Automatic Design Method Research Articles

Extracting BIM Information for Lattice Toolpath Planning in Digital Concrete Printing with Developed Dynamo Script: A Case Study

In this paper, a novel method for printing path design in the three-dimensional (3D) concrete printing process (3DCP) has been developed and applied to a large-scale construction process. Despite the large number of research works in digital concrete printing over the last decade, few studies have explored the method of automatic printing path design, specifically designing the printing path by directly extracting building information modeling (BIM) information from the BIM platform in 3DCP. To obtain BIM information and automatically design the printing path, a script package was developed using a single scripting environment called Dynamo, a Revit plug-in. The proposed method was evaluated using the results of simulation and real-time printing. Compared with the conventional method, the simulation results show that the proposed approach can reduce data loss from a 3D BIM model to printing path generation. The real-time printing test implies that the proposed approach is perfectly suitable for integrating BIM with 3DCP.

Choosing the Optimal Power Coils Using Open-Source k-Oriented Design Automation.

Wireless power coils have found important use in implantable medical devices for safe and reliable wireless power transfer. Designing coils for each specific application is a complex process with many interdependent design variables; determining the most optimal design parameters for each pair is challenging and time-consuming. In this paper, we develop an automated design method for planar square-spiral coils that generates the idealized design parameters for maximum power transfer efficiency according to the input design requirements. Computational complexity is first reduced by isolating the inductive coupling coefficient, k, from other design parameters. A simplified but accurate equivalent circuit model is then developed, where skin effect, proximity effect, and parasitic capacitive coupling are iteratively considered. The proposed method is implemented in an open-source software which accounts for the input fabrication limitations and application specific requirements. The accuracy of the estimated power transfer efficiency is validated via finite element method simulation. Using the presented approach, the coil design process is fully automated and can be done in few minutes.

Optimization of Molecular Characteristics via Machine Learning Based on Continuous Representation of Molecules

We demonstrate an automatic materials design method using continuous representation of molecule and its atomic arrangement via a neural network algorithm. This method is applied to optimizing and predicting the HOMO-LUMO gap within the molecules composed of carbon, oxygen, nitrogen, fluorine, and hydrogen. Adopting the Quantum Machine 9 (QM9) dataset as a training dataset for the molecules, we first established a continuous representation of molecules in a latent space, then predicted molecules that have target values of the HOMO-LUMO gap. In the gap maximization calculation, the CF4 with the largest gap value in the QM9 dataset was automatically found despite there is no a priori data for the gap. In the case of a target gap value of 0.10 hartree, we found a new molecule whose gap value is closer to 0.10 hartree than any other molecules in the QM9 dataset.

A Data-Driven Automatic Design Method for Electric Machines Based on Reinforcement Learning and Evolutionary Optimization

The design problems of electric machines are actually treated as a kind of mixed-integer problem, because the machine shapes are defined by some integer variables, such as number of slots, and the other variables, such as the tooth width, which are here called the fundamental and shape variables, respectively. To automatically solve these design problems, this article presents an automatic design method by combining the reinforcement learning and evolutionary optimization. In the proposed method, the decision process is modeled as a tree structure to seek for the fundamental variables, which are determined as a result of the tree search depending on the value functions of the nodes. Then, the shape variables are estimated from the function of the fundamental variables. These functions are constructed based on the design data, to generate which the reinforcement learning and evolutionary optimization are employed. As a result, the proposed method can automatically be adapted to unexperienced design problems through the data generation and function learning. The proposed method is applied to a design problem of a linear induction motor. It is shown that the machine designs with the prescribed performance for given specifications are automatically obtained. Moreover, it is also shown that the acceptable candidate designs can immediately be generated when the given specification is similar to the previously-solved problems by utilizing the design data generated by the past explorations.

An Automatic and Optimal MPA Design Method.

Raw polarimetric images are captured by a focal plane polarimeter which is covered by a micro-polarizer array (MPA). The design of the MPA plays a crucial role in polarimetric imaging. MPAs are predominantly designed according to expert engineering experience and rules of thumb. Typically, only one optimization criterion, maximizing bandwidth, is used to design the MPA. To select a design, an exhaustive search is usually performed on a very limited set of available polarizing patterns, which must be constrained in order to make the search tractable. In contrast, this paper proposes a fully automated and optimal MPA design method (AO-MPA) which generates significantly improved MPAs. Instead of the single criterion of bandwidth, we propose six design principles, and show how they can be utilized to mutually optimize the MPA design by formulating a tri-objective optimization problem with multiple constraints. A much larger set of possible MPA patterns is rapidly and automatically searched by applying advanced multi-objective optimization techniques. We have tested AO-MPA using two groups of experiments, in which AO-MPA is compared against several other leading MPA design methods, and the patterns generated by AO-MPA are compared against state-of-the-art patterns from the literature. The results, obtained using a public benchmark dataset, show that the AO-MPA method is very computationally efficient, and can find all optimal MPA patterns for all array sizes. Moreover, for each size, AO-MPA obtains all optimal layouts simultaneously. AO-MPA generates designs which require fewer polarization orientations, while also yielding better performance in estimating intensity measurements, Stokes vector and the degree of linear polarization. This results in MPAs which are easier to manufacture while also being more robust to noise.

DEVELOPMENT OF A METHOD FOR AUTOMATED DESIGN OF ASSORTMENT INDUSTRIAL COLLECTIONS TAKING INTO ACCOUNT MASS CUSTOMIZATION

DEVELOPMENT OF A METHOD FOR AUTOMATED DESIGN OF ASSORTMENT INDUSTRIAL COLLECTIONS TAKING INTO ACCOUNT MASS CUSTOMIZATION

Image Processing-based Method for Automatic Design of Patient-Specific Cranial Implant for Additive Manufacturing

Decompressive craniectomy (DC) is a surgical procedure where a portion of the skull (flap) is removed to relieve the built-up pressure from the patient’s brain due to swelling of the brain tissue after a traumatic injury to the head. Subsequently, another surgical procedure called cranioplasty is carried out to fix an implant or bone flap in patients who have undergone DC. In this paper, an automatic design methodology for additive manufacturing of a PSCI (patient-specific cranial implant) has been proposed. The input is the DICOM digital data from a CT scan and the output is the STL file geometry of the cranial implant. The proposed method has been tested and validated using real de-identified DICOM data, and the resultant implant was 3D printed and fit to the skull of a cadaver. The key contribution made in this paper is the complete automation of the design of a PSCI based on the skull’s unique geometry using a combination of image-processing and computational geometry techniques. Another important characteristic of the proposed method is that medical professionals need not have any technical expertise in additive manufacturing or part design for generating a PSCI.

Concept for the experimental and numerical study of fluid-structure interaction and gas transport in Precise Electrochemical Machining

The biggest challenge in precise electrochemical machining (pECM) remains the highly time and cost consuming tool and process design. The development of deterministic and automated design methods requires a physics-based understanding of the individual phenomena influencing the removal rate and structural vibration of the workpiece during the machining process and the exact geometry of the final product. A concept study for the quantitative prediction of the fluid-structure interaction (FSI) between the multiphase electrolyte flow and the workpiece electrode is presented. This includes a novel experimental setup for the pECM process, in which the machining gap is upscaled using the principles of dynamic similarity. This enables the application of 3D particle image velocimetry for detailed measurements including a tracking of the workpiece deflections. The experimental results are used for the validation of a computational fluid dynamics method especially developed for the multiphase electrolyte flow. First results of numerical simulations focusing on the gas transport in the turbulent multiphase electrolyte flow are discussed.

FGATR-Net: Automatic Network Architecture Design for Fine-Grained Aircraft Type Recognition in Remote Sensing Images

Fine-grained aircraft type recognition in remote sensing images, aiming to distinguish different types of the same parent category aircraft, is quite a significant task. In recent decades, with the development of deep learning, the solution scheme for this problem has shifted from handcrafted feature design to model architecture design. Although a great progress has been achieved, this paradigm generally needs strong expert knowledge and rich expert experience. It is still an extremely laborious work and the automation level is relatively low. In this paper, inspired by Neural Architecture Search (NAS), we explore a novel differentiable automatic architecture design framework for fine-grained aircraft type recognition in remote sensing images. In our framework, the search process is divided into several phases. Network architecture deepens at each phase while the number of candidate functions gradually decreases. To achieve it, we adopt different pruning strategies. Then, the network architecture is determined through a potentiality judgment after an architecture heating process. This approach can not only search deeper network, but also reduce the computational complexity, especially for relatively large size of remote sensing images. When all differentiable search phases are finished, the searched model called Fine-Grained Aircraft Type Recognition Net (FGATR-Net) is obtained. Compared with previous NAS, ours are more suitable for relatively large and complex remote sensing images. Experiments on Multitype Aircraft Remote Sensing Images (MTARSI) and Aircraft17 validate that FGATR-Net possesses a strong capability of feature extraction and feature representation. Besides, it is also compact enough, i.e., parameter quantity is relatively small. This powerfully indicates the feasibility and effectiveness of the proposed automatic network architecture design method.

Automatic modular design of robot swarms using behavior trees as a control architecture.

We investigate the possibilities, challenges, and limitations that arise from the use of behavior trees in the context of the automatic modular design of collective behaviors in swarm robotics. To do so, we introduce Maple, an automatic design method that combines predefined modules—low-level behaviors and conditions—into a behavior tree that encodes the individual behavior of each robot of the swarm. We present three empirical studies based on two missions: aggregation and Foraging. To explore the strengths and weaknesses of adopting behavior trees as a control architecture, we compare Maple with Chocolate, a previously proposed automatic design method that uses probabilistic finite state machines instead. In the first study, we assess Maple’s ability to produce control software that crosses the reality gap satisfactorily. In the second study, we investigate Maple’s performance as a function of the design budget, that is, the maximum number of simulation runs that the design process is allowed to perform. In the third study, we explore a number of possible variants of Maple that differ in the constraints imposed on the structure of the behavior trees generated. The results of the three studies indicate that, in the context of swarm robotics, behavior trees might be appealing but in many settings do not produce better solutions than finite state machines.

Enhancing the Reliability of MEDA Biochips Using IJTAG and Wear Leveling

A digital microfluidic biochip (DMFB) enables the miniaturization of immunoassays, point-of-care clinical diagnostics, DNA sequencing, and other laboratory procedures in biochemistry. A recent generation of biochips uses a micro-electrode-dot-array (MEDA) architecture, which provides fine-grained control of droplets and seamlessly integrates microelectronics and microfluidics using CMOS technology and a TSMC fabrication process. To ensure that bioassays are carried out on MEDA biochips efficiently, high-level synthesis algorithms have recently been proposed. However, as in the case of conventional DMFBs, microelectrodes are likely to fail when they are heavily utilized, and previous methods fail to consider reliability issues. In this article, we first present a new microelectrode cell (MC) design such that the droplet-sensing operation can be enabled/disabled for individual MCs. Next, “partial update” and “partial sensing” operations are presented based on an IEEE Std. 1687 IJTAG network design. Finally, wear-leveling synthesis method is proposed to ensure uniform utilization of MCs on MEDA. A comprehensive set of simulation results demonstrate the effectiveness of the proposed hardware design and design automation methods.

Fast automatic design method for freeform imaging systems through system construction and correction.

In traditional optical design, a starting point is selected and coefficients optimization is then performed using software. The process requires considerable time and the involvement of a human with design skills and experience. In this Letter, a fast automatic method for freeform imaging systems design is proposed. Using a plane system as the input, a freeform optical system with high image quality can be designed automatically at high speed. The method consists of system construction and system correction, combining the advantages of the direct design method and the methods based on aberration analysis. After system construction generates a system with fundamental optical parameters, system correction is an iterative process that alternates between image plane correction and surfaces correction to improve the image quality to a high level. Two examples required 5 min 56 s and 6 min 10 s to design freeform systems with near-diffraction-limit image quality.

Efficient analysis-suitable T-spline fitting for freeform surface reconstruction and intelligent sampling

Optical scanning instruments require sampling and reconstruction with high accuracy and low computational cost. T-splines have recently been developed that allow significant reductions in the number of control parameters by overcoming some of the topological constraints of B-splines and NURBS. As a subset of T-splines, analysis-suitable T-splines (ASTS) show promise due to the linear independence and partition of unity of their basis functions. In this paper, a computationally efficient ASTS fitting algorithm for freeform surface reconstruction is proposed. This algorithm starts with adaptive construction of an initial analysis-suitable T-mesh according to the distribution of high-curvature feature points. A local refinement and local optimisation algorithm of the analysis-suitable T-mesh is then iteratively performed until a preset accuracy condition is satisfied. Our experimental results show that the proposed ASTS fitting can produce over 50% root-mean-square reconstruction error reduction compared to NURBS fitting, with the same number of control parameters. The computing efficiency of the proposed algorithm is equivalent to or higher than that for simple T-spline fitting. Fast derivative analysis of the ASTS has also been carried out, where two automatic intelligent sampling design methods have been developed, namely element area sampling and element curvature sampling. Up to 50% reconstruction error reduction is observed when compared to uniform and statistically optimised sampling designs. With the novel reconstruction and compatible intelligent sampling design techniques, freeform surface measurement accuracy and efficiency could be effectively improved using coordinate measuring machines.

Modular automatic design of collective behaviors for robots endowed with local communication capabilities.

We investigate the automatic design of communication in swarm robotics through two studies. We first introduce Gianduja an automatic design method that generates collective behaviors for robot swarms in which individuals can locally exchange a message whose semantics is not a priori fixed. It is the automatic design process that, on a per-mission basis, defines the conditions under which the message is sent and the effect that it has on the receiving peers. Then, we extend Gianduja to Gianduja2 and Gianduja 3, which target robots that can exchange multiple distinct messages. Also in this case, the semantics of the messages is automatically defined on a per-mission basis by the design process. Gianduja and its variants are based on Chocolate, which does not provide any support for local communication. In the article, we compare Gianduja and its variants with a standard neuro-evolutionary approach. We consider a total of six different swarm robotics missions. We present results based on simulation and tests performed with 20 e-puck robots. Results show that, typically, Gianduja and its variants are able to associate a meaningful semantics to messages.

Hierarchical Abstraction of Compensator for Reaction Torque Observer Based on Element Description Method

The expansion of the applicable range of robots and machines requires the ability to cooperate with humans and adapt to external environments. It is necessary to use torque information in order to achieve these capabilities. A reaction torque observer is one of the effective methods to obtain torque information because it does not need a torque sensor and it can deal with torque information in the high-frequency domain. However, it needs a correct compensator to reject disturbances to estimate the precise torque. Generally, the disturbance compensator of the reaction torque observer is derived by manual model selection and manual/automatic parameter fitting. This method not only takes time and effort but also does not always obtain an optimal solution because it depends on a predetermined model. In particular, it is well known that nonlinear elements such as friction are difficult to model. To overcome this issue, an automatic design method of a disturbance compensator is proposed in this article. The compensator is abstracted automatically using an element description method. Using the element description method, model design in advance is not needed because the model selection and the parameter fitting are conducted simultaneously. Therefore, the element description method can be applied to systems whose model structure is unknown. In addition, it is possible to input human intentions as initial settings. These features enable designs that make use of human knowledge and the computational power of computers.

Automatic Design of Collective Behaviors for Robots that Can Display and Perceive Colors

Research in swarm robotics has shown that automatic design is an effective approach to realize robot swarms. In automatic design methods, the collective behavior of a swarm is obtained by automatically configuring and fine-tuning the control software of individual robots. In this paper, we present TuttiFrutti: an automatic design method for robot swarms that belongs to AutoMoDe—a family of methods that produce control software by assembling preexisting software modules via optimization. The peculiarity of TuttiFrutti is that it designs control software for e-puck robots that can display and perceive colors using their RGB LEDs and omnidirectional camera. Studies with AutoMoDe have been so far restricted by the limited capabilities of the e-pucks. By enabling the use of colors, we significantly enlarge the variety of collective behaviors they can produce. We assess TuttiFrutti with swarms of e-pucks that perform missions in which they should react to colored light. Results show that TuttiFrutti designs collective behaviors in which the robots identify the colored light displayed in the environment and act accordingly. The control software designed by TuttiFrutti endowed the swarms of e-pucks with the ability to use color-based information for handling events, communicating, and navigating.

Automatic Design of Droplet-Based Microfluidic Ring Networks

Droplet-based microfluidic networks allow to process biological or medical samples by standard unit operations, such as mixing, incubating, sorting, or sensing. However, many of these networks usually perform such operations in a predefined way and, thus, lack in their flexibility. To overcome this problem, ring networks are used, since they allow to execute multiple operations in a row. But while several concepts and also prototypical implementations exist that realize such ring networks, the design process for them is still mainly conducted manually thus far. This is a severe drawback since various aspects, such as the dimensions of the channels, the effects of droplets, the used fluids, the volumetric flow rates inside the channels, etc., have to be considered for this purpose. In this article, we propose design automation methods which address this problem. The proposed solution will automatically generate a proper design as well as correspondingly needed droplet sequences. A case study demonstrates the applicability of the resulting methods and simulations confirms the validity of the proposed approach.

Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification.

Convolutional neural networks (CNNs) have gained remarkable success on many image classification tasks in recent years. However, the performance of CNNs highly relies upon their architectures. For the most state-of-the-art CNNs, their architectures are often manually designed with expertise in both CNNs and the investigated problems. Therefore, it is difficult for users, who have no extended expertise in CNNs, to design optimal CNN architectures for their own image classification problems of interest. In this article, we propose an automatic CNN architecture design method by using genetic algorithms, to effectively address the image classification tasks. The most merit of the proposed algorithm remains in its "automatic" characteristic that users do not need domain knowledge of CNNs when using the proposed algorithm, while they can still obtain a promising CNN architecture for the given images. The proposed algorithm is validated on widely used benchmark image classification datasets, compared to the state-of-the-art peer competitors covering eight manually designed CNNs, seven automatic + manually tuning, and five automatic CNN architecture design algorithms. The experimental results indicate the proposed algorithm outperforms the existing automatic CNN architecture design algorithms in terms of classification accuracy, parameter numbers, and consumed computational resources. The proposed algorithm also shows the very comparable classification accuracy to the best one from manually designed and automatic + manually tuning CNNs, while consuming fewer computational resources.

Reward-based epigenetic learning algorithm for a decentralised multi-agent system

PurposeThis paper aims to propose a novel epigenetic learning (EpiLearn) algorithm, which is designed specifically for a decentralised multi-agent system such as swarm robotics.Design/methodology/approachFirst, this paper begins with overview of swarm robotics and the challenges in designing swarm behaviour automatically. This should indicate the direction of improvements required to enhance an automatic swarm design. Second, the evolutionary learning (EpiLearn) algorithm for a swarm system using an epigenetic layer is formulated and discussed. The algorithm is then tested through various test functions to investigate its performance. Finally, the results are discussed along with possible future research directions.FindingsThrough various test functions, the algorithm can solve non-local and many local minima problems. This article also shows that by using a reward system, the algorithm can handle the deceptive problem which often occurs in dynamic problems. Moreover, utilization of rewards from the environment in the form of a methylation process on the epigenetic layer improves the performance of traditional evolutionary algorithms applied to automatic swarm design. Finally, this article shows that a regeneration process that embeds an epigenetic layer in the inheritance process performs better than a traditional crossover operator in a swarm system.Originality/valueThis paper proposes a novel method for automatic swarm design by taking into account the importance of multi-agent settings and environmental characteristics surrounding the swarm. The novel evolutionary learning (EpiLearn) algorithm using an epigenetic layer gives the swarm the ability to perform co-evolution and co-learning.

Multistage Turbomachinery Design Using the Discrete Adjoint Method Within the Open-Source Software SU2

This paper documents a fully turbulent discrete adjoint method for three-dimensional multistage turbomachinery design. The method is based on a duality preserving algorithm and is implemented in the open-source computational fluid dynamics tool SU2. The SU2 Reynolds-averaged Navier–Stokes solver is first extended to treat three-dimensional steady turbomachinery flow using a conservative formulation of the mixing-plane coupled to nonreflective boundary conditions. The numerical features of the flow solver are automatically inherited by the discrete adjoint solver, ensuring the same convergence rate of the primal solver. The flow solver is then validated against experimental data available for three turbine configurations, namely, a one-and-half axial turbine stage, a transonic radial turbine coupled to a downstream diffuser, and a supersonic mini–organic Rankine cycle radial turbine operating with a fluid made by a heavy molecule. Finally the adjoint-based optimization framework is applied to the concurrent shape optimization of three rows of the axial turbine, demonstrating the advantages deriving from adopting multirow automated design methods in the context of turbomachinery design.