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Byline: Stratis A. Kanarachos In this paper, a new method for the computation of optimal obstacle avoidance steering manoeuvres, within the consideration of the vehicle dynamics, is presented and discussed. The method is based on a reformulation of Pontryagin's Maximum Principle (PMP), in which the manoeuvre time is prescribed and the bang-bang control amplitude is minimised. The resulting control is computationally extremely efficient and has very interesting performance features. Numerical examples and benchmark tests illustrate the performance of the proposed methodology. A sensitivity analysis for different vehicle parameters is performed and finally conclusions and future directions are drawn.

The present study discusses the mechanical behaviour and modelling of a prototype automotive magnetorheological (MR) damper, which presents different viscous damping coefficients in jounce and rebound. The force generated by the MR damper is measured at different velocities and electrical currents, and a modified damper model is proposed to improve fitting of the experimental data. The model is calibrated by means of parameter identification, and for this purpose a new swarm intelligence algorithm is proposed, that we call the contrast-based Fruit Fly Optimisation Algorithm (c-FOA). The performance of c-FOA is compared with that of Genetic Algorithms, Particle Swarm Optimisation, Differential Evolution and Artificial Bee Colony. The comparison is made on the basis of no a-priori knowledge of the damper model parameters range. The results confirm the good performance of c-FOA under parametric range uncertainty. A sensitivity analysis discusses c-FOA’s performance with respect to its tuning parameters. Finally, a ride comfort simulation study quantifies the discrepancies in the results, for different identified damper model sets. The discrepancies underline the importance of accurately describing MR damper nonlinear behaviour, considering that virtual sign-off processes are increasingly gaining momentum in the automotive industry. [ABSTRACT FROM AUTHOR]

Topology optimisation is a process that is becoming increasingly reliable and necessary in the pursuit of highly efficient components comprising of low mass with a high structural performance. These components are typically mass-produced on a large-scale in automotive sectors for instance, where components are usually metallic and pressed. The ability to maximise a component’s structural characteristics has yielded many variations of computational topological solvers over the years. Over time many different methodologies have been used to generate suitable manufacturable solutions. Despite this, a gap between the generation of topology optimisation solutions and the creation of ready-to-manufacture solutions still exists today. This review paper outlines existing methods for computational topology optimisation and addresses any refinement methods used to generate a manufacturable solution, particularly focussing on methodologies used in automotive sheet metal forming. These methods are scrutinised in regards to the level of manual user input needed to create a Computer Aided Design (CAD) model representation of the manufacturable solution. Suggestions are also made to highlight further work to improve these techniques for large-scale industry-standard product development. [ABSTRACT FROM AUTHOR]

• Automation of Post-Processing of Topology Optimisation Results. • Creation of semi-automated customisable optimisation refinement tool. • Standalone topology optimisation post-processor. • Refinement of topology optimised designs for sheet metal manufacturing. Topology optimisation is an increasingly important process used in a variety of industries to improve the designs of manufacturable products. The higher reliance of optimisation software, used for instance in the automotive industry, highlights its importance for designing more efficient and refined mass-produced components. Post-processing of topology optimisation results (e.g. from variable density to manufacturable structures) does however remain a heavily heuristic process where the end-results (and consequently the "efficiency" of the optimised product) can vary significantly as a function of the individual designer/engineer. This "variation" coupled with the often-significant time associated with post-processing makes the use of topology optimisation prohibitive in certain instances. In this paper, a systematic and repeatable three-step approach to automated post-processing of topology optimisation results for sheet metal manufacturing of automotive components will be introduced. The method, which has been implemented into a software tool, is mesh independent and can handle topology optimisation results in binary as well as variable density formats. The software contains three main steps; namely geometry refinement, re-analysis and manufacturability check. The methodology and software utilise a stencil method, for which the principles are described here. The main objective from this is to generate repeatable refined interpretations of optimisation results. In addition to presenting the actual methodology and software, this paper also investigates different parameter variations; such as geometry update sequence, search radii, stencil shape and type and their influence on the generated post-processed result. Definition of algorithm parameters is provided, together with suggested user-defined settings to enable the derivation of consistent refinements of the topology results. [ABSTRACT FROM AUTHOR]