Prototyping Strategy Research Articles

Phase-2 CMS DAQ — growing from prototype boards to demonstrator systems

For the Phase-2 upgrade of the CMS experiment, the CMS central DAQ group designed and developed two custom ATCA boards. These boards provide the interfaces between the sub-detector electronics and the central CMS systems. This paper describes our experience with the chosen prototyping strategy, with a focus on the design modification choices made along the way. It concludes with a brief overview of recent firmware developments, and a look at the transition towards the full board production.

General Spin-Restricted Open-Shell Configuration Interaction Approach: Application to Metal K-Edge X-ray Absorption Spectra of Ferro- and Antiferromagnetically Coupled Dimers.

In this work, we present a generalized implementation of the previously developed restricted open-shell configuration interaction singles (ROCIS) family of methods. The new method allows us to treat high-spin (HS) ferro- as well as antiferromagnetically (AF) coupled systems while retaining the total spin as a good quantum number. To achieve this important and nontrivial goal, we employ the machinery of the iterative configuration expansion (ICE) method, which is able to tackle general configuration interaction (CI) problems on the basis of spin-adapted configuration state functions (CSFs). While ICE is designed to work in restricted orbital spaces, the new general-spin ROCIS (GS-ROCIS) method is designed to be applicable to larger molecules by employing a prototyping strategy. This new method can be applied to closed-shell, high-spin open-shell, as well as antiferromagnetic reference CSFs. In addition, GS-ROCIS can be combined with the pair natural orbital (PNO) machinery in the form of the PNO-GS-ROCIS method. With this extension, one can drastically reduce the required virtual space in the vicinity of the involved core orbitals, leading to computational savings of several orders of magnitude with negligible (<1%) loss in accuracy. To demonstrate the use of the new methodology, the metal K pre-edge X-ray absorption excitation problem of an antiferromagnetically coupled copper model dimer was investigated. By first analyzing a model copper dimer, it is shown that even for the minimum core excitation problem that involves the two antiferromagnetically coupled singly occupied orbitals and one virtual orbital, the resulting GS-ROCIS and broken-symmetry configuration interaction singles (BS-CIS) spectra may differ in terms of the number, energy position, and relative intensity of the computed bands. Furthermore, the methodology was validated to perform equally well in computing the K-edge spectra of antiferromagnetic nickel oxide dimers and mixed-valence cobalt oxide trimers. Collectively, the present development represents an important methodological advance in the application of theoretical X-ray spectroscopy.

How to design prototypes to prevent potential dominant logic conflict during the iterative learning process

Although prototype-based iterative learning is receiving increasing attention from research on lean startups, this stream of literature has a narrow focus on customer feedback and neglects the challenges of managing internal cognitive coherence during iterative learning processes. Without a shared dominant logic, the iterative revision of a prototype may fail. This research addresses this limitation by distinguishing the effects of different prototype strategies on dominant logic conflict and examining the moderating effect of socialization. Four hypotheses are developed and examined with survey data from 299 Chinese high-tech firms. We find that a basic prototype strategy results in low dominant logic conflict while an enhanced prototype strategy results in high dominant logic conflict during prototype-based iterative learning. Furthermore, socialization amplifies the negative and positive respective effects of these two prototype strategies. This research contributes to the literature on prototype-based iterative learning (e.g., for lean startups) and offers new managerial implications for practice.

Prototype strategy, market big data and identification of latent customer needs: an organizational learning perspective

PurposeAlthough the literature provides insights into the role of experiential learning based on prototypes in identification of latent customer need, it offers different views on the role of product prototypes in improving the efficacy of learning customer need, and also neglects the role of vicarious learning in prototype-based experiential learning. In a data-rich environment, market big data create new opportunities to learn from vicarious, digitalized experiences that are not observable with prototype-based learning. Therefore, the purpose of this study is to compare the effects of product prototype strategies – basic prototype strategy and enhanced prototype strategy – on identification of latent customer needs, and determine how each prototype strategy interacts with vicarious learning based on market big data to identify latent customer needs.Design/methodology/approachWe collected data from 299 Chinese manufacturing firms via on-site surveys to explore our research question. All of our hypotheses were supported by the regression results.FindingsThis study finds that both the enhanced and basic prototype strategies (experiential learning from direct market experience based on prototyping) have positive effects on latent need identification, but the effect of enhanced prototypes is stronger. Furthermore, the enhanced and basic prototype strategies have different interaction effects with market big data (vicarious learning from indirect market experiences) on latent need identification.Originality/valueThis research extends the literature on prototype-based learning for latent need identification. It also contributes to the experiential prototype-based learning literature by exploring the role of vicarious learning based on market big data.

Rapid prototyping of thermoplastic microfluidic devices via SLA 3D printing

Microfluidic devices have immense potential for widespread community use, but a current bottleneck is the transition from research prototyping into mass production because the gold standard prototyping strategy is too costly and labor intensive when scaling up fabrication throughput. For increased throughput, it is common to mold devices out of thermoplastics due to low per-unit costs at high volumes. However, conventional fabrication methods have high upfront development expenses with slow mold fabrication methods that limit the speed of design evolution for expedited marketability. To overcome this limitation, we propose a rapid prototyping protocol to fabricate thermoplastic devices from a stereolithography (SLA) 3D printed template through intermediate steps akin to those employed in soft lithography. We apply this process towards the design of self-operating capillaric circuits, well suited for deployment as low-cost decentralized assays. Rapid development of these geometry- and material-dependent devices benefits from prototyping with thermoplastics. We validated the constructed capillaric circuits by performing an autonomous, pre-programmed, bead-based immunofluorescent assay for protein quantification. Overall, this prototyping method provides a valuable means for quickly iterating and refining microfluidic devices, paving the way for future scaling of production.

MIL, SIL and PIL Implementation for Closed Loop Control of Flyback Converter

Power electronic systems are rapidly evolving; thus, effective prototyping methods are required to test control algorithms and assess system performance prior to hardware implementation. This research suggests a Model-In-The-Loop (MIL), Software-In-The-Loop (SIL), and Processor-In-The-Loop (PIL) methodologies-based complete prototyping strategy for flyback converters using the TI F28069M Launchpad kit. These techniques can be combined to evaluate control strategies accurately and quickly, speeding up design cycles and enhancing system reliability. The proposed prototyping platform is presented in this work, along with a thorough explanation of each prototyping stage and its associated advantages. The effectiveness of the suggested approach for 50W rated power flyback converter in terms of quick algorithm development, system simulation, real-time control implementation and controller design accuracy is analyzed and shown by experimental results. The results show that the performance of the designed controller for the flyback converter is almost the same as in MIL and SIL implementation in terms of the overshoot and settling time in the reference voltage tracking. On the other hand, in the PIL implementation, the overshoot performance of the controller deviates by 1.18% more than in SIL and MIL implementation. These also confirm that the flyback control system's performance is reliable and effective during all phases of development.

Rapid prototyping and facile customization of conductive hydrogel bioelectronics based on all laser process

Proper customization in size and shape is essential in implantable bioelectronics for stable bio-signal recording. Over the past decades, many researchers have heavily relied on conventional photolithography processes to fabricate implantable bioelectronics. Therefore, they could not avoid the critical limitation of high cost and complex processing steps to optimize bioelectronic devices for target organs with various sizes and shapes. Here, we propose rapid prototyping using all laser processes to fabricate customized bioelectronics. PEDOT:PSS is selectively irradiated by an ultraviolet (UV) pulse laser to form wet-stable conductive hydrogels that can softly interact with biological tissues (50 μm line width). The encapsulation layer is selectively patterned using the same laser source by UV-curing polymer networks (110 μm line width). For high stretchability (over 100%), mesh structures are made by the selective laser cutting process. Our rapid prototyping strategy minimizes the use of high-cost equipment, using only a single UV laser source to process the electrodes, encapsulation, and substrates that constitute bioelectronics without a photomask, enabling the prototyping stretchable microelectrode array with an area of 1 cm2 less than 10 min. We fabricated an optimized stretchable microelectrode array with low impedances (∼1.1 kΩ at 1 kHz) that can effectively record rat's cardiac signals with various health states.

Developing Shape Change-Based Fashion Prototyping Strategies: Enhancing Computational Thinking in Fashion Practice and Creativity

Emerging technologies enable fluid and versatile material forms of fashionable wearables and e-textiles, with experts in engineering and material science proposing numerous strategies for dynamic textile and garment structures to satisfy various needs. Nevertheless, a critical gap remains in developing practical fashion prototyping strategies that fuse with computational thinking to challenge current norms and envision the future of fashion. This study introduces shape change-based fashion prototyping as a design strategy for dynamic expressions and affordances to inspire fashion practitioners’ interdisciplinary endeavors. We present three studio-based practices as case studies to demonstrate how shape-changing mechanisms including servo motors, shape memory alloys, and pneumatics, spur new fashion construction skills and broaden the scope of potential applications. By doing so, this study contributes to material and conceptual innovation, creating pathways for the seamless integration of technologies from conceptualization, and implementation to envision. Our findings shed light on design possibilities and challenges and offer design recommendations that guide future endeavors. The implications of our research underscore the importance of adopting a relational approach to design variables, emphasize the value of fostering shared vocabulary between fashion and technical design, and highlight the transformative potential of shape-changing prototyping in reshaping the intricate body-material relationship.

Enhancing Out-of-distribution Generalization on Graphs via Causal Attention Learning

In graph classification, attention- and pooling-based graph neural networks (GNNs) predominate to extract salient features from the input graph and support the prediction. They mostly follow the paradigm of “learning to attend,” which maximizes the mutual information between the attended graph and the ground-truth label. However, this paradigm causes GNN classifiers to indiscriminately absorb all statistical correlations between input features and labels in the training data without distinguishing the causal and noncausal effects of features. Rather than emphasizing causal features, the attended graphs tend to rely on noncausal features as shortcuts to predictions. These shortcut features may easily change outside the training distribution, thereby leading to poor generalization for GNN classifiers. In this article, we take a causal view on GNN modeling. Under our causal assumption, the shortcut feature serves as a confounder between the causal feature and prediction. It misleads the classifier into learning spurious correlations that facilitate prediction in in-distribution (ID) test evaluation while causing significant performance drop in out-of-distribution (OOD) test data. To address this issue, we employ the backdoor adjustment from causal theory—combining each causal feature with various shortcut features, to identify causal patterns and mitigate the confounding effect. Specifically, we employ attention modules to estimate the causal and shortcut features of the input graph. Then, a memory bank collects the estimated shortcut features, enhancing the diversity of shortcut features for combination. Simultaneously, we apply the prototype strategy to improve the consistency of intra-class causal features. We term our method as CAL+, which can promote stable relationships between causal estimation and prediction, regardless of distribution changes. Extensive experiments on synthetic and real-world OOD benchmarks demonstrate our method’s effectiveness in improving OOD generalization. Our codes are released at https://github.com/shuyao-wang/CAL-plus .

A Rapid Control Prototyping and Hardware-in-the Loop Approach for Upper Limb Robotic Exoskeletons Control

In the last decade, robotic-mediated rehabilitation has emerged as a potential solution to improve repetitive task training. Each device in this field has a unique development history shaped by engineers’ expertise in specific programming languages or platforms. In this work we adopt an approach that tries to abstract from the final implementation with the aim to make control logic more shareable and understandable. The authors will present the outcomes of the application of a Rapid Control Prototyping strategy to an upper-limb robotic exoskeleton. A model-based design approach implemented on a real-time target machine is presented. This modern design approach was explored with several control strategies and was used to test the exoskeleton’s performances. The proposed method highlights how it is possible to develop the entire control architecture in a single programming environment.

A promising yellow-emitting langbeinite-type phosphor NaBaY2(PO4)3:Eu2+ for WLEDs.

The development of novel phosphors with excellent luminescence properties for white light-emitting diodes (WLEDs) is always a formidable task. Compared with the traditional laborious and aimless "trial and error" experiments, a mineral-inspired prototype strategy can provide an efficient and accurate method. Herein, for the first time, a novel yellow-emitting phosphor NaBaY2(PO4)3:Eu2+ was discovered using the mineral-inspired prototype strategy and cation substitution method. The phosphor was prepared via the high-temperature solid-state reaction method, and its crystal structure, luminescence properties and potential application for WLEDs were systematically investigated. The NaBaY2(PO4)3 phase was derived from the K2Mg2(SO4)3-type mineral structure and a photoluminescence study revealed that the phosphor can emit bright yellow light with a maximum at 545 nm upon excitation at 351 nm. A WLED lamp was fabricated by the use of a blend of commercial blue-emitting BaMgAl10O17:Eu2+, yellow-emitting NaBaY2(PO4)3:Eu2+ and red-emitting (Ca, Sr)AlSiN3:Eu2+ phosphors with a 380 nm LED chip. The CIE, CCT, Ra and efficiency of the as-fabricated LEDs were measured to be equal to (0.366, 0.365), 4327 K, 91.2 and 40.8 lm W-1, respectively. These results suggest that NaBaY2(PO4)3:Eu2+ could be a promising candidate for n-UV WLEDs.

Agile prototyping strategy for building shared understanding in requirements engineering

The strategy proposed in this paper addresses a critical challenge in requirements engineering: building a shared understanding among all stakeholders in software development. Shared understanding refers to the ability of people in a team to have a common and consistent understanding of the requirements that the system will have, which is essential for effective collaboration. The construction of this strategy was done using Situational Method Engineering (SME), which allowed to obtain, at the end of the realization of each of its activities, a set of phases, with their respective activities and tasks, which seek the construction of shared understanding through the realization of agile prototyping. The use of prototypes allows to provide a visual and tangible representation of the system requirements, which facilitates communication and feedback among stakeholders. In this sense, this article shows the use of SME for the final construction of the strategy, which consists of several phases, including planning, requirements identification and analysis, non-functional prototyping, requirements management, functional prototyping, and socialization. These phases are designed to strengthen communication, promote collaboration, and ensure a solid shared understanding throughout the software development process.

A generalized fuzzy clustering framework for incomplete data by integrating feature weighted and kernel learning.

Missing data presents a challenge to clustering algorithms, as traditional methods tend to pad incomplete data first before clustering. To combine the two processes of padding and clustering and improve the clustering accuracy, a generalized fuzzy clustering framework is proposed based on optimal completion strategy (OCS) and nearest prototype strategy (NPS) with four improved algorithms developed. Feature weights are introduced to reduce outliers' influence on the cluster centers, and kernel functions are used to solve the linear indistinguishability problem. The proposed algorithms are evaluated regarding correct clustering rate, iteration number, and external evaluation indexes with nine datasets from the UCI (University of California, Irvine) Machine Learning Repository. The results of the experiment indicate that the clustering accuracy of the feature weighted kernel fuzzy C-means algorithm with NPS (NPS-WKFCM) and feature weighted kernel fuzzy C-means algorithm with OCS (OCS-WKFCM) under varying missing rates is superior to that of seven conventional algorithms. Experiments demonstrate that the enhanced algorithm proposed for clustering incomplete data is superior.

HOW DO PROTOTYPES CHANGE? CHARACTERISING QUANTITATIVE AND QUALITATIVE CHANGES BETWEEN PROTOTYPE ITERATIONS

AbstractPrototyping strategies and technology often focus on reducing the fabrication time and cost between design iterations, however, there is limited knowledge about the specific characteristics of change that prototyping strategies aim to impact. To investigate, and better understand these characteristics, this study curates and systematically analyses a representative dataset of 50 'real-world' prototype samples. The study aims to explore the various elements that constitute a design change and to determine their impact on the scale of volumetric change detected. The results highlight emergent patterns and correlations between study metrics to better understand the reasons for design change and the frequency and scale of changes detected in the sample dataset. Findings reveal that the purpose of a design change is, in certain cases, highly correlated to the scale of change affected, and that some changes are more prevalent in the dataset than others, with an average volumetric difference of 4.2% between sample versions detected. The study provides an initial characterisation of prototype change to guide iterative prototyping processes and improve the efficiency and effectiveness of design iterations.

Prototyping Strategies to Engage Stakeholders During Early Stages of Design: A Study Across Three Design Domains

Abstract Using prototypes to engage stakeholders during front-end design activities is crucial for successful design outcomes. Compared to prototyping that is used for iterative refinement during back-end engineering design activities, prototyping that informs problem definition, requirements and specifications development, concept generation, and other front-end design activities is understudied. To identify patterns in prototyping strategies for engaging stakeholders during the design front end, we conducted semi-structured interviews with 26 design practitioners across three product design domains: automotive, consumer products, and medical devices. Seventeen strategies evident across the collection of practitioners were used in generally consistent ways, with some variation based on context, e.g., project scope, stakeholders engaged, and the stakeholder interaction situation. Twelve of those 17 strategies were used by industry practitioners across the three domains, and five of those 17 strategies were used by practitioners from the medical device domain and either the automotive or consumer products domain. The descriptions and in-context examples of prototyping strategies used to engage stakeholders during front-end design can guide the design strategies of both experienced and novice designers.

Meta-prompt based learning for low-resource false information detection

The wide spread of false information has detrimental effects on society, and false information detection has received wide attention. When new domains appear, the relevant labeled data is scarce, which brings severe challenges to the detection. Previous work mainly leverages additional data or domain adaptation technology to assist detection. The former would lead to a severe data burden; the latter underutilizes the pre-trained language model because there is a gap between the downstream task and the pre-training task, which is also inefficient for model storage because it needs to store a set of parameters for each domain. To this end, we propose a meta-prompt based learning (MAP) framework for low-resource false information detection. We excavate the potential of pre-trained language models by transforming the detection tasks into pre-training tasks by constructing template. To solve the problem of the randomly initialized template hindering excavation performance, we learn optimal initialized parameters by borrowing the benefit of meta learning in fast parameter training. The combination of meta learning and prompt learning for the detection is non-trivial: Constructing meta tasks to get initialized parameters suitable for different domains and setting up the prompt model’s verbalizer for classification in the noisy low-resource scenario are challenging. For the former, we propose a multi-domain meta task construction method to learn domain-invariant meta knowledge. For the latter, we propose a prototype verbalizer to summarize category information and design a noise-resistant prototyping strategy to reduce the influence of noise data. Extensive experiments on real-world data demonstrate the superiority of the MAP in new domains of false information detection.

3D Printing of Small‐Scale Soft Robots with Programmable Magnetization

AbstractSoft magnetic structures having a non‐uniform magnetization profile can achieve multimodal locomotion that is helpful to operate in confined spaces. However, incorporating such magnetic anisotropy into their body is not straightforward. Existing methods are either limited in the anisotropic profiles they can achieve or too cumbersome and time‐consuming to produce. Herein, a 3D printing method allowing to incorporate magnetic anisotropy directly into the printed soft structure is demonstrated. This offers at the same time a simple and time‐efficient magnetic soft robot prototyping strategy. The proposed process involves orienting the magnetized particles in the magnetic ink used in the 3D printer by a custom electromagnetic coil system acting onto the particles while printing. The resulting structures are extensively characterized to confirm the validity of the process. The extent of orientation is determined to be between 92% and 99%. A few examples of remotely actuated small‐scale soft robots that are printed through this method are also demonstrated. Just like 3D printing gives the freedom to print a large number of variations in shapes, the proposed method also gives the freedom to incorporate an extensive range of magnetic anisotropies.

Front-end design prototyping strategies during remote stakeholder engagement

Abstract Engineers must engage project stakeholders effectively if stakeholder needs are to be met, and prototypes are key tools for communicating design form and function. Quality stakeholder engagement in the front end of design processes, in particular, is critical in the success or failure of design projects. As remote stakeholder engagement has become increasingly common as industry trends toward distributed design, there is a need to develop the theory and practices behind effective remote design processes, which have not yet been as well-studied as in-person design. This study explored the prototyping strategies for remote stakeholder engagement during front-end design used by 10 engineering practitioners and 10 senior engineering students through semi-structured interviews. Prototyping strategies were found to overlap with many of the strategies described by prior literature that are not specific to remote engagement modes, though several of these strategies were adapted to the remote context, and three emergent strategies for prototyping in remote engagements were identified. Designers’ perceptions of remote versus in-person prototyping strategies for stakeholder engagement in front-end design, including perceived advantages and limitations, were also explored, and recommendations for educators to better prepare engineering students for hybrid and remote work are provided.

Design, additive manufacturing and component testing of pneumatic rotary vane actuators for lightweight robots

Purpose This study aims to investigate the suitability of a multi-step prototyping strategy for producing pneumatic rotary vane actuators (RVAs) for the development of lightweight robots and actuation systems. Design/methodology/approach RVAs typically have cast aluminum housings and injection-molded seals that consist of hard thermoplastic cores and soft elastomeric overmolds. Using a combination of additive manufacturing (AM), computer numerical control (CNC) machining and elastomer molding, a conventionally manufactured standard RVA was replicated. The standard housing design was modified, and polymeric replicas were obtained by selective laser sintering (SLS) or PolyJet (PJ) printing and subsequent CNC milling. Using laser-sintered molds, actuator seals were replicated by overmolding laser-sintered polyamide cores with silicone (SIL) and polyurethane (PU) elastomers. The replica RVAs were subjected to a series of leakage, friction and durability experiments. Findings The AM-based prototyping strategy described is suitable for producing functional and reliable RVAs for research and product development. In a representative durability experiment, the RVAs in this study endured between 40,000 and 1,000,000 load cycles. Frictional torques were around 0.5 Nm, which is 10% of the theoretical torque at 6 bar and comparable to that of the standard RVA. Models and parameters are provided for describing the velocity-dependent frictional torque. Leakage experiments at 10,000 load cycles and 6 bar differential pressure showed that PJ housings exhibit lower leakage values (6.8 L/min) than laser-sintered housings (15.2 L/min), and PU seals exhibit lower values (8.0 l/min) than SIL seals (14.0 L/min). Combining PU seals with PJ housings led to an initial leakage of 0.4 L/min, which increased to only 1.2 L/min after 10,000 load cycles. Overall, the PU material used was more difficult to process but also more abrasion- and tear-resistant than the SIL elastomer. Research limitations/implications More work is needed to understand individual cause–effect relationships between specific design features and system behavior. Originality/value To date, pneumatic RVAs have been manufactured by large-scale production technologies. The absence of suitable prototyping strategies has limited the available range to fixed sizes and has thus complicated the use of RVAs in research and product development. This paper proves that functional pneumatic RVAs can be produced by using more accessible manufacturing technologies and provides the tools for prototyping of application-specific RVAs.

Comparing parallel and iterative prototyping strategies during engineering design

Comparing parallel and iterative prototyping strategies during engineering design