Optimising towards robust metal forming processes

Product improvement and cost saving have always been important goals in the metal forming\ud industry. Numerical optimisation can help to achieve these goals, but optimisation with a deterministic\ud approach will often lead to critical process settings, such that the slightest variation in e.g. material behaviour\ud will result in violation of constraints. To avoid a high scrap ratio, process robustness must be considered in the\ud optimisation model. Optimising for robustness includes Robust Manufacturing (RM) techniques, Optimisation\ud Under Uncertainty (OUU) methods and Finite Element (FEM) simulations of the processes. In this paper,\ud we review RM and OUU. Subsequently, the combination of Statistical Process Control (SPC), robust and\ud reliability based optimisation methods, and FEM-based process simulation implemented in AutoForm-Sigma\ud is presented. An automotive deep drawing application demonstrates the potential of strategies that optimise\ud towards robust metal forming processes

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

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

Energy-Efficient Machining Process Analysis and Optimisation Based on BS EN24T Alloy Steel as Case Studies

Computer Numerical Controlled (CNC) machining, which is one of the most widely-deployed manufacturing techniques, is an energy-intensive process. It is important to develop energy-efficient CNC machining strategies to achieve the overall goal of sustainable manufacturing. Due to the complexity of machining parameters, it is challenging to develop effective modelling and optimisation approaches to implement energy-efficient CNC machining. To address the challenge, in this paper, BS EN24T alloy (AISI 4340) has been used as a case study to conduct energy-efficient analysis and optimisation. Using a combination of experimentation and Taguchi analysis, the impact of the key machining parameters of CNC machining processes on energy consumption has been investigated in detail. A multi-objective optimisation model has been formulated, and a novel improved multi-swarm Fruit Fly optimisation algorithm (iMFOA) has been developed to identify optimal solutions. Case studies and algorithm benchmarking have been conducted to validate the effectiveness of the optimisation approach. The relationships between energy consumption and key machining parameters (e.g., cutting speed, feed per tooth, engagement depth) have been analysed to support process planners in implementing energy-saving measures efficiently. The optimisation approach developed is effective in fine-tuning key parameters for enhancing energy efficiency while meeting other technical requirements of production

The optimisation of a strategic business process

The optimisation of a Tendering Process for Warship Refit Contracts is presented. The Pre Contract Award process (PCA) involves all the activities needed to successfully win a Refit Contract, e.g. estimating, planning, tendering and negotiation. Process activities and information flows have been modelled using Integrated computer aided manufacturing DEFinition methodology (IDEF0) and a Design Structure Matrix (DSM) with optimisation performed via a Genetic Algorithm (DSM-GA) search technique [1]. The aim of the DSM-GA is to provide the user with an enhanced sequence of performing process activities. A new process was extracted from the optimised solution, showing an improved sequence with reduced iteration and planned activity concurrency based on carefully considered information requirements. This is of practical benefit to enhance understanding and to provide a guide to implementation. The approach suggests an enhanced sequence of process activities, based on information requirements, and can lead to improved business practice. This Paper discusses the potential benefits and limitations of this approach in a practical setting

The optimisation of the estimating and tendering process in warship refit - a case study

The optimisation of a tendering process for warship refit contracts is presented. The tendering process, also known as the pre-contract award process (PCA), involves all the activities needed to be successfully awarded a refit contract. Process activities and information flows have been modelled using Integrated Definition Language IDEF0 and a Dependency Structure Matrix (DSM) with optimisation performed via a Genetic Algorithm (DSM-GA) search technique. By utilising this approach the process activities were re-sequenced in such an order that the number and size of rework cycles were reduced. The result being a 57% reduction in a criterion indicating 're-work' cycles

Integrated system to perform surrogate based aerodynamic optimisation for high-lift airfoil

This work deals with the aerodynamics optimisation of a generic two-dimensional three element high-lift configuration. Although the high-lift system is applied only during take-off and landing in the low speed phase of the flight the cost efficiency of the airplane is strongly influenced by it [1]. The ultimate goal of an aircraft high lift system design team is to define the simplest configuration which, for prescribed constraints, will meet the take-off, climb, and landing requirements usually expressed in terms of maximum L/D and/or maximum CL. The ability of the calculation method to accurately predict changes in objective function value when gaps, overlaps and element deflections are varied is therefore critical. Despite advances in computer capacity, the enormous computational cost of running complex engineering simulations makes it impractical to rely exclusively on simulation for the purpose of design optimisation. To cut down the cost, surrogate models, also known as metamodels, are constructed from and then used in place of the actual simulation models. This work outlines the development of integrated systems to perform aerodynamics multi-objective optimisation for a three-element airfoil test case in high lift configuration, making use of surrogate models available in MACROS Generic Tools, which has been integrated in our design tool. Different metamodeling techniques have been compared based on multiple performance criteria. With MACROS is possible performing either optimisation of the model built with predefined training sample (GSO) or Iterative Surrogate-Based Optimization (SBO). In this first case the model is build independent from the optimisation and then use it as a black box in the optimisation process. In the second case is needed to provide the possibility to call CFD code from the optimisation process, and there is no need to build any model, it is being built internally during the optimisation process. Both approaches have been applied. A detailed analysis of the integrated design system, the methods as well as th

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