Intelligent conceptual mould layout design system (ICMLDS) : innovation report

Family Mould Cavity Runner Layout Design (FMCRLD) is the most demanding and critical task in the early Conceptual Mould Layout Design (CMLD) phase. Traditional experience-dependent manual FCMRLD workflow results in long design lead time, non-optimum designs and costs of errors. However, no previous research, existing commercial software packages or patented technologies can support FMCRLD automation and optimisation. The nature of FMCRLD is non-repetitive and generative. The complexity of FMCRLD optimisation involves solving a complex two-level combinatorial layout design optimisation problem. This research first developed the Intelligent Conceptual Mould Layout Design System (ICMLDS) prototype based on the innovative nature-inspired evolutionary FCMRLD approach for FMCRLD automation and optimisation using Genetic Algorithm (GA) and Shape Grammar (SG). The ICMLDS prototype has been proven to be a powerful intelligent design tool as well as an interactive design-training tool that can encourage and accelerate mould designers’ design alternative exploration, exploitation and optimisation for better design in less time. This previously unavailable capability enables the supporting company not only to innovate the existing traditional mould making business but also to explore new business opportunities in the high-value low-volume market (such as telecommunication, consumer electronic and medical devices) of high precision injection moulding parts. On the other hand, the innovation of this research also provides a deeper insight into the art of evolutionary design and expands research opportunities in the evolutionary design approach into a wide variety of new application areas including hot runner layout design, ejector layout design, cooling layout design and architectural space layout design

Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

Modified genetic algorithm as a new approach for solving the problem of 3d packaging

In this paper, we proposed one of the options for developing a new evolutionary heuristic approach for solving the three-dimensional packing problem called BPP (Bin packing problem), as applied to the variation of this problem with a single container and a set of boxes of various dimensions, called the SKP (Single knapsack problem), and The comparison of 11 basic evolutionary heuristic approaches to solving the problem of three-dimensional packing of BPP (Bin packing problem) variations SKP (Single knapsack problem) with the developed new evolutionary heuristic approach to solving BPP using modi cited genetic algorithm (MGA). By performing correlation and statistical analysis using 10 randomly created sets of input data for solving BPP, the effectiveness of MGAs was proved in comparison with 11 basic evolutionary algorithms for solving BPP. Thus, it was confirmed that MGA and similar algorithms can be effectively used to solve such logistic NP-difficult problems

Development and Integration of Geometric and Optimization Algorithms for Packing and Layout Design

The research work presented in this dissertation focuses on the development and application of optimization and geometric algorithms to packing and layout optimization problems. As part of this research work, a compact packing algorithm, a physically-based shape morphing algorithm, and a general purpose constrained multi-objective optimization algorithm are proposed. The compact packing algorithm is designed to pack three-dimensional free-form objects with full rotational freedom inside an arbitrary enclosure such that the packing efficiency is maximized. The proposed compact packing algorithm can handle objects with holes or cavities and its performance does not degrade significantly with the increase in the complexity of the enclosure or the objects. It outputs the location and orientation of all the objects, the packing sequence, and the packed configuration at the end of the packing operation. An improved layout algorithm that works with arbitrary enclosure geometry is also proposed. Different layout algorithms for the SAE and ISO luggage are proposed that exploit the unique characteristics of the problem under consideration. Several heuristics to improve the performance of the packing algorithm are also proposed. The proposed compact packing algorithm is benchmarked on a wide variety of synthetic and hypothetical problems and is shown to outperform other similar approaches. The physically-based shape morphing algorithm proposed in this dissertation is specifically designed for packing and layout applications, and thus it augments the compact packing algorithm. The proposed shape morphing algorithm is based on a modified mass-spring system which is used to model the morphable object. The shape morphing algorithm mimics a quasi-physical process similar to the inflation/deflation of a balloon filled with air. The morphing algorithm starts with an initial manifold geometry and morphs it to obtain a desired volume such that the obtained geometry does not interfere with the objects surrounding it. Several modifications to the original mass-spring system and to the underlying physics that governs it are proposed to significantly speed-up the shape morphing process. Since the geometry of a morphable object continuously changes during the morphing process, most collision detection algorithms that assume the colliding objects to be rigid cannot be used efficiently. And therefore, a general-purpose surface collision detection algorithm is also proposed that works with deformable objects and does not require any preprocessing. Many industrial design problems such as packing and layout optimization are computationally expensive, and a faster optimization algorithm can reduce the number of iterations (function evaluations) required to find the satisfycing solutions. A new multi-objective optimization algorithm namely Archive-based Micro Genetic Algorithm (AMGA2) is presented in this dissertation. Improved formulation for various operators used by the AMGA2 such as diversity preservation techniques, genetic variation operators, and the selection mechanism are also proposed. The AMGA2 also borrows several concepts from mathematical sciences to improve its performance and benefits from the existing literature in evolutionary optimization. A comprehensive benchmarking and comparison of AMGA2 with other state-of-the-art optimization algorithms on a wide variety of mathematical problems gleaned from literature demonstrates the superior performance of AMGA2. Thus, the research work presented in this dissertation makes contributions to the development and application of optimization and geometric algorithms