A Data Mining Methodology for Vehicle Crashworthiness Design

This study develops a systematic design methodology based on data mining theory for decision-making in the development of crashworthy vehicles. The new data mining methodology allows the exploration of a large crash simulation dataset to discover the underlying relationships among vehicle crash responses and design variables at multiple levels and to derive design rules based on the whole-vehicle safety requirements to make decisions about component-level and subcomponent-level design. The method can resolve a major issue with existing design approaches related to vehicle crashworthiness: that is, limited abilities to explore information from large datasets, which may hamper decision-making in the design processes. At the component level, two structural design approaches were implemented for detailed component design with the data mining method: namely, a dimension-based approach and a node-based approach to handle structures with regular and irregular shapes, respectively. These two approaches were used to design a thin-walled vehicular structure, the S-shaped beam, against crash loading. A large number of design alternatives were created, and their responses under loading were evaluated by finite element simulations. The design variables and computed responses formed a large design dataset. This dataset was then mined to build a decision tree. Based on the decision tree, the interrelationships among the design parameters were revealed, and design rules were generated to produce a set of good designs. After the data mining, the critical design parameters were identified and the design space was reduced, which can simplify the design process. To partially replace the expensive finite element simulations, a surrogate model was used to model the relationships between design variables and response. Four machine learning algorithms, which can be used for surrogate model development, were compared. Based on the results, Gaussian process regression was determined to be the most suitable technique in the present scenario, and an optimization process was developed to tune the algorithm’s hyperparameters, which govern the model structure and training process. To account for engineering uncertainty in the data mining method, a new decision tree for uncertain data was proposed based on the joint probability in uncertain spaces, and it was implemented to again design the S-beam structure. The findings show that the new decision tree can produce effective decision-making rules for engineering design under uncertainty. To evaluate the new approaches developed in this work, a comprehensive case study was conducted by designing a vehicle system against the frontal crash. A publicly available vehicle model was simplified and validated. Using the newly developed approaches, new component designs in this vehicle were generated and integrated back into the vehicle model so their crash behavior could be simulated. Based on the simulation results, one can conclude that the designs with the new method can outperform the original design in terms of measures of mass, intrusion and peak acceleration. Therefore, the performance of the new design methodology has been confirmed. The current study demonstrates that the new data mining method can be used in vehicle crashworthiness design, and it has the potential to be applied to other complex engineering systems with a large amount of design data

Multi-objective analysis of machine learning algorithms using model-based optimization techniques

My dissertation deals with the research areas optimization and machine learning. However, both of them are too extensive to be covered by a single person in a single work, and that is not the goal of my work either. Therefore, my dissertation focuses on interactions between these fields. On the one hand, most machine learning algorithms rely on optimization techniques. First, the training of a learner often implies an optimization. This is demonstrated by the SVM, where the weighted sum of the margin size and the sum of margin violations has to be optimized. Many other learners internally optimize either a least-squares or a maximum likelihood problem. Second, the performance of most machine learning algorithms depends on a set of hyper-parameters and an optimization has to be conducted in order to find the best performing model. Unfortunately, there is no globally accepted optimization algorithm for hyper-parameter tuning problems, and in practice naive algorithms like random or grid search are frequently used. On the other hand, some optimization algorithms rely on machine learning models. They are called model-based optimization algorithms and are mostly used to solve expensive optimization problems. During the optimization, the model is iteratively refined and exploited. One of the most challenging tasks here is the choice of the model class. It has to be applicable to the particular parameter space of the OP and to be well suited for modeling the function’s landscape. In this work, I gave special attention to the multi-objective case. In contrast to the single-objective case, where a single best solution is likely to exist, all possible trade-offs between the objectives have to be considered. Hence, not only a single best, but a set of best solutions exists, one for each trade-off. Although approaches for solving multi-objective problems differ from the corresponding approaches for single-objective problems in some parts, other parts can remain unchanged. This is shown for model-based multi-objective optimization algorithms. The last third of this work addresses the field of offline algorithm selection. In online algorithm selection the best algorithm for a problem is selected while solving it. Contrary, offline algorithm selection guesses the best algorithm a-priori. Again, the work focuses on the multi-objective case: An algorithm has to be selected with respect to multiple, conflicting objectives. As with all offline techniques, this selection rule hast to be trained on a set of available training data sets and can only be applied to new data sets that are similar enough to those in the training set

Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017

Dieser Tagungsband enthält die Beiträge des 27. Workshops Computational Intelligence. Die Schwerpunkte sind Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen und Benchmark-Problemen