Automatic online algorithm selection for optimization in cyber-physical production systems

Shrinking product lifecycles, progressing market penetration of innovative product technologies, and increasing demand for product individualization lead to frequent adjustments of production processes and thus to an increasing demand for frequent optimization of production processes. Offline solutions are not always available, and even the optimization problem class itself may have changed in terms of the value landscape of the objective function: Parameters may have been added, the locations of optimal values and the values themselves may have changed. This thesis develops an automatic solution to the algorithm selection problem for continuous optimization. Furthermore, based on the evaluation of three different real-world use cases and a review of well-known architectures from the field of automation and cognitive science, a system architecture suitable for use in large data scenarios was developed. The developed architecture has been implemented and evaluated on two real-world problems: A Versatile Production System (VPS) and Injection Molding Optimization (IM). The developed solution for the VPS was able to automatically tune the feasible algorithms and select the most promising candidate, which significantly outperformed the competitors. This was evaluated by applying statistical tests based on the generated test instances using the process data and by performing benchmark experiments. This solution was extended to the area of multi-objective optimization for the IM use case by specifying an appropriate algorithm portfolio and selecting a suitable performance metric to automatically compare the algorithms. This allows the automatic optimization of three largely uncorrelated objectives: cycle time, average volume shrinkage, and maximum warpage of the parts to be produced. The extension to multi-objective handling for IM optimization showed a huge benefit in terms of manual implementation effort, as most of the work could be done by configuration. The implementation effort was reduced to selecting optimizers and hypervolume computation

Parallel Hybrid Trajectory Based Metaheuristics for Real-World Problems

G. Luque, E. Alba, Parallel Hybrid Trajectory Based Metaheuristics for Real-World Problems, In Proceedings of Intelligent Networking and Collaborative Systems, pp. 184-191, 2-4 September, 2015, Taipei, Taiwan, IEEE PressThis paper proposes a novel algorithm combining path relinking with a set of cooperating trajectory based parallel algorithms to yield a new metaheuristic of enhanced search features. Algorithms based on the exploration of the neighborhood of a single solution, like simulated annealing (SA), have offered accurate results for a large number of real-world problems in the past. Because of their trajectory based nature, some advanced models such as the cooperative one are competitive in academic problems, but still show many limitations in addressing large scale instances. In addition, the field of parallel models for trajectory methods has not deeply been studied yet (at least in comparison with parallel population based models). In this work, we propose a new hybrid algorithm which improves cooperative single solution techniques by using path relinking, allowing both to reduce the global execution time and to improve the efficacy of the method. We applied here this new model using a large benchmark of instances of two real-world NP-hard problems: DNA fragment assembly and QAP problems, with competitive results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Advances in Theoretical and Computational Energy Optimization Processes

Industry, construction and transport are the three sectors that traditionally lead to the highest energy requirements. This is why, over the past few years, all the involved stakeholders have widely expressed the necessity to introduce a new approach to the analysis and management of those energy processes characterizing the aforementioned sectors. The objective is to guide production and energy processes to an approach aimed at energy savings and a decrease in environmental impact. Indeed, all of the ecosystems are stressed by obsolete production schemes deriving from an unsustainable paradigm of constant growth and related to the hypothesis of an environment able to absorb and accept all of the anthropogenic changes

Numerical modelling of heat transfer and experimental validation in Powder-Bed Fusion with the Virtual Domain Approximation

Among metal additive manufacturing technologies, powder-bed fusion features very thin layers and rapid solidification rates, leading to long build jobs and a highly localized process. Many efforts are being devoted to accelerate simulation times for practical industrial applications. The new approach suggested here, the virtual domain approximation, is a physics-based rationale for spatial reduction of the domain in the thermal finite-element analysis at the part scale. Computational experiments address, among others, validation against a large physical experiment of 17.5 $\mathrm{[cm^3]}$ of deposited volume in 647 layers. For fast and automatic parameter estimation at such level of complexity, a high-performance computing framework is employed. It couples FEMPAR-AM, a specialized parallel finite-element software, with Dakota, for the parametric exploration. Compared to previous state-of-the-art, this formulation provides higher accuracy at the same computational cost. This sets the path to a fully virtualized model, considering an upwards-moving domain covering the last printed layers

Sequential Parameter Optimization

We provide a comprehensive, effective and very efficient methodology for the design and experimental analysis of algorithms. We rely on modern statistical techniques for tuning and understanding algorithms from an experimental perspective. Therefore, we make use of the sequential parameter optimization (SPO) method that has been successfully applied as a tuning procedure to numerous heuristics for practical and theoretical optimization problems. Two case studies, which illustrate the applicability of SPO to algorithm tuning and model selection, are presented

Numerical optimization of gating systems for light metals sand castings

This thesis proposed an optimization technique for design of gating system parameters of a light metal casting based on the Taguchi method with multiple performance characteristics. Firstly, the casting model with a gating system was designed and exported as International Graphics Exchange Standard (IGES) models by Unigraphic NX4.0. Based on the IGES models of the casting, Finite Element (FE) Models were generated using Hypermesh software. Then, mold filling and solidification processes of the castings were simulated with the MAGMASOFT. Finally, the simulation result can be converted to numerical data according to the 3D coordinates of the FE model by MAGMALink module of MAGMASOFT . The various designs of gating systems for the casting model were generated and the simulated results indicated that gating system parameters significantly affect the quality of the castings. To obtain the optimal process parameters of the gating system, the Taguchi method including the orthogonal array, the signal to noise (S/N) ratio, and the analysis of variance (ANOVA) were used to analyze the effect of various gating designs on cavity filling and casting quality using a weighting method. The gating system parameters were optimized with evaluating criteria including filling velocity, shrinkage porosity and product yield

Optimization of polymer processing: a review (Part II - Molding technologies)

The application of optimization techniques to improve the performance of polymer processing technologies is of great practical consequence, since it may result in significant savings of materials and energy resources, assist recycling schemes and generate products with better properties. The present review aims at identifying and discussing the most important characteristics of polymer processing optimization problems in terms of the nature of the objective function, optimization algorithm, and process modelling approach that is used to evaluate the solutions and the parameters to optimize. Taking into account the research efforts developed so far, it is shown that several optimization methodologies can be applied to polymer processing with good results, without demanding important computational requirements. Furthermore, within the field of artificial intelligence, several approaches can reach significant success. The first part of this review demonstrated the advantages of the optimization approach in polymer processing, discussed some concepts on multi-objective optimization and reported the application of optimization methodologies to single and twin screw extruders, extrusion dies and calibrators. This second part focuses on injection molding, blow molding and thermoforming technologies.This research was funded by NAWA-Narodowa Agencja Wymiany Akademickiej, under grant PPN/ULM/2020/1/00125 and European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No 734205–H2020-MSCA-RISE-2016. The authors also acknowledge the funding by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT (Portuguese Foundation for Science and Technology) under the projects UIDB/05256/2020, UIDP/05256/2020