Undetected fatigue and overload damages at the key locations of the crane boom are among the biggest threats in construction, leading to structural failure. Thus, the structural health of the crane boom should be monitored in real-time to ensure that it works under the designed load capacity. In this work, we developed a lightweight digital twin by the multi-fidelity surrogate (MFS) model to improve the real-time monitoring and prediction accuracy of the structural safety of a crane boom. Digital twin technology, which can establish real-time mapping between the physical space and the digital space, has a promising potential for online monitoring and analysis of structures, equipment, and even human bodies. By combining the MFS model and sensor data, the lightweight digital twin can dynamically mirror the crane boom postures and predict its structural performance in real-time. In this study, the structural analysis of the crane boom is limited to the linear elastic stage of materials. Numerical experiments showed that the accuracy of the lightweight digital twin was enhanced compared with that established by the single-fidelity surrogate model, and the computational cost of the lightweight digital twin was decreased with respect to the digital twin built by the numerical method. Meanwhile, the uncertainty from the physical space was analyzed to enhance the reliability of the lightweight digital twin. Thus, the lightweight digital twin developed in our work can ensure accurate safety prediction and design optimization for crane booms.

This technical brief is an attempt to report a case analysis of Engineering for Global Development (EGD) research from India. As a part of the engineering design and research project, two models of Improvised Betel Nut Cutter (IBNC) are designed, manufactured and field-tested. Engineering ethnography is adopted as a research tool during the design process of IBCN. It facilitated the participation of local stakeholders in the design process which is found to be effective for contextual understandings of the target community's needs, aspirations, constraints and socio-cultural norms and values. The technological intervention is expected to solve the problems like finger cuts, health drudgery (musculoskeletal pain, back pain, etc.) of the women involved in the supari business in Assam, India. It will also improve productivity and efficiency in the supari cutting practices of women. This case analysis depicts the effectiveness of trans-disciplinary effort in solving complex engineering problems at the bottom of the pyramid (BOP). Evidence gathered from this study is expected to be a useful resource for future endeavours in EGD research.

This article presents a piezo-actuated microgripper characterized by large amplification ratio and compact structure size. The microgripper is actuated by a piezo-stack actuator that is integrated with a two-stage displacement amplifier to achieve large travel range. A new design methodology “flexure hinge individualized design” (FHID) was proposed to realize large amplification ratio. According to this methodology, each flexure hinge was designed personally based on force condition of the piviot to reconfigure the motion stiffness of the compliant microgripper so that the parasitic motion and displacement loss could be eliminated. Consequently, a 52-amplification ratio amplifier was obtained. The developed microgripper was modeled via kinematics and Castigliano’s displacement theorem, respectively. Finite element analysis and the experimental studies were conducted to evaluate the characteristics of the microgripper. The results show that the motion stroke of the gripper tip is 917 μm, and the structure dimension is 62 mm × 42 mm × 12 mm. The design methodology FHID is generic and can be extended to other compliant mechanisms.

Recently, online user-generated data have been used as an efficient resource for customer analysis. In the product design area, various methods for analyzing customer preference for product features have been suggested. However, most of them focused on feature categories rather than product components which are crucial in practical applications. To address that limitation, this paper proposes a new methodology for extracting sub-features from online data. First, the method detects phrases in the data and filtered them using product manual documents. The filtered phrases are embedded into vectors, and then they are divided into several groups by two clustering methods. The resulting clusters are labeled by analyzing items in each cluster. Finally, cue phrases for sub-features are obtained by selecting clusters with labels representing product features. The proposed methodology was tested on smartphone review data. The result provides feature clusters containing sub-feature phrases with high accuracy. The obtained cue phrases will be used in analyzing customer preferences for sub-features and this can help product designers determine the optimal component configuration in embodiment design.

Designers from around the world have proposed numerous engineering design solutions for problems related to the COVID-19 pandemic, many of which leverage the rapid prototyping and manufacturing capabilities of additive manufacturing (AM). While some of these solutions are motivated by complex and urgent requirements (e.g., face masks), others are motivated by simpler and less urgent needs (e.g., hands-free door openers). Previous research suggests that problem definition influences the creativity of solutions generated for the problem. In this study, we investigate the relationship between the definition of problems related to the COVID-19 pandemic and the characteristics of AM solutions that were openly shared for these problems. Specifically, we analyze 26 AM solutions spanning three categories: (1) hands-free door openers (low complexity problem), (2) face shields (moderate complexity problem), and (3) face masks (high complexity problem). These designs were compared on (1) DfAM utilization, (2) manufacturability (i.e., build time, cost, and material usage), and (3) creativity. We see that the solutions designed for the high complexity problem, i.e., face masks, were least suitable for AM. Moreover, we see that solutions designed for the moderate complexity problem, i.e., face shields, had the lowest build time, build cost, and material consumption. Finally, we observe that the problem definition did not relate to the creativity of the AM solutions. In light of these findings, designers must sufficiently emphasize the AM suitability and manufacturability of their solutions when designing for urgent and complex problems in rapid response situations.

Sudden changes in life and work patterns due to COVID-19 have affected customer requirements for the products. Under these circumstances, companies can achieve high profitability and customer satisfaction when they can efficiently identify and respond quickly to changing customer preferences caused by COVID-19. This article presents empirical research on dynamic changes in customer responses for product features caused by the spread of COVID-19 through sentiment analysis based on online reviews. A case study is conducted using new and refurbished smartphone reviews to investigate the dynamic changes in customer sentiment before/during COVID-19. The importance of the result is shown by comparing it to the actual market data.

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).

This paper aims to present the topological structure design and kinematic analysis of a novel double-ring truss deployable satellite antenna mechanism. First, a new topological scheme and a new rectangular prism deployable linkage unit are proposed for constructing the kind of antenna mechanisms. Second, the degree-of-freedom (DOF) of the deployable unit and the antenna mechanism are analyzed based on structure decomposition and screw theory. Third, the kinematic model of the double-ring truss deployable antenna mechanism is established based on its structural characteristics. Finally, a typical numerical example is used to illustrate the effectiveness of the designed mechanism and the established kinematic model. The new double-ring truss deployable antenna mechanism consists of the units with the better structural symmetry and has simpler joint axis layouts, comparing with the same type of most existing mechanisms.

Engineering design problems often involve large state and action spaces along with highly sparse rewards. Since an exhaustive search of those spaces is not feasible, humans utilize relevant domain knowledge to condense the search space. Deep learning agents (DLAgents) were previously introduced to use visual imitation learning to model design domain knowledge. This note builds on DLAgents and integrates them with one-step lookahead search to develop goal-directed agents capable of enhancing learned strategies for sequentially generating designs. Goal-directed DLAgents can employ human strategies learned from data along with optimizing an objective function. The visual imitation network from DLAgents is composed of a convolutional encoder–decoder network, acting as a rough planning step that is agnostic to feedback. Meanwhile, the lookahead search identifies the fine-tuned design action guided by an objective. These design agents are trained on an unconstrained truss design problem modeled as a sequential, action-based configuration design problem. The agents are then evaluated on two versions of the problem: the original version used for training and an unseen constrained version with an obstructed construction space. The goal-directed agents outperform the human designers used to train the network as well as the previous feedback-agnostic versions of the agent in both scenarios. This illustrates a design agent framework that can efficiently use feedback to not only enhance learned design strategies but also adapt to unseen design problems.

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.

While the modeling analysis of the kinetostatic behavior of underactuated tendon-driven robotic fingers has been largely addressed in the literature, tendon routing is often not considered by these theoretical models. The tendon routing path plays a fundamental role in defining joint torques, and subsequently, the force vectors produced by the phalanges. However, dynamic tendon behavior is difficult to predict and is influenced by many external factors including tendon friction, the shape of the grasped object, the initial pose of the fingers, and finger contact points. In this paper, we present an experimental comparison of the force performance of nine fingers, with different tendon routing configurations. We use the concept of force-isotropy, in which forces are equal and distributed on each phalanx as the optimum condition for an adaptive grasp. Our results show only some of the finger designs surveyed exhibited a partial adaptive behavior, showing distributed force for the proximal and distal phalanxes throughout grasping cycles, while other routings resulted in only a single phalanx remaining in contact with the object.

High power density in-wheel motor could be achieved by integrating a high-speed ratio (larger than 25) compound power-split mechanism (CPSM) with small motors. However, due to the exhaustive searching method adopted by the traditional lever analogy method, it is time-consuming to design high-speed ratio compound power-split mechanism configurations satisfying the high power density of in-wheel motor. In this paper, an improved lever analogy method is proposed to find the optimal configurations with a high-speed ratio to satisfy the high power density in-wheel motor. In this method, a judgment algorithm about the rank of structure matrix is proposed to identify three-node compound lever models of the CPSM. The improved lever analogy method can filter out useless configurations that significantly improve the calculation efficiency. The simulation results show that the calculation efficiency is improved by 215 times higher than that of the lever analogy method. Finally, 16 reasonable and 14 new configurations are obtained. This indicates that the improved lever analogy method can provide an effective way to design the high-speed ratio CPSM, which is widely used in-wheel motor-driven vehicles.

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.

Design strategies for foldable mechanisms have been developed with inspiration from origami. In this study, we investigate a new direction that blocks are folded in a way that origami folds as the ori-blocks to generate a new type of foldable mechanisms consisting of multiple blocks. During the investigation, we propose a design approach to construct ori-blocks dissected from cylinders and cones, where “ori” is derived from the word “origami” in its original meaning as “folding”. In this way, we cut the solids into six portions and assign rotation axes to assemble the portions into movable blocks. Interestingly, this connects the Bricard classical linkages developed in 1897 to these ori-blocks with coincidence of the position and orientation of the axes when the blocks are replaced by links. The study bridges the gap between ori-blocks, origami, and mechanisms, which proposes a set of novel reconfigurable mechanisms as ori-blocks. As spatial linkages have been widely used in a broad range of technical fields, we anticipate that ori-blocks will find several potential applications owing to their kinematics in reconfigurability.

The transition to Industry 4.0 poses new challenges for sensor integration design. The foundation of any intelligent system is the data, and the data quality depends largely on the integration of the sensor generating it. In this study, the barriers for robust sensor system design are explored through an interview study among practitioners from different industrial contexts. The aim is to explore potential challenges within different contexts and suggest possible directions for research within the field of sensor integration design. Beyond the question of new sensing technologies, the study highlights an increasing challenge of physical integration tasks and illustrates the varying requirements for development support in different industry sectors.

Identifying product attributes from the perspective of a customer is essential to measure the satisfaction, importance, and Kano category of each product attribute for product design. This article proposes automated keyword filtering to identify product attributes from online customer reviews based on latent Dirichlet allocation. The preprocessing for latent Dirichlet allocation is important because it affects the results of topic modeling; however, previous research performed latent Dirichlet allocation either without removing noise keywords or by manually eliminating them. The proposed method improves the preprocessing for latent Dirichlet allocation by conducting automated filtering to remove the noise keywords that are not related to the product. A case study of Android smartphones is performed to validate the proposed method. The performance of the latent Dirichlet allocation by the proposed method is compared to that of a previous method, and according to the latent Dirichlet allocation results, the former exhibits a higher performance than the latter.

System design is commonly thought of as a process of maximizing a design objective subject to constraints, among which are the system requirements. Given system-level requirements, a convenient management approach is to disaggregate the system into subsystems and to “flowdown” the system-level requirements to the subsystem or lower levels. We note, however, that requirements truly are constraints, and they typically impose a penalty on system performance. Furthermore, disaggregation of the system-level requirements into the flowdown requirements creates added sets of constraints, all of which have the potential to impose further penalties on overall system performance. This is a highly undesirable effect of an otherwise beneficial system design management process. This article derives conditions that may be imposed on the flowdown requirements to assure that they do not penalize overall system performance beyond the system-level requirement.

A novel planetary thread roller bearing (PTRB) was proposed in this paper based on the Hertz theory combined with multiple principles (e.g., equivalent steel ball, multi-point meshing, and power dividing). A significant improvement in load-carrying capacity, compared with existing bearings, was achieved and thus providing a new option for the advanced equipment with high thrust-to-weight ratio requirement. The newly proposed PTRB exhibited a 83% increase in dynamic axial load rating while a comparable static axial load rating comparing with the thrust ball bearing with a comparable size. Furthermore, the working efficiency of the new PTRB was constantly higher than 97%. A self-degradation operation was achieved by the system when some thread rollers were stuck, which improved the system fault tolerance. Finally, friction torque tests were performed on the self-developed test instrument. The results showed a good agreement with the theoretical analysis.

In the past few decades, multidisciplinary design optimization (MDO) has become a very important research topic along with the increase of the system complexity. In an MDO problem, it is very typical that multiple disciplines are involved, making the problem coupled and complex. Monolithic and distributed architectures have been proposed for solving MDO problems. However, efficient architectures are still needed. In the prior work, a sequential multidisciplinary design optimization (S-MDO) architecture was proposed that has a distributed structure that decomposes the original MDO problem into several subproblems. However, in the original S-MDO work, the theoretical behaviors were not analyzed because its mathematical representations were not clear. In this article, we present a clear mathematical representation of the S-MDO architecture and conduct theoretical analysis on the S-MDO architecture to explain its performance in solving MDO problems. The optimality condition of the S-MDO architecture is derived and summarized as a theorem and a proposition. To demonstrate the general formulation of solving an MDO problem using the S-MDO architecture and validate the correctness of the optimality condition, we use it to obtain the Pareto frontier of a benchmark MDO problem. From the spread of the obtained Pareto frontier, we can conclude that the S-MDO architecture performs well, as long as the global optimum of each disciplinary subproblem can be found.

Additive manufacturing (AM) offers designers access to the entire volume of an artifact during its build operation, including the embedding of foreign objects, like sensors, motors, and actuators, into the artifact to produce multifunctional products from the build tray. However, the application of embedding requires extensive designer expertise in AM. This research aims to develop a tool to automate design decisions for in situ embedding, eliminating the need for ad hoc design decisions made by experts. Two unique approaches are proposed in this work: shadow projection and voxel simulation. Both of these approaches follow a three-stage methodology to achieve design automation by (1) identifying the optimum orientation for the object, (2) designing cavity, and finally (3) designing the shape converter for a flush surface at the paused layer. The two approaches differ in Stages 2 and 3. Where the shadow projection approach employs a series of point cloud manipulation to geometry of the embedded object, the voxel simulation approach simulates the process of insertion of the embedding geometry into the part voxel by voxel. While both proposed approaches are successful in automating design for embedding complex geometries, they result in tradeoffs between final designs and the time for computation. Computational experiment with six test cases shows that designers must strategically choose from one of the approaches to efficiently automate the digital design for embedding.