including the generation of fast and frugal Meta-Models with controlled error based on model reduction in mathematical physical or numerical model reduction. Reduced Models are derived to avoid any unnecessary complexity. The advances of the Meta-Modelling technique are based on three main concepts: (i) classification methods that decomposes the space of process parameters into feasible and non-feasible regions facilitating optimization, or monotone regions (ii) smart sampling methods for faster generation of a Meta-Model, and (iii) a method for multi-dimensional interpolation using a radial basis function network continuously mapping the discrete, multi-dimensional sampling set that contains the process parameters as well as the quality criteria. Both, model reduction and optimization on a multi-dimensional parameter space are improved by exploring the data mapping within an advancing

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Section: Meta-modelling Methodsmentioning

confidence: 99%

Meta-Modelling Techniques Towards Virtual Production Intelligence

Schulz

Khawli

2014

Lecture Notes in Production Engineering

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“…Likewise, the information from the monitoring platform and the measured data is fed into the process control system. The control system uses reduced process models (e.g., meta-models [13,14], for viability in real-time) to determine an appropriate control response for diminishing the gap between the actual process output and the desired process output. These settings are used to adjust the manufacturing process.…”

Section: Instantiating the Frameworkmentioning

confidence: 99%

“…More recently, with concepts like zero-defect manufacturing gaining importance, the focus has shifted toward fault diagnosis and troubleshooting activities that consume a considerably larger portion of the process downtime [1,7] compared to fault detection activities. In this context, several data-driven [7][8][9][10][11], model-based [12][13][14], and statistical [15] approaches have been proposed to support Processes 2020, 8, 89 2 of 11 the identification of the underlying root cause of a fault. However, most of these approaches lack features necessary to completely diagnose and isolate a fault [16].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Data-Based and Model-Based Approach to Process Monitoring and Control in Sheet Metal Forming

et al. 2020

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The ability to predict and control the outcome of the sheet metal forming process demands holistic knowledge of the product/process parameter influences and their contribution in shaping the output product quality. Recent improvements in the ability to harvest in-line production data and the increased capability to understand complex process behaviour through computer simulations open up the possibility for new approaches to monitor and control production process performance and output product quality. This research presents an overview of the common process monitoring and control approaches while highlighting their limitations in handling the dynamics of the sheet metal forming process. The current paper envisions the need for a collaborative monitoring and control system for enhancing production process performance. Such a system must incorporate comprehensive knowledge regarding process behaviour and parameter influences in addition to the current-system-state derived using in-line production data to function effectively. Accordingly, a framework for monitoring and control within automotive sheet metal forming is proposed. The framework addresses the current limitations through the use of real-time production data and reduced process models. Lastly, the significance of the presented framework in transitioning to the digital manufacturing paradigm is reflected upon.

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“…Self-optimisation (ICD D) allows dynamic adaptations at different levels of production systems. On the level of production networks the research within the cluster focuses cybernetic production and logistics management (Schmitt et al 2011). Recent work in this area analyses the human factors in supply chain management (Brauner et al 2013), an approach that requires the close collaboration of the disciplines engineering, economics and social sciences (Chap.…”

Section: Scientific Roadmapmentioning

confidence: 99%

Advances in Production Technology

2015

Lecture Notes in Production Engineering

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Meta-modeling for Manufacturing Processes

Cited by 16 publications

References 15 publications

Meta-Modelling Techniques Towards Virtual Production Intelligence

Meta-Modelling Techniques Towards Virtual Production Intelligence

A Hybrid Data-Based and Model-Based Approach to Process Monitoring and Control in Sheet Metal Forming

Advances in Production Technology

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