How to enlarge the output displacement is a key issue in the research field of microgrippers. It is difficult to further enlarge the output displacement for the traditional displacement transmission mechanism (DTM). In this research, a two-stage amplification cylinder-driven DTM based on the compliant mechanisms is designed to realize the displacement output expansion. The opening and closing of the clamping jaws is driven by the air cylinder to enlarge the output displacement of the microgripper. According to the analysis of statics model of the mechanism, the relationship between the output displacement of the microgripper and the driving pressure of the cylinder is established. The magnification of the microgripper is obtained using a dynamic model. Moreover, based on the finite element analysis, the mechanical structure parameters are optimized. The microgripper was fabricated by utilizing wire electro discharge machining (WEDM) technique, and then a series of experiments were carried out to obtain the relationship between the displacement and the driving pressure. It is found that the maximum output displacement measured is 1190.4 μm under the pressure of 0–0.6 MPa, corresponding to the magnification of 47.63. Compared with the results of finite element analysis and theoretical calculation, the test results have a discrepancy of 2.39% and 6.62%, respectively. The microgripper has successfully grasped a variety of micro-parts with irregular shapes, and parallel grasping can be achieved, demonstrating the potential application of this design in the field of micromanipulation.

Human beings are physically and cognitively variable, leading to a wide array of potential system use cases. To design safe and effective systems for highly heterogeneous populations, engineers must cater to this variability to minimize the chance of error and system failure. This can be a challenge because of the increasing costs associated with providing additional product variety. Most guidance for navigating these trade-offs is intended for late-stage design, when significant resources have been expended, thus risking expensive redesign or exclusion of users when new human concerns become apparent. Despite the critical need to evaluate accommodation-cost trade-offs in early stages of design, there is currently a lack of structured guidance. In this work, an approach to function modeling is proposed that allows the simultaneous consideration of human and machine functionality. This modeling approach facilitates the allocation of system functions to humans and machines to be used as an accessible baseline for concept development. Further, a multi-objective optimization model was developed to allocate functions with metrics for accommodation and cost. The model was demonstrated in a design case study. About 16 senior mechanical engineering students were recruited and tasked with performing the allocation task manually. The results were compared to the output of the optimization model. Results indicated that participants were unable to produce concepts with the same accommodation-cost efficiency as the optimization model. Further, the optimization model successfully produced a wide range of potential product concepts, demonstrating its utility as a decision-aid.

Automated inverse design methods are critical to the development of metamaterial systems that exhibit special user-demanded properties. While machine learning approaches represent an emerging paradigm in the design of metamaterial structures, the ability to retrieve inverse designs on-demand remains lacking. Such an ability can be useful in accelerating optimization-based inverse design processes. This paper develops an inverse design framework that provides this capability through the novel usage of invertible neural networks (INNs). We exploit an INN architecture that can be trained to perform forward prediction over a set of high-fidelity samples and automatically learns the reverse mapping with guaranteed invertibility. We apply this INN for modeling the frequency response of periodic and aperiodic phononic structures, with the performance demonstrated on vibration suppression of drill pipes. Training and testing samples are generated by employing a transfer matrix method. The INN models provide competitive forward and inverse prediction performance compared to typical deep neural networks (DNNs). These INN models are used to retrieve approximate inverse designs for a queried non-resonant frequency range; the inverse designs are then used to initialize a constrained gradient-based optimization process to find a more accurate inverse design that also minimizes mass. The INN-initialized optimizations are found to be generally superior in terms of the queried property and mass compared to randomly initialized and inverse DNN-initialized optimizations. Particle swarm optimization with INN-derived initial points is then found to provide even better solutions, especially for the higher-dimensional aperiodic structures.

Surrogate models have several uses in engineering design, including speeding up design optimization, noise reduction, test measurement interpolation, gradient estimation, portability, and protection of intellectual property. Traditionally, surrogate models require that all training data conform to the same parametrization (e.g., design variables), limiting design freedom and prohibiting the reuse of historical data. In response, this article proposes graph-based surrogate models (GSMs) for trusses. The GSM can accurately predict displacement fields from static loads given the structure’s geometry as input, enabling training across multiple parametrizations. GSMs build upon recent advancements in geometric deep learning, which have led to the ability to learn on undirected graphs: a natural representation for trusses. To further promote flexible surrogate models, this article explores transfer learning within the context of engineering design and demonstrates positive knowledge transfer across data sets of different topologies, complexities, loads, and applications, resulting in more flexible and data-efficient surrogate models for trusses.

One of the critical components in industrial gas turbines (IGT) is the turbine blade. The design of turbine blades needs to consider multiple aspects like aerodynamic efficiency, durability, safety, and manufacturing, which make the design process sequential and iterative. The sequential nature of these iterations forces a long design cycle time, ranging from several months to years. Due to the reactionary nature of these iterations, little effort has been made to accumulate data in a manner that allows for deep exploration and understanding of the total design space. This is exemplified in the process of designing the individual components of the IGT, resulting in a potential unrealized efficiency. To overcome the aforementioned challenges, we demonstrate a probabilistic inverse design machine learning (PMI) framework to carry out an explicit inverse design. PMI calculates the design explicitly without excessive costly iteration and overcomes the challenges associated with ill-posed inverse problems. In this study, the framework will be demonstrated on inverse aerodynamic design of three-dimensional turbine blades.

Scientific and engineering problems often require the use of artificial intelligence to aid understanding and the search for promising designs. While Gaussian processes (GP) stand out as easy-to-use and interpretable learners, they have difficulties in accommodating big data sets, categorical inputs, and multiple responses, which has become a common challenge for a growing number of data-driven design applications. In this paper, we propose a GP model that utilizes latent variables and functions obtained through variational inference to address the aforementioned challenges simultaneously. The method is built upon the latent-variable Gaussian process (LVGP) model where categorical factors are mapped into a continuous latent space to enable GP modeling of mixed-variable data sets. By extending variational inference to LVGP models, the large training data set is replaced by a small set of inducing points to address the scalability issue. Output response vectors are represented by a linear combination of independent latent functions, forming a flexible kernel structure to handle multiple responses that might have distinct behaviors. Comparative studies demonstrate that the proposed method scales well for large data sets with over 10 4 data points, while outperforming state-of-the-art machine learning methods without requiring much hyperparameter tuning. In addition, an interpretable latent space is obtained to draw insights into the effect of categorical factors, such as those associated with “building blocks” of architectures and element choices in metamaterial and materials design. Our approach is demonstrated for machine learning of ternary oxide materials and topology optimization of a multiscale compliant mechanism with aperiodic microstructures and multiple materials.

Ever since its introduction over five decades ago, geometric solid modeling has been crucial for engineering design purposes and is used in engineering software packages such as computer-aided design ( cad ), computer-aided manufacturing, computer-aided engineering, etc. Solid models produced by cad software have been used to transfer geometric information from designers to manufacturers. Since the emergence of additive manufacturing (AM), a CAD file can also be directly uploaded to a three-dimensional (3D) printer and used for production. AM techniques allow manufacturing of complex geometric objects such as bio-inspired structures and lattice structures. These structures are shapes inspired by nature and periodical geometric shapes consisting of struts interconnecting in nodes. Both structures have unique properties such as significantly reduced weight. However, geometric modeling of such structures has significant challenges due to the inability of current techniques to handle their geometric complexity. This calls for a novel modeling method that would allow engineers to design complex geometric objects. This survey paper reviews geometric modeling methods of complex structures to support bio-inspired design created for AM which includes discussing reasoning behind bio-inspired design, limitations of current modeling approaches applied to bio-inspired structures, challenges encountered with geometric modeling, and opportunities that these challenges reveal. Based on the review, a need for a novel geometric modeling method for bio-inspired geometries produced by AM is identified. A framework for such a bio-inspired geometric modeling method is proposed as a part of this work.

Current legged landers are typical truss structures acting as one type of fundamental equipment for the close-range extraterrestrial exploration missions. Unluckily, the development process applying the current design framework always consumes a long time span searching for the final design, accompanying masses of trial and error with inefficiency and diseconomy. Its kernel reason is that the stages from concept to scheme employ the paradigm of structural-analysis-oriented redesign and untimely embed physical prototype experiments in masses of iterative design cycles. Furthermore, the current framework cannot support the creative development of future legged landers with novel functions and mechanisms. Herein, we present a complete computable design framework for speeding up the development of both current and future legged landers, highlighted by new mathematical models and new principles of forward-design paradigm and multi-mode synergistic design paradigm. It applies the numerical prototype simulation instead of the physical prototype experiment in most iterative processes. This work will facilitate the extraterrestrial exploration missions using the current legged landers (truss-based) and the future legged lander (robot-based).

A multifidelity surrogate (MFS) model is a data fusion method for the enhanced prediction of less intensively sampled primary variables of interest (i.e., high-fidelity (HF) samples) with the assistance of intensively sampled auxiliary variables (i.e., low-fidelity (LF) samples). In this article, an MFS model based on the gradient-enhanced radial basis function, termed gradient-enhanced multifidelity surrogate based on the radial basis function (GEMFS-RBF), is proposed to establish a mapping relationship between HF and LF samples. To identify the scaling factor and the undetermined coefficients in GEMFS-RBF, an expanded correlation matrix is constructed by considering the correlations between the acquired samples, the correlations between the gradients, and the correlations between the samples and their corresponding gradients. To evaluate the prediction accuracy of the GEMFS-RBF model, it is compared with the co-Kriging model, multifidelity surrogate based on the radial basis function (MFS-RBF) model, and two single-fidelity surrogate models. The influences of key factors (i.e., the correlations between the HF and LF functions, the subordinations between the sample sets) and the effect of the cost ratio on the performance of GEMFS-RBF are also investigated. It is observed that GEMFS-RBF presents a more acceptable accuracy rate and is less sensitive to the aforementioned factors than the other benchmark models in most cases in this article, which illustrates the practicability and robustness of the proposed GEMFS-RBF model.

Emulation plays an important role in engineering design. However, most emulators such as Gaussian processes (GPs) are exclusively developed for interpolation/regression and their performance significantly deteriorates in extrapolation. To address this shortcoming, we introduce evolutionary Gaussian processes (EGPs) that aim to increase the extrapolation capabilities of GPs. An EGP differs from a GP in that its training involves automatic discovery of some free-form symbolic bases that explain the data reasonably well. In our approach, this automatic discovery is achieved via evolutionary programming (EP) which is integrated with GP modeling via maximum likelihood estimation, bootstrap sampling, and singular value decomposition. As we demonstrate via examples that include a host of analytical functions as well as an engineering problem on materials modeling, EGP can improve the performance of ordinary GPs in terms of not only extrapolation, but also interpolation/regression and numerical stability.

This paper presents a novel miniature contact-aided compliant mechanism (CCM) that includes flexure hinges and contact-aided structures. This continuum mechanism comprises a nickel–titanium alloy (Nitinol) tube with CCM cut via laser micromachining and actuated using wires bending from −80 deg to +80 deg in four directions. The proposed CCM has the following merits: perfect capacity for deflection around the centroid, a self-backbone, and improved torsional as well as tensile strengths. Further, it is pre-assembled. First, kinematic and static models are used to predict the bending behavior of the mechanism. Thereafter, the maximum strain is evaluated using finite element analysis (FEA) then compared with the static models. Finally, the performances of the mechanism are characterized by experiments. The results validate the proposed models and demonstrate that the torsional and tensile strengths of the proposed CCM increased by more than 100% and 30%, respectively, compared with those of conventional non-CCMs with a similar fatigue life. Moreover, with the integrated forceps and probe, the proposed mechanism can achieve object transfer and square trajectory scanning of the targeted location. These experimental results demonstrate the potential clinical value of the proposed mechanism and provide important insights into the design of long and flexible instruments for endoscopic surgery.

Computationally studying team discourse can provide valuable, real-time insights into the state of design teams and design cognition during problem-solving. The particular experimental design, adopted from previous work by the authors, places one of the design team conditions under the guidance of a human process manager. In that work, teams under this process management outperformed the unmanaged teams in terms of their design performance. This opens the opportunity to not only model design discourse during problem-solving, but more critically, to explore process manager interventions and their impact on design cognition. Utilizing this experimental framework, a topic model is trained on the discourse of human designers of both managed and unmanaged teams collaboratively solving a conceptual engineering design task. Results show that the two team conditions significantly differ in a number of the extracted topics and, in particular, those topics that most pertain to the manager interventions. A dynamic look during the design process reveals that the largest differences between the managed and unmanaged teams occur during the latter half of problem-solving. Furthermore, a before and after analysis of the topic-motivated interventions reveals that the process manager interventions significantly shift the topic mixture of the team members’ discourse immediately after intervening. Taken together, these results from this work not only corroborate the effect of the process manager interventions on design team discourse and cognition but provide promise for the computational detection and facilitation of design interventions based on real-time, discourse data.

Industry 4.0 promises better control of the overall product development process; however, there is a lack of computational frameworks that can inject human factors engineering principles early in the design. This shortage is particularly crucial for prototyping human-centered products where the stakes are high. Thus, a smooth Industry 4.0 transformation requires keeping ergonomics in the loop, specifically to address the needs in the digitized prototyping process. In this paper, we explore a computational prototyping approach that focuses on various fidelity levels and different human–product interaction levels when conducting ergonomics assessments. Three computational prototyping strategies were explored, including (1) a digital sketchpad-based tool, (2) computer-aided design and digital human modeling-based approach, and (3) a combination of computer-aided design, digital human modeling, and surrogate modeling. These strategies are applied to six case studies to perform various ergonomics assessments (reach, vision, and lower-back). The results from this study show that the designers need to consider the tradeoffs between the accuracy of ergonomic outcomes and resource availability when determining the fidelity level of prototypes. Understanding the intricacies between the fidelity level, type of ergonomic assessment, and human–product interaction level helps designers in getting one step closer to digitizing human-centered prototyping and meeting Industry 4.0 objectives.

Gaussian process (GP) regression or kriging has been extensively applied in the engineering literature for the purposes of building a cheap-to-evaluate surrogate, within the contexts of multi-fidelity modeling, model calibration, and design optimization. With the ongoing automation of manufacturing and industrial practices as a part of Industry 4.0, there has been a greater need for advancing GP regression techniques to handle challenges such as high input dimensionality, data paucity or big data problems, these consist primarily of proposing efficient design of experiments, optimal data acquisition strategies, sparsifying covariance kernels, and other mathematical tricks. In this work, our attention is focused on the challenges of efficiently training a GP model, which, to the authors opinion, has attracted very little attention and is to-date poorly addressed. The performance of widely used training approaches such as maximum likelihood estimation and Markov Chain Monte Carlo (MCMC) sampling can deteriorate significantly in high-dimensional and big data problems and can lead to cost deficient implementations of critical importance to many industrial applications. Here, we compare an Adaptive Sequential Monte Carlo (ASMC) sampling algorithm to classic MCMC sampling strategies and we demonstrate the effectiveness of our implementation on several mathematical problems and challenging industry applications of varying complexity. The computational time savings of the ASMC approach manifest in large-scale problems helping us to push the boundaries of applicability and scalability of GPs for model calibration in various domains of the industry, including but not limited to design automation, design engineering, smart manufacturing, predictive maintenance, and supply chain manufacturing.

Planetary gear set (PGS) has been one of the best components to constitute a transmission configuration, including the dedicated hybrid transmission (DHT). Using different synthesis approaches, the DHT configurations can be obtained through algorithms. However, different synthesis results correspond to different connection states of the planetary gear system. There are a certain number of results that violate the motion requirements of the mechanical principal need to be detected and removed. Therefore, this paper presents a novel modeling method to systematically remove the interference structures, with graph theory in structural synthesis. Based on the original graph theory, this paper proposes an equivalent replacement modeling method to convert the motor graph model into a brake-like graph model. Based on the conversion, avoid the appearance of the hanging points in the graph model. By applying the proposed approach, a DHT structure proves the feasibility of the method. The proposed detection approach can systematically detect all the PGS-based transmission with multi-PGSs, multi-shifting elements, and multi-power sources.

For customized design of a hybrid manipulator for a specific application, selection of an appropriate configuration is always a challenge. To assist in this foremost decision in data-driven synthesis, a novel approach is proposed for modular formation of quick configurations. Majorly, a unified methodology is presented for the development of respective kinematic models and differential relations for their performance analyses. This unified modular approach utilizes modular primitives to define planar hybrid configurations. Three types of primitives are introduced as modular components, and the pattern study is detailed. Modeling results from the proposed approach are also compared with normally used partial differentiation with respect to the computational efforts, streamlined modular implementations, and applicability in optimal design approaches. The comparison highlights how the column-wise approach is appropriate for modular methodology. The strategy will help a designer as a tool for analyzing several configurations. Two realistic case studies are demonstrated in this article for application of the methodology in the medical robotics field.

Even though many efforts have been devoted to effective strategies to build accurate surrogate models, surrogate model uncertainty is inevitable due to a limited number of available simulation samples. Therefore, the surrogate model uncertainty, one of the epistemic uncertainties in reliability-based design optimization (RBDO), has to be considered during the design process to prevent unexpected failure of a system that stems from an inaccurate surrogate model. However, there have been limited attempts to obtain a reliable optimum taking into account the surrogate model uncertainty due to its complexity and computational burden. Thus, this paper proposes a confidence-based design optimization (CBDO) under surrogate model uncertainty to find a conservative optimum despite an insufficient number of simulation samples. To compensate the surrogate model uncertainty in reliability analysis, the confidence of reliability is brought to describe the uncertainty of reliability. The proposed method employs the Gaussian process modeling to explicitly quantify the uncertainty of a surrogate model. Thus, metamodel-based importance sampling and expansion optimal linear estimation are exploited to reduce the computational burden on confidence estimation. In addition, stochastic sensitivity analysis of the confidence is developed for CBDO, which is formulated to find a conservative optimum than an RBDO optimum at a specific confidence level. Numerical examples using mathematical functions and finite element analysis show that the proposed confidence analysis and CBDO can prevent overestimation of reliability caused by an inaccurate surrogate model.

Additive manufacturing (AM) offers many advantages to make objects compared to traditional subtractive manufacturing methods. For example, complex geometries can be easily fabricated, and lightweight parts can be formed while maintaining the parts strength for the low carbon footprint, low material consumption and waste. But there are some areas for AM to improve in sustainability, reliability, productivity, robustness, material diversity, and part quality. Life-cycle assessment studies have identified that the AM printing stage has a big impact on the life-cycle sustainability of 3D printed products. AM building parameters can be properly selected to improve the sustainability of AM. This paper explores the fused deposition modeling (FDM) process parameters for sustainability to reduce the process energy and material consumption. Investigated parameters include the printing layer height, number of shells, material infilling percentage, infilling type, and building orientation. Taguchi design of experiments approach and statistical analysis tools are used to find optimal parameter settings to improve the sustainability of the FDM process. Models formulated in this research can be easily extended to other AM processes.

In this study, we propose a scalable batch sampling scheme for optimization of simulation models with spatially varying noise. The proposed scheme has two primary advantages: (i) reduced simulation cost by recommending batches of samples at carefully selected spatial locations and (ii) improved scalability by actively considering replicating at previously observed sampling locations. Replication improves the scalability of the proposed sampling scheme as the computational cost of adaptive sampling schemes grow cubicly with the number of unique sampling locations. Our main consideration for the allocation of computational resources is the minimization of the uncertainty in the optimal design. We analytically derive the relationship between the “exploration versus replication decision” and the posterior variance of the spatial random process used to approximate the simulation model’s mean response. Leveraging this reformulation in a novel objective-driven adaptive sampling scheme, we show that we can identify batches of samples that minimize the prediction uncertainty only in the regions of the design space expected to contain the global optimum. Finally, the proposed sampling scheme adopts a modified preposterior analysis that uses a zeroth-order interpolation of the spatially varying simulation noise to identify sampling batches. Through the optimization of three numerical test functions and one engineering problem, we demonstrate (i) the efficacy and of the proposed sampling scheme to deal with a wide array of stochastic functions, (ii) the superior performance of the proposed method on all test functions compared to existing methods, (iii) the empirical validity of using a zeroth-order approximation for the allocation of sampling batches, and (iv) its applicability to molecular dynamics simulations by optimizing the performance of an organic photovoltaic cell as a function of its processing settings.

This work deduces principles of bioinspired product architecture to effectively leverage biological function-sharing in engineering design. Function-sharing enables a single structure to perform multiple functions and can improve the performance characteristics of a system. The process of evolution via natural selection has led to the emergence of function-sharing adaptations in biological systems. However, the current practice of bioinspired function-sharing is largely limited to the solution-driven imitation of biological structures. This work aims to overcome such limitations by performing a function-based analysis of biological product architectures. First, a phylogenetic approach is used to select generalized case studies from the animal kingdom. Next, the product architectures of the selected case studies are then modeled using function modeling and analyzed by clustering the identified functions into modules. A function-based categorization of the sampled biological modules reveals the presence of four types of modules in the biological case studies. Analyzing the function-sharing scenarios associated with each type of biological module enables us to deduce four guidelines for bioinspired development and arrangement of function-sharing modules. Finally, a demonstration study applies the guidelines to the design of an inlet–outlet port for a washer–dryer system. The deduced guidelines can enable engineers to identify function-sharing scenarios in the early stages of product design and reduce the need to imitate biological structures for function-sharing.