Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 2: modelling and optimization

This is the second part of a study investigating a model-based transient calibration process for diesel engines. The first part addressed the data requirements and data processing required for empirical transient emission and torque models. The current work focuses on modelling and optimization. The unexpected result of this investigation is that when trained on transient data, simple regression models perform better than more powerful methods such as neural networks or localized regression. This result has been attributed to extrapolation over data that have estimated rather than measured transient air-handling parameters. The challenges of detecting and preventing extrapolation using statistical methods that work well with steady-state data have been explained. The concept of constraining the distribution of statistical leverage relative to the distribution of the starting solution to prevent extrapolation during the optimization process has been proposed and demonstrated. Separate from the issue of extrapolation is preventing the search from being quasi-static. Second-order linear dynamic constraint models have been proposed to prevent the search from returning solutions that are feasible if each point were run at steady state, but which are unrealistic in a transient sense. Dynamic constraint models translate commanded parameters to actually achieved parameters that then feed into the transient emission and torque models. Combined model inaccuracies have been used to adjust the optimized solutions. To frame the optimization problem within reasonable dimensionality, the coefficients of commanded surfaces that approximate engine tables are adjusted during search iterations, each of which involves simulating the entire transient cycle. The resulting strategy, different from the corresponding manual calibration strategy and resulting in lower emissions and efficiency, is intended to improve rather than replace the manual calibration process.

Anisotropic Mechanical Properties of ABS Parts Fabricated by Fused Deposition Modelling

In recent years, layered manufacturing (LM) processes have begun to progress from rapid prototyping techniques towards rapid manufacturing methods, where the objective is now to produce finished components for potential end use in a product (Caulfield et al., 2007). LM is especially promising for the fabrication of specific need, low volume products such as replacement parts for larger systems. This trend accentuates the need for a thorough understanding of the associated mechanical properties and the resulting behavior of parts produced by layered methods. Not only must the base material be durable, but the mechanical properties of the layered components must be sufficient to meet in-service loading and operational requirements, and be reasonably comparable to parts produced by more traditional manufacturing techniques. This chapter presents the details of a study completed to quantitatively analyze the potential of fused deposition modelling to fully evolve into a rapid manufacturing tool. The project objective is to develop an understanding of the dependence of the mechanical properties of FDM parts on raster orientation and to assess whether these parts are capable of maintaining their integrity while under service loading. The study examines the effect of fiber orientation, i.e. the direction of the polymer beads relative to the loading direction of the part, on a variety of important mechanical properties of ABS components fabricated by fused deposition modeling. Tensile, compressive, flexural, impact, and fatigue strength properties of FDM specimens are examined, evaluated, and placed in context in comparison with the properties of injection molded ABS parts.