Deterministic and robust optimisation strategies for metal forming proceesses

Product improvement and cost reduction have always been important goals in the metal forming industry. The rise of\ud Finite Element simulations for metal forming processes has contributed to these goals in a major way. More recently, coupling\ud FEM simulations to mathematical optimisation techniques has shown the potential to make a further contribution to product\ud improvement and cost reduction.\ud Mathematical optimisation consists of the modelling and solving of optimisation problems. Although both the\ud modelling and the solving are essential for successfully optimising metal forming problems, much of the research published until\ud now has focussed on the solving part, i.e. the development of a specific optimisation algorithm and its application to a specific\ud optimisation problem for a specific metal forming process.\ud In this paper, we propose a generally applicable optimisation strategy which makes use of FEM simulations of metal\ud forming processes. It consists of a structured methodology for modelling optimisation problems related to metal forming.\ud Subsequently, screening is applied to reduce the size of the optimisation problem by selecting only the most important design\ud variables. Screening is also utilised to select the best level of discrete variables, which are in such a way removed from the\ud optimisation problem. Finally, the reduced optimisation problem is solved by an efficient optimisation algorithm. The strategy is\ud generally applicable in a sense that it is not constrained to a certain type of metal forming problems, products or processes. Also\ud any FEM code may be included in the strategy.\ud However, the above strategy is deterministic, which implies that the robustness of the optimum solution is not taken\ud into account. Robustness is a major item in the metal forming industry, hence we extended the deterministic optimisation\ud strategy in order to be able to include noise variables (e.g. material variation) during optimisation. This yielded a robust\ud optimisation strategy that enables to optimise to a robust solution of the problem, which contributes significantly to the industrial\ud demand to design robust metal forming processes. Just as the deterministic optimisation strategy, it consists of a modelling,\ud screening and solving stage.\ud The deterministic and robust optimisation strategies are compared to each other by application to an analytical test\ud function. This application emphasises the need to take robustness into account during optimisation, especially in case of\ud constrained optimisation. Finally, both the deterministic and the robust optimisation strategies are demonstrated by application to\ud an industrial hydroforming example

FREE SEARCH – A NOVEL HEURISTIC METHOD

Key words to describe the work: Evolutionary computing, Artificial Intelligence, Free Search. Key Results: Inspired from the nature new population-based algorithm applied to numerical optimisation. How does the work advance the state-of-the-art?: Novel approach to stochastic processes. Reflects on an improvement of the optimisation effectiveness and robustness. Benefits optimisation and nature understanding Motivation (problems addressed): An improvement of optimisation process in terms of better performance and robustness, which can support wide range disciplines, we consider as a challenge for research

A Robust Optimisation Strategy for Metal Forming Processes

Robustness, reliability, optimisation and Finite Element simulations are of major importance to improve product\ud quality and reduce costs in the metal forming industry. In this paper, we propose a robust optimisation strategy for metal\ud forming processes. The importance of including robustness during optimisation is demonstrated by applying the robust\ud optimisation strategy to an analytical test function and an industrial hydroforming process, and comparing it to deterministic\ud optimisation methods. Applying the robust optimisation strategy significantly reduces the scrap rate for both the analytical\ud test function and the hydroforming proces

The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

open access articleWe present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the Holm–Bonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them

Direct Step & Shoot: a New Module for Prostate Cancer IMRT

Aims & Objectives The aim of this treatment planning study is to compare the techniques of 3D conformal radiotherapy (3DCRT) and IMRT to determine the feasible advantages for prostate cancer patientsof using a new direct step & shoot (DSS) IMRT module. For the optimisation of the IMRT, Nucletron offers as a part of the optimising process their IM-optimisation software or their new module DSS. The earlier IM-optimisation software searches first for the ideal fluence for each beam, and this is then followed by the segmentation. The new DSS module integrates the segmentation into the optimisation process. Materials & Methods Between March 2006 and November 2006, four patients with a mean age of 71 years were enrolled for primary EBRT for localised prostate cancer. Three of these patients received antiandrogen therapy either before or during radiotherapy. All four patients had 3D CT treatment planning with a slice thickness of 5 mm and with immobilisation in a vacuum mattress (BlueBAG BodyFIX, Medical Intelligence). As an initial step, it was planned (using Oncentra MasterPlan) to deliver 60 Gy to the planning target volume (PTV), calculated using data for a Siemens Primus linear accelerator (15 MV photons, with multileaf collimator leaf width of 1cm at the isocentre). The preselected gantry angles were 25, 90, 120, 240, 270 and 335 degrees. The rectal volume and urinary bladder were delineated as organs at risk (OARs). Additional structures were also contoured in order to help (we term them ‘Help Contours’) avoid hot spots in normal tissues surrounding the PTV to a distance of 1cm. The dose-volume objectives were defined by two schemes. After optimisation the plans were re-normalised to the average of PTV, giving 30 fractions with a fractional dose of 2 Gy. The 3DCRT plan used identical gantry angles with the beams weighted by experience. Results Both IMRT optimisation schemes reduced the doses received by the OARs when compared to the 3DCRT plan. Using the Nucletron IM-optimisation software the first weighting scheme of the objectives resulted in satisfactory dose-volume histograms (DVHs) for the OARs, and an obviously 'softened' DVH for the PTV (when compared to the 3DCRT plan). The DSS optimisation produced a steeper DVH for the PTV, but worse results for the OARs when compared to the IM-optimisation. Scheme 2 improved the DVHs for the OARs using the DSS process, to about the same level as the IM-optimisation with scheme 1, the PTV DVH staying nearly unchanged. The IM-optimisation produced the worst DVH for the PTV of the five different plans we considered. In Figure 1 the DVHs are shown of a characteristic 3DCRT plan, the IM plan (Scheme 1) and the DSS plan (Scheme 2). Table 1 presents the mean values, averaged over the four patients, for the PTV and for the two OARs. Conclusions In every case the DSS optimisation resulted in a steeper DVH for the PTV than found using the IM process. The DVHs for the OARs are worse with scheme 1 but improve to about the same level with scheme 2. The patients benefit from IMRT by reduced doses to the OARs, keeping a very steep DVH for the PTV with the DSS optimisation. The user should note that weighting schemes based on the experience with IM-optimisation are not retained for the new DSS optimiation without control

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Ant colony optimisation-based radiation pattern manipulation algorithm for electronically steerable array radiator antennas

A new algorithm for manipulating the radiation pattern of Electronically Steerable Array Radiator Antennas is proposed. A continuous implementation of the Ant Colony Optimisation (ACO) technique calculates the optimal impedance values of reactances loading different parasitic radiators placed in a circle around a centre antenna. By proposing a method to obtain a suitable sampling frequency of the radiation pattern for use in the optimisation algorithm and by transforming the reactance search space into the search space of associated phases, special care was taken to create a fast and reliable implementation, resulting in an approach that is suitable for real-time implementation. The authors compare their approach to analytical techniques and optimisation algorithms for calculating these reactances. Results show that the method is able to calculate near-optimal solutions for gain optimisation and side lobe reduction