Complex Engineering Design Research Articles

Virtual simulation experiment of the design and manufacture of a beer bottle-defect detection system

BackgroundMachine learning-based beer bottle-defect detection is a complex technology that runs automatically; however, it consumes considerable memory, is expensive, and poses a certain danger when training novice operators. Moreover, some topics are difficult to learn from experimental lectures, such as digital image processing and computer vision. However, virtual simulation experiments have been widely used to good effect within education. A virtual simulation of the design and manufacture of a beer bottle-defect detection system will not only help the students to increase their image-processing knowledge, but also improve their ability to solve complex engineering problems and design complex systems. MethodsThe hardware models for the experiment (camera, light source, conveyor belt, power supply, manipulator, and computer) were built using the 3DS MAX modeling and animation software. The Unreal Engine 4 (UE4) game engine was utilized to build a virtual design room, design the interactive operations, and simulate the system operation. ResultsThe results showed that the virtual-simulation system received much better experimental feedback, which facilitated the design and manufacture of a beer bottle-defect detection system. The specialized functions of the functional modules in the detection system, including a basic experimental operation menu, power switch, image shooting, image processing, and manipulator grasping, allowed students (or virtual designers) to easily build a detection system by retrieving basic models from the model library, and creating the beer-bottle transportation, image shooting, image processing, defect detection, and defective-product removal. The virtual simulation experiment was completed with image processing as the main body. ConclusionsBy mainly focusing on bottle mouthdefect detection, the detection system dedicates more attention to the user and the task. With more detailed tasks available, the virtual system will eventually yield much better results as a training tool for imageprocessing education. In addition, a novel visual perception-thinking pedagogical framework enables better comprehension than the traditional lecture-tutorial style.

Who Is in the Crowd? Characterizing the Capabilities of Prize Competition Competitors

This article leverages a unique field experiment to characterize who in the crowd is willing and able to solve complex engineering design problems for the prospect of a prize. Contrary to popular skepticism, we find that the crowd is highly capable of not only providing quality point solutions but also contributing valuable engineering insight. However, although a diverse population of more than 9000 registrants showed initial interest in the competitions, of the 255 judgeable solutions received, the majority of good solutions came from solvers with prior within-discipline experience. Of those, more of the good solutions came from solvers who perceived the relevant problem as “at the boundary” of their expertise. This suggests that while complex engineering design problems do require some threshold of relevant training and skills, prize competitions still play an important role in attracting solvers who perceive themselves to be adjacent to the problem. Furthermore, the submissions revealed a role for solvers in the crowd in augmenting traditional concept generation and tradespace exploration processes, by leveraging the independence of solvers in the crowd. These findings contribute an empirical basis for resolving how and when the crowd can be used most effectively, as part of an innovation toolkit.

Multiobjective discrete optimization using the TOPSIS and entropy method for protection of pedestrian lower extremity

Design of vehicle frontal structures signifies an important topic of studies on protection of pedestrian lower extremity. Conventional optimization has been largely focused on continuous variable problems without involving complexity of human model. Nevertheless, design of frontal structures is commonly discrete from a manufacturing perspective and the responses of the lower extremity of pedestrian are highly nonlinear in nature. For this reason, this study aimed to develop a multiobjective discrete optimization (MODO) algorithm for design of frontal structures involving a pedestrian model. In the proposed MODO method, the order preference by similarity to ideal solution (TOPSIS) was coupled with the entropy algorithm to develop a multiple attribute decision making (MADM) model for converting multiple conflicting objectives into a unified single cost function. The presented optimization procedure is iterated using the successive orthogonal experiment to deal with a large number of design variables and design levels. In this study, the proposed method was first verified by two benchmark examples; and then was applied to a more sophisticated real-life design case of vehicle frontal structure to better protect pedestrian lower extremity from impacting injury. The finite element model was validated via experimental tests first and the optimized design was then prototyped for the further physical tests. The results showed that the algorithm is able to achieve an optimal design in a fairly efficient manner. The proposed algorithm exhibits considerable potential to solve other complex engineering design problems.

Multi-objective optimization under uncertainty of geothermal reservoirs using experimental design-based proxy models

Geothermal energy has a high potential to contribute to a more sustainable energy system if the associated economic risks can be overcome in the design process. The development planning of deep geothermal reservoirs (over 1000 m depth) relies on computer models to forecast and then optimize system design. Optimization is easy where all the objective's (e.g. NPV) optimization parameters and, most importantly, the geology are considered as known, but this is almost always not the case. Where the complex engineering design (e.g. well placement) meets significant geological uncertainty every development option should be tested using an expensive simulation against the range of geological possibilities. The impracticality of simulating so many models results in a limited exploration of geological uncertainties and development options. Consequently, the risk of improper system design cannot be properly assessed. This paper presents an approach to understand the trade-offs in maximizing heat extraction while minimizing energy usage in re-injection for a new geothermal reservoir development while considering the uncertainty from 18 different geological models. Our approach is computationally feasible because we apply multi-objective particle swarm optimization (MOPSO), to an ensemble of response surface models, built using Gaussian process regression (GPR), for each and every geological scenario. MOPSO explores the trade-off surface for the competing objectives using the mean reservoir responses (covering the geological uncertainty). Our results highlight the impact of geological uncertainty on the optimal well placement and show the need to consider geological uncertainties adequately in optimization. The work demonstrates the shortcomings of using only one geological model of a geothermal reservoir and/or a single objective in optimization. We additionally demonstrate the practicalities of using response surface models in this way for geothermal systems. We anticipate that our work raises awareness for the scope of optimization of geothermal reservoir design under geological uncertainty.

A Comprehensive Study on Fitness Approximation Techniques in Shape Optimization of Aerofoil Forging Preform Tools

In the process of complex engineering designs or optimizations, a large number of physical experiments or numerical simulations are required to evaluate certain performance qualities before a satisfactory result can be obtained. In both cases, constructing an approximate model is often necessary to provide a reliable response as an alternative to experiments or simulations. In this paper, three types of approximation models were developed and applied in a shape design of an aerofoil forging preform tool. Their modeling techniques are presented in detail. An optimal Latin hypercube technique was employed for the design of the experiment and sampling with the expected coverage of parameter space. Finite element (FE) simulations of multistep forging processes were implemented to acquire the objective function values for evaluating the forging performance. By a parametric study, the effects of design variables on objective responses and correlations were investigated for a clear insight into their functional nature. Comprehensive analyses and comparisons between different approximate models have been carried out. Finally, an optimization design of a preform tool was successfully achieved based on a particle swarm (PSO) algorithm combined with the proposed approximate model.

Infracomposer: Policy-driven adaptive and reflective middleware for the cloudification of simulation & optimization workflows

Abstract The simulation and optimization of complex engineering designs in automotive or aerospace involves multiple mathematical tools, long-running workflows and resource-intensive computations on distributed infrastructures. Finding the optimal deployment in terms of task distribution, parallelization, collocation and resource assignment for each execution is a step-wise process involving both human input with domain-specific knowledge about the tools as well as the acquisition of new knowledge based on the actual execution history. In this paper, we present a policy-driven adaptive and reflective middleware that supports smart cloud-based deployment and execution of engineering workflows. This middleware supports deep inspection of the workflow task structure and execution, as well as of the very specific mathematical tools, their executions and used parameters. The reflective capabilities are based on multiple meta-models to reflect workflow structure, deployment, execution and resources. Adaptive deployment is driven by both human input as meta-data annotations as well as adaptation policies that reason over the actual execution history of the workflows. We validate and evaluate this middleware in real-life application cases and scenarios in the domain of aeronautics.

An Integrated Modeling, Simulation and Analysis Framework for Engineering Complex Systems

The discipline of component-based modeling and simulation offers promising gains in reducing cost, time, and the complexity of model development through the (re)use of modular components. Model-driven development suggests 1) the realization of a complex system using a conceptual model; 2) its automatic transformation into an executable form using transformation rules, and; 3) its automatic verification using a formal analysis technique for an accurate assessment of its correctness. Both approaches have numerous complementary benefits in rapid prototyping of complex systems using model reuse. In this paper, we propose a framework grounded in a combination of component-based and model-driven approaches to promote rapid prototyping of complex systems through the effective reuse of the simulation models. Our proposed process allows developers to 1) build or select existing components and compose them to formulate the conceptual models of complex systems; 2) automatically transform the conceptual models for the rapid implementation and simulation, and; 3) automatically verify them as per the requirement specifications. We propose the use of the extended finite-state machine (EFSM) as conceptual modeling formalism, anylogic simulation platform for the implementation, and probabilistic model checking technique using communicating sequential process (CSP) formalism for the verification. Finally, we present a case study of a real-time adaptive cruise control system to demonstrate the functionality of our framework. Our proposed component-based model-driven approach facilitates rapid prototyping and effective meaningful reuse of complex system models, which further accelerates the modeling, simulation, and analysis process of real-time systems and aids in complex engineering designs and implementations.

Knowledge-based service oriented architecture for automotive product development: South East Asia scenario

This paper presents an application of knowledge-based service oriented architecture (SOA) systems for complex engineering design problems. A knowledge-based design approach integrates all aspects of product design, manufacturing tools and a powerful decision-making for reducing the time between product conception and final production. Knowledge-based SOA is able to provide a substantial impact to the product development cycle especially for automotive part manufacturers who their product cycles are quite short. The approach is applied for a number of automotive part manufacturers. The results show an improvement on product cycle time and expert skill management. The specification of the products are improved and obtained at a competitive cost. This will be a great value added to the manufacturers to sustain in their business.

Knowledge-based service oriented architecture for automotive product development: South East Asia scenario

This paper presents an application of knowledge-based service oriented architecture (SOA) systems for complex engineering design problems. A knowledge-based design approach integrates all aspects of product design, manufacturing tools and a powerful decision-making for reducing the time between product conception and final production. Knowledge-based SOA is able to provide a substantial impact to the product development cycle especially for automotive part manufacturers who their product cycles are quite short. The approach is applied for a number of automotive part manufacturers. The results show an improvement on product cycle time and expert skill management. The specification of the products are improved and obtained at a competitive cost. This will be a great value added to the manufacturers to sustain in their business.

A systems engineering framework for the design of bioprocess operator training simulators

Operator training simulators (OTS) are widely used in several industries including chemical processing, oil and gas, medicine, aircraft and nuclear facilities. However, developing a biorefinery OTS is a complex engineering design activity that requires a structured technique. This paper presents a structured methodology that applies design frameworks from other disciplines and a user-centred approach for biorefinery OTS design. These include the definition of end user requirements (operator training needs), and the analysis of these requirements using Quality Function Deployment (QFD). Furthermore, an algorithm for bioprocess optimisation and automatic adjustment of operating parameters is developed for integration into the OTS. This algorithm is based on the Nelder-Mead simplex method for multi-dimensional function minimisation. Identified user requirements were categorized into primary, secondary and tertiary training needs, with increasing levels of detail from primary to tertiary needs. The relationships between identified operator training needs and OTS technical and functional specifications were investigated, and a priority rating assigned to the most important OTS specifications. Identified OTS specifications were evaluated for robustness to ensure that important features were not omitted from the final design.

Optimization of Isogeometric Analysis-Based Topology Optimization Design with Global Stress Constraint in AI

Topology optimization is a powerful technique used in engineering design to optimize the material distribution within a given design domain, resulting in improved structural performance. Isogeometric analysis (IGA) has emerged as a promising approach for topology optimization due to its ability to seamlessly integrate geometric design and analysis. The traditional formulation of IGA-based topology optimization focuses on minimizing compliance, neglecting stress constraints that are crucial for ensuring structural integrity. In propose an optimization framework that combines IGA-based topology optimization and artificial intelligence (AI) to optimize designs with a global stress constraint. The objective is to simultaneously minimize the compliance and control the maximum stress in the structure. The proposed framework leverages the power of AI algorithms, such as deep neural networks, to efficiently search for the optimal material distribution that satisfies the stress constraint.
 The optimization process begins with the generation of an initial design based on the isogeometric representation. The design is then iteratively updated using an AI-driven optimization algorithm that incorporates the global stress constraint. The AI algorithm learns from the stress distribution patterns in the design domain and guides the optimization towards finding an optimal material distribution that minimizes compliance while ensuring the maximum stress does not exceed the predefined limit. To evaluate the performance of the proposed approach, several numerical experiments and comparisons with traditional methods are conducted. The results demonstrate that the combination of IGA-based topology optimization and AI significantly improves the structural performance by effectively controlling stress levels while achieving better compliance values. The optimized designs exhibit enhanced strength and structural efficiency compared to those obtained using conventional methods.
 The integration of AI algorithms with IGA-based topology optimization offers a promising avenue for tackling complex engineering design problems with stress constraints. The proposed framework provides engineers with a powerful tool to design lightweight and structurally robust components. Furthermore, the application of AI in topology optimization can significantly reduce the computational cost and accelerate the design process. Overall, this research contributes to advancing the field of structural optimization by integrating AI-driven approaches into isogeometric analysis-based topology optimization for enhanced design efficiency and performance.

Resource-constrained scheduling of design changes based on simulation of change propagation process in the complex engineering design

When resource constraints are taken into consideration in the deployment of engineering changes, assignments of resources and changes to design entities and selection of change propagation paths make the scheduling problem difficult to solve. Due to complicated dependency relations between engineering entities in the complex engineering design process making it difficult to build an explicit objective function to evaluate different change propagation paths, an integrated simulation and optimization approach is adopted in this research to identify the optimal change scheduling plans for renewable resource-constrained change propagations. First, a digraph model is built to represent the product structure or design process connected with input and output logical relations; second, mathematic formulations of change scheduling problems in two kinds of resource usage schemes are presented; third, a forward-propagate-later-rollback strategy is adopted to develop the resource-constrained change propagation simulation algorithms; fourth, simulation results are used to evaluate individuals in genetic algorithm (GA) during the optimization of propagation likelihood for each involved design dependency. Two case studies are conducted to demonstrate the effectiveness of the models and methods developed in this research. Results for both resource usage schemes are compared, and it can be found that: (1) shorter-duration change propagation plans can be found in the dedicated resource usage scheme than in the flexible scheme; (2) the flexible resource usage scheme can produce a fairer workload allocation between design resources than the dedicated scheme; and (3) the tradeoff between change duration and cost should be determined by design delivery urgency and cost budget in addition to choosing an optimal change propagation plan.

Advanced Multi-Objective Robust Optimization Under Interval Uncertainty Using Kriging Model and Support Vector Machine

There are a large number of real-world engineering design problems that are multi-objective and multiconstrained, having uncertainty in their inputs. Robust optimization is developed to obtain solutions that are optimal and less sensitive to uncertainty. Since most of complex engineering design problems rely on time-consuming simulations, the robust optimization approaches may become computationally intractable. To address this issue, an advanced multi-objective robust optimization approach based on Kriging model and support vector machine (MORO-KS) is proposed in this work. First, the main problem in MORO-KS is iteratively restricted by constraint cuts formed in the subproblem. Second, each objective function is approximated by a Kriging model to predict the response value. Third, a support vector machine (SVM) classifier is constructed to replace all constraint functions classifying design alternatives into two categories: feasible and infeasible. The proposed MORO-KS approach is tested on two numerical examples and the design optimization of a micro-aerial vehicle (MAV) fuselage. Compared with the results obtained from other MORO approaches, the effectiveness and efficiency of the proposed MORO-KS approach are illustrated.

MBSE-based multidisciplinary modeling for designing turbine blade cooling structures

Complex engineering design requires building a considerable number of multidisciplinary models, among which a plethora of “isolated islands and description inconsistencies were created by conventional engineering design approaches. To enhance the global performance measures of a complex engineering system, engineers must bridge these isolated models, eliminate model inconsistencies, and effectively analyze system-model interactions. Systems engineering approaches appear to be essential to multidisciplinary design optimization of complex engineering systems. Here, unified multidisciplinary modeling of complex engineering systems is addressed by using the model-based systems engineering (MBSE) methodology. Following the MBSE principles, this work builds up an integrated hierarchy of geometric, aerodynamics, heat transfer, structure dynamics, and design optimization models, which are used to support the development of turbine blade cooling structures. MBSE approaches break through traditional disciplinary boundaries and adopt a standard modeling language to build coherent models describing various disciplinary phenomena of interest. Implementations of MBSE approaches in conjunction with SysML models are expected to identify and eliminate readily the model inconsistencies, boost integration of multidisciplinary models, and increase engineering design efficiency significantly.

A constrained ITGO heuristic applied to engineering optimization

Nonlinear optimization is an active line of research, given the wide range of scientific fields that benefit from its development. In the last years, the meta-heuristics proved to be one of the most effective methods to tackle difficult optimization problems, providing an alternative in cases where exact methods would be unfeasible. In this work, we present a method based on the Iterative Topographical Global Optimization meta-heuristic, which we call C-ITGO, incorporating specific mechanisms to solve nonlinearly constrained optimization problems. We use the method developed in this work to optimize eight complex engineering design problems and compare the results obtained here against those obtained with several other methods found in the literature. In the tests performed, C-ITGO outperforms all competing methods, achieving state of the art results for the problems considered.

An output mapping variable fidelity metamodeling approach based on nested Latin hypercube design for complex engineering design optimization

Engineering design is usually a daunting optimization task which often involving time-consuming, even computation-prohibitive process. Variable fidelity metamodeling has been developed as an efficient approach to alleviate this issue, owing to its capacity of achieving accurate metamodels within limited sample size. An output mapping modeling method is proposed in this paper as an alternative variable fidelity metamodeling method in which the low fidelity outputs is directly mapped to the high fidelity outputs through least square support vector regression. Furthermore, a nested Latin hypercube design method is developed for the output mapping modeling, in which a simple sampling design is treated as a building block or DOE seed to be translated and propagated through the design space, resulting in a final sampling configuration. Effectiveness of the proposed method are demonstrated by several numerical functions and two engineering design problem, in which different sample sizes, predictive accuracies and robustness are considered.

Designing human-centered robots: The role of constructive failure

Framing of failure as constructive can help students to engage in authentic practices of iterative design and to leverage failure in future experiences. In contrast to classroom norms that privilege “right” answers and perfect final submissions, classrooms that position failure and iteration as norms prepare students for the realities of complex problem solving. This qualitative case study examines the role of failure in one middle school human-centered robotics curriculum, a challenging collaborative problem-solving experience for these students. In this context, we conceptualize constructive failure as short-term and supported experiences of failure that build a base for longer-term success solving complex engineering design problems. Within this unit focused on the design and development of robots that serve a social function, scaffolds were provided to orient students to the benefits of failing fast and often. Across several episodes, we show how the instructor of this course introduced and supported failure, and we trace the trajectory of two groups as they moved from frustration to articulating failure as necessary in a comparative case study. This analysis highlights constructive failure as an asset for learning through iterative design and for the development of creative problem-solving skills. We conjecture that formative experiences of failure can support the process of problem-based learning (PBL) – particularly PBL centered on design thinking. This case study works to inform how future K-12 classrooms and curricula might be structured to support and recognize failure as a positive norm and a valued part of collaborative problem solving and design.

A space mapping method based on Gaussian process model for variable fidelity metamodeling

Computational simulation models with different fidelities are usually available in the design of engineering products for obtaining the quantity of interest (QOI). To integrate and fully exploit variable fidelity information, a space mapping based variable-fidelity metamodeling (VFM) approach is developed in this work. Firstly, a Gaussian process (GP) model is constructed for the low-fidelity (LF) model. Secondly, a variable-fidelity metamodel is constructed by taking the predicted information from this GP model as a prior-knowledge of the QOI and directly mapped into the outputs space of the high-fidelity (HF) model. This space mapping process is performed by constructing another GP model. A mathematic example is first adopted for illustrating how the proposed approach works under different sample sizes and sample noises. Then, the proposed approach is applied to two real-life cases, modeling of the maximum stress for the structure of a Small Waterplane Area Twin Hull (SWATH) catamaran and predicting weld geometry in fiber laser keyhole welding, to illustrate its ability in support of complex engineering design.

A modified PSO algorithm with dynamic parameters for solving complex engineering design problem

ABSTRACTThis paper proposed a new approach of particle swarm optimization (PSO). The proposed modified PSO algorithm is equipped with some specially designed mechanisms of adaptively updating algorithm parameters to preserve the diversity of the swarm and to keep the balance between exploration and exploitation searches. All these mechanisms help the algorithm to avoid the premature convergence and to strengthen its robustness. Experiments are conducted on different complicated, unimodal and multimodal test functions, as well as a typical engineering inverse problem, the TEAM Workshop problem 22. The numerical results illustrate that the proposed PSO shows better performance as compared to other well developed evolutionary algorithms.

Parametric Study on the Element Size Effect for Optimal Topologies

Topology optimization is complex engineering design tool. It needs intensive mathematical, mechanical and computing tools to perform the required design. During its hundred years of history it has become clear that the non-unique solution property of the method is affected by the material parameters (Poisson ratio) and the ways of the discretization. The aim of the paper is to investigate the influence of parameter changes to optimal design property in tasks with great number of degrees of freedom. The parametric study includes influence of material parameter (Poisson ratio) as well as the size of the ground elements which are commonly applied during the discretization. Increasing the size of the ground elements while the total number of the finite elements is constant, the computational time is significantly reduced. Therefore the study on changing accuracy versus ground element resolution may be important factor in choosing ground element size. In addition to it the effective properties of arrangements of the strong and weak materials (black and white elements) in a checkerboard fashion are also investigated. The Michell-type problem is investigated by the minimization of the weight of the structure subjected to a compliance constraint.