Automated Design Optimization Research Articles

Automatic Modeling and Optimization Design Platform for Highway Tunnels with Application to Stress and Displacement Prediction of Surrounding Rocks during Tunnel Excavation

Automatic Modeling and Optimization Design Platform for Highway Tunnels with Application to Stress and Displacement Prediction of Surrounding Rocks during Tunnel Excavation

Automatic Optimal Design Method for Minimum Total Resistance Hull Based on Enhanced FFD Method

Hull optimization plays a crucial role in enhancing ship performance and efficiency. This study aimed to improve ship performance, particularly by reducing total resistance, through automated optimization methods. To this end, an integrated, fully automated optimization program was developed based on Python, incorporating Enhanced Free-Form Deformation (FFD) technology, scripted CFD numerical evaluation, and the Particle Swarm Optimization (PSO) algorithm. This program allowed precise control of hull form, improving efficiency while reducing costs. The KCS hull, known for its excellent resistance performance, was chosen as the optimization target, with the goal of minimizing total resistance by adjusting the bulbous bow line plan. The research results indicated that the optimized scheme exhibited lower resistance characteristics compared to the original design while satisfying design constraints. This study not only provides a new optimization strategy for ship design but also lays a foundation for future hull optimization research.

Active learning assisted piezoelectric materials synthesis on the basis of composite decision-making

The synthesis and development of novel materials for soft electronics, health monitoring, etc., have become a research hotspot. Traditional laboratory synthesis is significantly time-and-resource-consuming. Machine learning therefore becomes an ideal approach for expediting experimental process, constructing a virtual and automated closed-loop material synthesis and evaluation approach. In this work, we combined piezoelectric materials’ synthesis with machine learning to achieve automatic design optimization. 300 samples with different material recipes were used to train the initial active learning model. Thereafter, more samples were fabricated based on the recommended feasible recipes for each learning loop and then proceeded to the next round of learning. Through 10 active learning loops, 105 piezoelectric samples were stagewise fabricated. Moreover, a reverse design model based on Bayesian optimization is demonstrated, and Spearman's rank correlation coefficient and p values revealed the rules for the synthesis of piezoelectric materials. Finally, according to the setup model, we fabricate optimized piezoelectric materials, and demonstrate the application in cycling monitoring. We anticipate this work establishes an essential approach to accelerate the development of new materials.

Automatic optimization design of laser triangulation ranging sensors using an improved genetic algorithm

Optical system parameter design is of great importance to ensure the accuracy of asymmetry systems such as laser triangulation ranging systems. However, the system parameter determination often depends on the experience and manual attempts of designers, which is not only time-consuming but also inevitable to introduce human errors. Therefore, in this paper an automatic optimization design method based on nonlinear programming genetic algorithm with elitism strategy (E-NPGA) is proposed, to accurately and fast determine the optimal system parameters of laser triangulation ranging systems assisting in improving the measurement accuracy. Firstly, an optimization model of system parameters is developed under the Scheimpflug rule establishing the constraints for various measurement resolutions and ranges. An image size constraint is constructed for the first time to improve and evaluate the parameter optimization. Secondly, the E-NPGA is proposed with nonlinear optimization and elitism strategy, which can determine the optimal system parameters in 15 iterations avoiding local extremum. In design examples, using the E-NPGA determined system parameters ZEMAX simulation and experimental results of the parameters depended image spot size show a slight relative difference below 0.6%. Moreover, the experiment results demonstrate the sensor system designed by using the E-NPGA enables a distance measurement with submicron absolute error and 10−4 relative uncertainty. The automatic optimization method proposed in this paper is compared with the conventional GA method and PSO method, and it is validated that the convergence accuracy of the proposed method is much higher than the conventional ones.

P‐90: Automatic Design Optimization Platform for FMM Mask Dummy Pattern Simulation Based on AMOLED Display Screen

In this paper, the automatic design optimization platform for FMM dummy pattern simulation based on AMOLED display screen has the advantages of short time, high precision, simple process and wide range of design combination. Specifically, the project simulation design time is reduced by half, the error rate in the simulation design process is reduced from 10% to 1%, the design combination range is increased by more than 10 times, and the FMM mask compensation accuracy is increased by 10%. Automated design optimization platform is helpful to improve product yield and save cost.

ARTIFICIAL INTELLIGENCE FOR SYSTEMS ENGINEERING COMPLEXITY: A REVIEW ON THE USE OF AI AND MACHINE LEARNING ALGORITHMS

This review examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in addressing the complexities of systems engineering. It highlights how AI and ML are revolutionizing system design, integration, and lifecycle management by enabling automated design optimization, predictive maintenance, and efficient configuration management. These technologies allow for the analysis of large datasets to predict system failures and optimize performance, thereby enhancing the reliability and sustainability of engineering systems. Despite the promising applications, the integration of AI into systems engineering presents challenges, including technical hurdles, ethical considerations, and the need for comprehensive education and training. The paper emphasizes the importance of interdisciplinary approaches and the continuous evolution of educational programs to equip engineers with the skills to leverage AI effectively. Concluding thoughts underscore AI's potential to redefine systems engineering, advocating for a balanced approach that addresses both the opportunities and challenges presented by AI advancements.
 Keywords: Artificial Intelligence, Machine Learning, Systems Engineering, Automated Design, Predictive Maintenance, Configuration Management, Education and Training, Technology Integration.

410 Improving Patient Outcomes through Design of Biodegradable Implants for Long Bone Fractures

OBJECTIVES/GOALS: Current long bone fracture standard of care uses inert metal intramedullary nails (IMN), 10x stiffer than femur cortex. Consequent “stress-shielding” bone loss sees >5% of patients needing revision surgery. To improve nonunion healing, we develop automated design optimization methods for biodegradable Mg alloy IMNs to control local reloading. METHODS/STUDY POPULATION: Finite element analysis (FEA) is performed on 3D bone-IMN representations to establish this study’s baseline strain states for existing inert IMN geometries within QCT-informed femoral models under simulated biomechanical loading. FEA with Mg alloy properties for same IMN designs simulate transient IMN material loss through discrete time-step models with experimental in vivo Mg corrosion rates and strain-based bone density evolution using remodeling algorithms from literature. Transient stability and strength metrics, fracture zone stress profiles under gradual reloading and manufacturing constraints are formulated through gradient-based sensitivity analysis into a topology optimization framework (TOF) incorporating a reaction-diffusion degradation model to generate IMN topologies. RESULTS/ANTICIPATED RESULTS: TOF designs for Mg alloy IMNs with transient allowable strength constraints, using safety factors to prevent IMN failure, demonstrate higher compliance than standard inert IMNs with mechanical properties closer to native cortical bone. The biodegradation model within the TOF, informed by corrosion behavior from bone-IMN FEA study, predicts how potential design evolutions affect transient strain states of the system. Thus, local fracture region stress states are controlled by the algorithm optimizing for desirable transient stiffness profiles based on a minimum variance objective of fracture zone stress compared to a target bone stress profile. Optimized IMNs with porous, high surface area features achieve 50% decrease in IMN stiffness over 6 months recovery time and complete in vivo degradation in 24 months. DISCUSSION/SIGNIFICANCE: Our TOF reduces “stress-shielding” effects via design for controlled IMN biodegradation to gradually increase fracture zone loading, stimulating remodeling and reducing current risk of post-operative fracture and surgical removal in ~15k cases/yr. in the U.S. In vitro mechanical and in vivo clinical testing is required to validate design results.

Front Cover: Automatic optimal design of field plate for silicon on insulator lateral double‐diffused metal oxide semiconductor using simulated annealing algorithm

Front Cover: Automatic optimal design of field plate for silicon on insulator lateral double‐diffused metal oxide semiconductor using simulated annealing algorithm

Automatic optimal design of field plate for silicon on insulator lateral double‐diffused metal oxide semiconductor using simulated annealing algorithm

AbstractField plate (FP) technology has been widely used due to its simple structure and process compatibility. Through introducing the interface charge to suppress the electric field peak at the junction region, the breakdown performance can be improved. However, designing an appropriate FP for semiconductor power devices is challenging and time‐consuming. In this paper, the authors propose a fully automated FP optimal design method based on simulated annealing algorithm (SA) for silicon on insulator lateral double‐diffused metal oxide semiconductor. By using an automatic iterative process to obtain the minimum value of the objective function, the parameters related to the FP can be effectively provided. Numerical results show that when the breakdown occurs at the N+N or PN junction, the breakdown voltage can be optimized by an average of 18.6% and 45.5%, respectively. Moreover, compared with the existing methods, the proposed approach is highly efficient with a runtime that does not exceed 12 s. The authors believe that this method can greatly accelerate the power device design process.

Deep Learning for Polarization Optical System Automated Design

Aiming at the problem that traditional design methods make it difficult to control the polarization aberration distribution of optical systems quickly and accurately, this study proposes an automatic optimization design method for polarization optical systems based on deep learning. The unsupervised training model based on ray tracing and polarized ray tracing was constructed by learning the reference lens structural feature data from the optical lens library, and the generalization ability of the deep neural network model was improved to achieve the automatic optimization design of the polarized optical system. The design results show that the optical system structure optimized by the network model is close to the reference lens in the full field of view and the full spectrum and that the optical system structure can be designed for different focal length requirements. The success rate of 1 million groups of initial structures designed is better than 96.403%, and the polarization effect of the optical system is effectively controlled. The proposed deep learning approach to optical design provides a new solution for future complex optical systems and also provides an effective way to improve the design accuracy of special optical systems such as polarization optical systems.

Automatic Optimal Design Method for Circuit Sizing Based on CNN Surrogate Model Assisted Differential Evolution Algorithm

Automatic Optimal Design Method for Circuit Sizing Based on CNN Surrogate Model Assisted Differential Evolution Algorithm

Automatic Scatterplot Design Optimization for Clustering Identification.

Scatterplots are among the most widely used visualization techniques. Compelling scatterplot visualizations improve understanding of data by leveraging visual perception to boost awareness when performing specific visual analytic tasks. Design choices in scatterplots, such as graphical encodings or data aspects, can directly impact decision-making quality for low-level tasks like clustering. Hence, constructing frameworks that consider both the perceptions of the visual encodings and the task being performed enables optimizing visualizations to maximize efficacy. In this article, we propose an automatic tool to optimize the design factors of scatterplots to reveal the most salient cluster structure. Our approach leverages the merge tree data structure to identify the clusters and optimize the choice of subsampling algorithm, sampling rate, marker size, and marker opacity used to generate a scatterplot image. We validate our approach with user and case studies that show it efficiently provides high-quality scatterplot designs from a large parameter space.

Closed‐Loop Optimization of Soft Sensor Morphology Using 3D Printing of Electrically Conductive Hydrogel

Soft sensing technologies provide a novel alternative for state estimation in wearables and robotic systems. They allow one to capture intrinsic state parameters in a highly conformable manner. However, due to the nonlinearities in the materials that make up a soft sensor, it is difficult to develop accurate models of these systems. Consequently, design of these soft sensors is largely user defined or based on trial and error. Since these sensors conform and take the shape of the sensing body, these issues are further exacerbated when they are installed. Herein, a framework for the automated design optimization of soft sensors using closed‐loop 3D printing of a recyclable hydrogel‐based sensing material is presented. The framework allows direct printing of the sensor on the sensing body using visual feedback, evaluates the sensor performance, and iteratively improves the sensor design. Following preliminary investigations into the material and morphology parameters, this is demonstrated through the optimization of a sensorized glove which can be matched to specific tasks and individual hand shapes. The glove's sensors are tuned to respond only to particular hand poses, including distinguishing between two similar tennis racket grip techniques.

Automatic optimal design of passive vibration control devices for buildings using two-level evolutionary algorithm

Automatic optimal design of passive vibration control devices for buildings using two-level evolutionary algorithm

A Bim-Based Automatic Design Optimization Method for Modular Steel Structures: Rectangular Modules as an Example

During the promotion of the modular steel structure in the architecture, engineering, and construction (AEC) industry, building information modeling (BIM) is leveraged to integrate the design process into the whole construction sequence. The absence of standards and interactive, tech-friendly tools for project participants limits the general implementation of the BIM-based design process. The present study proposes an automatic design optimization method based on the BIM platform for modular steel structures. The method consists of digital modeling sequences that contain data exchange between different software applications and the program of structural design optimization. A prototype workflow of the method is explained and assessed in a case study to indicate its reliability and practicability. The proposed design coheres with common design rules and enhances the utilization rate of column structure by 40–50% with minimal redundancy compared to initial designs. The proposed method is also discussed through interviews with and surveys of engineers working in the AEC industry in terms of its potential adoption in actual projects. The discussion shows that this method can reduce the time consumption of the model creation and optimization of modular steel structures effectively. Special knowledge of the relevant software is no longer a hindrance for engineers.

Optimum design of acoustic stealth shape of underwater vehicle model with conning tower

We present an optimization algorithm for achieving optimal acoustic stealth performance during designing an underwater vehicle shape in the free field using COMSOL-MATLAB integrated software. A component superposition method based on phase interference is adopted to simplify and decompose a nonaxisymmetric complex underwater vehicle model into two main parts: the hull and the conning tower. The shape of underwater vehicle hull is described mathematically with a sequence of undetermined coefficients for optimization. The basic mathematical principle of the proposed method is Kirchhoff approximation, also called planar element method (PEM). Additionally, some examples and experimental results show that this method can realize the automatic optimization design of acoustic stealth shapes for underwater vehicle model in a given frequency band and acoustic incident angle range. The underwater vehicle design has a smooth appearance with low target strength (TS) or angle detection rate for most detection angles and frequency bands after optimized.

Study on the automatic optimization design of the cross-sectional layout of an umbilical with layers based on the GA-GLM

Marine umbilical provides the remote control function for offshore oil and gas exploitation, which is usually integrated by different functional components that vary widely in their mechanical properties. Under the action of external load, unreasonable cross-sectional layouts may lead to large cross-sectional deformation and contact pressure between components thus affecting the service life of an umbilical. Therefore, how to design the cross-sectional layout is a very necessary research work in order to improve service life of the umbilical to withstand various loads during operation. At present, the research on the cross-sectional layout of an umbilical is not sufficient, and there is no standardized design method in the design specification. Considering the mechanical and geometrical properties as the quantitative design objectives, the mathematical optimization model of the cross-sectional layout of an umbilical is proposed. Meanwhile, the layering strategy is introduced because of the processing and manufacturing factors.Then, the genetic algorithm (GA) with good global search performance and the generalized Lagrange multiplier (GLM) with fast convergence speed are combined to solve the above model. Finally, taking an umbilical as an example, the optimal cross-sectional layout is obtained and the effectiveness of the algorithm is verified by the numerical simulation. The results show that the optimization method proposed in this paper can quickly obtain the optimal design cross-sectional layout, and supply a specific way for the fast design of an umbilical, which has the certain engineering application value.

Structural optics design of the zoom system based on particle swarm optimization

Selection of initial structural parameters is an essential step for the zoom system design. In this paper, a new method based on Gaussian optics and Particle Swarm Optimization (PSO) algorithm is proposed to design initial structural parameters of the zoom system, and an optical automatic optimization design is carried out based on the initial structural parameters. Firstly, displacement parameters of the zoom group and the compensation group of the zoom system are derived based on Gaussian optics. Secondly, constraints of the zoom system are established using the PSO algorithm, and a fitness function is designed. Then, the initial structure parameters of the zoom system are optimized. Finally, uniform and monotonic movement of the cam curve is achieved in an example of a 6 × zoom system. The results indicate that our proposed methods improve the efficiency and accuracy of the zoom system design, which has guiding significance for the design of multi-group moving zoom system with high zoom ratio and other engineering applications.

Design optimization of a packaged thermoelectric generator for electrically active implants

Thermoelectric generators (TEGs) are being considered as an alternative power supply for implantable medical devices. TEGs provide electrical power from temperature differences within the human body. However, the performance of implanted TEGs strongly depends on their geometry and implantation environment. Numerical simulations are performed to investigate the impact of various design variables on output power. This paper proposes a hermetically sealed housing, which supports optimum TEG performance when embedded in the human body. The model includes a TEG, a housing made from polyetheretherketone and metal plates for improved thermal tissue contact. The model also covers the surrounding tissue with its physiological thermal properties. The impact of housing and metal plate dimensions as well as the implantation depth is analysed via parameter studies. We consider patient and body region-specific tissue layer thicknesses through the evaluation of various layer thickness variations. Furthermore, we derive minimum dimensions of a packaged TEG, which is capable of providing 100μW power by automated design optimization.

The building plan automatic design optimization based on CFD numerical intelligent simulation

To apply the thermal comfort to the optimization of building layout, a variable design method based on Kmeans clustering is proposed. The evaluation was based on numerical simulation, genetic algorithm and universal thermal climate index (UTCI) to implement the building layout optimization on Matlab. Finally, the building layouts with centralized type, decentralized type and edge flow type water configuration were optimized, respectively. The results show that after the optimization, a reduction of 0.1∼0.6°C of UTCI was observed. We concluded that under moderate heat stress, the increase in wind velocity is the key to thermal comfort improvement.