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The modularity of a product architecture with standard, variant, and optional modules can be measured by the characteristics of commonality and combinability. Positive and negative effects of a more communal or more combinable structure are summarized and visualized in an impact model. However, due to the megatrend of personalization, the solution space of a modular product architecture needs to be extended to include personalizable modules. What remains unclear till now is how personalization impacts the different life phases. Therefore, this article derives an impact model considering product personalization. First, the modularity property of personalizability is derived, in order to then specifically investigate the effects occurring in the different life phases. Therefore, a literature review is conducted. New effects are found, and the existing effects of commonality and combinability are examined for their validity for personalizability. The findings are then combined with the known effects of commonality and combinability to create a holistic impact model of modular product families. This new model takes personalizable modules into account and can support companies in defining the goals and focus of a modularization project. [ABSTRACT FROM AUTHOR]

Designing mechatronic products requires interdisciplinary skills and as products become more complex, the design of mechatronic systems plays a critical role. To minimize waste production and pollution, a shift toward a circular economy is necessary, with mechatronic products being particularly impacted by such policies. Repairing plays a key part in achieving a circular economy. Through repairability, the product lifespan can be extended, and combined with maintenance the rate of product replacement can be reduced. Within this context, the goal of this paper is to propose a design methodology (based on the EN 45554:2020 standard) for generating and implementing eco-design rules for disassembly and repair. The methodology has four phases, the first one is the identification of target components (those that are more likely to fail during the lifespan). The second phase encompasses the experimental disassembly analysis which can be manual or virtual. The third phase is the assessment of the disassemblability index which includes the analysis of parameters that affect the disassembly phase. The last phase is the implementation of the eco-design methodology for all the components that do not meet the minimum repairability requirements. A case study of electro-mechanical ovens is presented, targeting replaceable components. The results show that the use of this framework and the eco-design actions derived from it are successful in improving the repairability of the product and increasing the disassemblability index (30% on average) through a virtual analysis. A sensitivity analysis has been conducted to study the impact of parameter weight modification. This research contributes to advancing repairability and supporting the circular economy paradigm in mechatronic product design. [ABSTRACT FROM AUTHOR]

Remanufacturing is a process that returns end-of-life equipment to as-new conditions and offers numerous environmental and economic benefits. To fully capitalize on remanufacturing, its synergistic interactions with design must be fully realized and addressed during the design stage. Although this fact is widely recognized in the literature, most of the current studies focus primarily either on the design or remanufacturing aspects of design for remanufacturing (DfRem). In an effort to offer a more integrated DfRem approach than those reported in the literature, we propose a new combined design and remanufacturing optimization (reman co-design) framework that takes a holistic approach by leveraging the intricate interplay between design and remanufacturing. The aim of this formulation is to identify the optimal decisions that maximize the benefits of remanufacturing throughout the entire lifespan of a product. To showcase the utility of the new formulation, we are using a case study of a hydraulic manifold, (re)manufactured by John Deere. Using this industry example, we compare the results of reman co-design to the ones from a decoupled remanufacturing design approach. Results reveal that remanufacturing benefits are better realized and improved upon when using the developed reman co-design approach. [ABSTRACT FROM AUTHOR]

As additive manufacturing (AM) usage increases, designers who wish to maximize AM's potential must reconsider the traditional manufacturing (TM) axioms they may be more familiar with. While research has previously investigated the potential influences that can affect the designs produced in concept generation, little research has been done explicitly targeting the manufacturability of early-stage concepts and how previous experience and the presenting of priming content in manufacturing affect these concepts. The research in this paper addresses this gap in knowledge, specifically targeting differences in concept generation due to designer experience and presenting design for traditional manufacturing (DFTM) and design for additive manufacturing (DFAM) axioms. To understand how designers approach design creation early in the design process and investigate potential influential factors, participants in this study were asked to complete a design challenge centered on concept generation. Before this design challenge, a randomized subset of these participants received priming content on DFTM and DFAM considerations. These participants' final designs were evaluated for both traditional manufacturability and additive manufacturability and compared against the final designs produced by participants who did not receive the priming content. Results show that students with low manufacturing experience levels create designs that are more naturally suited for TM. Additionally, as designers' manufacturing experience levels increase, there is an increase in the number of designs more naturally suited for AM. This correlates with a higher self-reported use of DFAM axioms in the evaluation of these designs. These results suggest that students with high manufacturing experience levels rely on their previous experience when it comes to creating a design for either manufacturing process. Lastly, while the manufacturing priming content significantly influenced the traditional manufacturability of the designs, the priming content did not increase the number of self-reported design for manufacturing (DFM) axioms in the designs. [ABSTRACT FROM AUTHOR]

Generative design (GD) techniques have been proposed to generate numerous designs at early design stages for ideation and exploration purposes. Previous research on GD using deep neural networks required tedious iterations between the neural network and design optimization, as well as post-processing to generate functional designs. Additionally, design constraints such as volume fraction could not be enforced. In this paper, a two-stage non-iterative formulation is proposed to overcome these limitations. In the first stage, a conditional generative adversarial network (cGAN) is utilized to control design parameters. In the second stage, topology optimization (TO) is embedded into cGAN (cGAN + TO) to ensure that desired functionality is achieved. Tests on different combinations of loss terms and different parameter settings within topology optimization demonstrated the diversity of generated designs. Further study showed that cGAN + TO can be extended to different load and boundary conditions by modifying these parameters in the second stage of training without having to retrain the first stage. Results demonstrate that GD can be realized efficiently and robustly by cGAN+TO. [ABSTRACT FROM AUTHOR]

Product personalization will play a key role in the future of society by making these products available for everyone, everywhere. Personalized production requires the involvement of the customer in the design process. Thus, tools to identify which components and modules allow for customer interaction are needed, and to further assess the effects of customer interaction on the product design and the manufacturing system. In this article, we propose a framework to analyze the extent of personalization a designer and a manufacturing firm can achieve in the context of efficient personalized production. We study this as a dyad of product design and manufacturing system, which uses two main attributes: product modularity and manufacturing configuration complexity. To demonstrate our ideas, we use two applications. The first is a case study for gearbox, usually manufactured at high levels of efficiency under the mass production paradigm. The second case is an application for the footwear industry for the particular case of a sneaker, a sports shoe that is prone to be customized. These case studies show the flexibility of this framework to a wide set of industries. In both cases, we compare a personalization scenario with a baseline scenario and provide managerial insights. [ABSTRACT FROM AUTHOR]

Path tracking error control is an essential functionality in the development of autonomous vehicles to follow a planned trajectory. Significant path tracking errors could lead to a collision or even out of the control of the vehicle. Model-based control strategies have been developed to minimize the vehicle's path tracking errors. However, the vehicle model may not truly represent the actual vehicle dynamics. Furthermore, the parameters employed in the vehicle dynamic model may not represent the actual operating conditions of the vehicle under environmental uncertainty. This paper proposes a real-time bias-learning method coupling with the model predictive control (MPC) to improve the fidelity of a baseline vehicle model with the aid of a few experiments (or virtual experiments) so that the path tracking error can be reduced in real-time operation. Gaussian process (GP) regression and recurrent neural network (RNN) are employed for bias-learning and their effectiveness are compared under different scenarios. GP regression learns non-linearity of the model bias through its nonlinear kernel function, whereas the RNN model formulates the bias as a linear combination of hidden nodes which capture the non-linearity of the model bias with a recurrent form. Results reveal that RNN is more effective for real-time learning of the nonlinear model bias than the classical GP regression and the proposed bias-learning model is able to improve the fidelity of a baseline vehicle dynamic model. Consequently, path tracking performance can be greatly improved under environmental uncertainty using the bias-learning-based MPC. [ABSTRACT FROM AUTHOR]

Designers should adequately develop user considerations such as usability, safety, and comfort during the design process of new systems. Nevertheless, incorporating human factors engineering principles during early design phases is not simple. The objective of this work is to assist designers in implementing human factors engineering principles during early design phases using a functional model framework. This effort expands our previous work on automating the function-human error design method (FHEDM) implementation. In this work, we use data mining techniques in a design repository to explore the construction of association rules between components, functions, flows, and user interactions. Such association rules can support designers assessing user-system interactions during the early design stages. To validate this approach, we compare the associations generated by expert designers using the FHEDM while designing a new product to those generated by an algorithm using the repository data. The results show notable similarities between the associations extracted by the algorithm and the associations identified by designers. Thus, the overall results show that association rules extracted from a rich dataset can be used to distinguish user-product interactions, demonstrating the potential of automating the identification of user-product interactions from a functional model. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is used to produce load-bearing, safety-critical components in industries like aerospace, automotive, and medical devices. Designers can create AM components with complex internal features, organic topologies, and lattice structures to reduce part mass or part count. However, such complex features can make designs difficult or impossible to inspect using mature nondestructive testing (NDT) methods. Professional organizations suggest designers keep quality assurance and quality control (QA/QC) in mind early in the design process. The Design for Inspectability (DfI) framework is suggested as a way of meeting the need for early-stage QA/QC considerations. This work presents a case study, where a group of designers considered one type of NDT, known as Pulse-Echo Ultrasonic (PEU) testing. Using heuristics derived from relevant literature, designers were able to create designs with increased inspectability. This improved inspectability came at the cost of other design objectives, however, such as strength and mass. This implies that certain design objectives may be inversely related to increased inspectability, raising significant concerns for the field. This work marks the first step toward mapping out the trade-offs between inspection and performance objectives. [ABSTRACT FROM AUTHOR]

The fuzzy front end of engineering design can present a difficult challenge, and as such, recent engineering design research has focused on guiding and influencing the way a designer ideates. Early ideation can be especially difficult when attempting to integrate specific design objectives in product design, called Design for X (DfX). This paper presents two experiments exploring the efficacy of a structured Design for the Environment (DfE) design method called the Guidelines and Regulations for Early design for the Environment (GREEn) Quiz that provides designers with sustainable design knowledge during the conceptual design phase. The GREEn Quiz operates on a web-based platform and queries the designer about their design concepts; an end-of-quiz report provides abstract DfE knowledge to designers. While this abstract knowledge was able to be applied by designers in a former study, we hypothesize that providing targeted, specific design strategies during conceptual design will enable novice designers to better integrate DfE. In this study, we created these DfE strategies, integrated these into the GREEn Quiz, and studied the efficacy of these strategies when presented to designers at both the expert and novice levels. Results suggest that respondents with access to the strategy-based GREEn Quiz produced concepts with evidence of a broader range of sustainable design decisions and higher solution quality scores. This work shows the promise of supplemental DfE methods for concept generation to enable the design of more environmentally sustainable products. [ABSTRACT FROM AUTHOR]

With limited time and resources available to carry out Engineering for Global Development (EGD) projects, it can be difficult to know where those resources should be allocated to have greater potential for meaningful impact. It is easy to assume that projects should occur in a particular location based on personal experience or where other development projects are taking place. This can be a consideration, but it may not lead to the greatest social impact. Where to work on a project and what problem to work on are key questions in the early stages of product development in the context of EGD. To aid in this process, this article presents a method for assessing global needs to ensure thoughtful use of limited EGD resources. We introduce a method for identifying locations where there is human need, gaps in technological achievement, and what the work environment is in a country. Results of the method are compared to what countries receive the most foreign aid dollars per capita. Measures were calculated using the principal component analysis on data from development agencies. These results can help practitioners in selecting where to undertake development projects with an eye toward targeting locations that may yield high levels of social impact. [ABSTRACT FROM AUTHOR]

When designing for additive manufacturing (AM), designers often need assistance in breaking out of their conventional manufacturing mind-set. Previously, the authors derived design heuristics for AM (DHAM) to assist designers in doing this during the early phases of the design process. This study proposes a set of 25 multimodal cards and objects to accompany each of the design heuristics for AM and studies their effect through a series of controlled, novice user studies conducted using both teams and individuals who redesign a city E-Bike. The resulting AM concepts are analyzed in terms of the quantity of design modifications relevant for AM, AM flexibility, novelty, and variety. It is found that the DHAM cards and objects increase the inclusion of AM concepts, AM modifications, and the unique capabilities of AM in the concepts generated by both individuals and teams. They also increase the creativity of the concepts generated by both individuals and teams, as measured through a series of defined metrics. Furthermore, the objects in combination with the cards are more effective at stimulating the generation of a wider variety of designs than the cards alone. Future work will focus on studying the use of the DHAM cards and objects in an industrial setting. [ABSTRACT FROM AUTHOR]

Industry 4.0, as the fourth industrial revolution, represents significant challenges and numerous innovation opportunities for future product realization. A critical area of Industry 4.0 is the advancement of new design theories, design methods, and design principles to drive and enable the revolution with designers, engineers, teams, and organizations. This paper focuses on the advancement of a design theory and design principles for a growing manufacturing capability for Industry 4.0: additive manufacturing (AM). With high degrees of freedom, the field and use of AM requires design guidance and highly practical knowledge for supporting ideation processes, enabling understanding of capabilities, and creating a basis to innovative with the technology. Some design principles for AM exist in the literature; however, designers seek more fundamental and practical design guidelines for successfully creating and building their customized design artefacts, especially as Industry 4.0 moves forward. In this study, a crowdsourced repository for additively manufacturable components is used as the source of design data, within an empirical study, to extract practical design principles for AM. A total of 23 crowdsourced design principles for AM are extracted and clustered according to level specificity: (i) design for manufacturing, (ii) design for digital manufacturing, (iii) design for AM, and (iv) design for fused deposition modeling. These 23 AM design principles, as a foundation for AM design and Industry 4.0, are provided in a common framework; expressed for ready use by designers, developers, and researchers; and illustrated through some contemporary designs. [ABSTRACT FROM AUTHOR]

Cyber-physical-social systems (CPSS) with highly integrated functions of sensing, actuation, computation, and communication are becoming the mainstream consumer and commercial products. The performance of CPSS heavily relies on the information sharing between devices. Given the extensive data collection and sharing, security and privacy are of major concerns. Thus, one major challenge of designing those CPSS is how to incorporate the perception of trust in product and systems design. Recently, a trust quantification method was proposed to measure the trustworthiness of CPSS by quantitative metrics of ability, benevolence, and integrity. The CPSS network architecture can be optimized by choosing a subnet such that the trust metrics are maximized. The combinatorial network optimization problem, however, is computationally challenging. Most of the available global optimization algorithms for solving such problems are heuristic methods. In this paper, a surrogate-based discrete Bayesian optimization method is developed to perform network design, where the most trustworthy CPSS network with respect to a reference node is formed to collaborate and share information with. The applications of ability and benevolence metrics in design optimization of CPSS architecture are demonstrated. [ABSTRACT FROM AUTHOR]

Prior research suggests that excess (purposeful inclusion of margin beyond what is required for known system uncertainties) can limit change propagation and reduce system modifications. Reducing change costs increases system flexibility, permitting adaptions that satisfy uncertain future requirements. The benefits of excess, however, must be traded against higher costs of the initial system and likely performance decreases. Assessing the benefits and costs of excess requires evaluating what forms, locations, and magnitudes of excess inclusion are optimal. This paper improves the state-of-the-art in two ways. First, prior research has generally assessed excess in system-level properties (aggregating component properties into a single metric). The approach presented in this paper extends excess assessment to the component level so that the effects of excess on change propagation may be explicitly captured. Second, this approach holistically assesses the value of excess by evaluating both its costs and benefits. The approach borrows from Decision-Based Design and Model Based System Engineering (MBSE) in creating a generic modeling method capable of excess valuation. A desktop computer example is used for demonstrating how excess is valued in a system and the potential gains associated with excess inclusion when mining cryptocurrency. A single component optimization of the power supply capacity for the desktop is assessed to be 750 W, which balances the initial cost against the future flexibility. A system-level optimization then demonstrates the identification of critical change propagation pathways and illuminates both where and how excess may be included to inhibit change propagation. This key component was identified as the motherboard-central processing unit (CPU) slot in the tested systems. [ABSTRACT FROM AUTHOR]

Networking complex sociotechnical systems into larger Systems of Systems (SoS) typically results in improved performance characteristics including sustainability, efficiency, and productivity. The response, or lack thereof, of many SoS to unexpected constituent system failures undermines their effectiveness in many cases. SoS performance after faults can be improved by improving the SoS's hard (physical design) or soft (human intervention) resilience. The current approaches to increase resilience are limited due to the cost and necessary of human response increasing non-linearly with SoS scale. The limitations of current approaches require a novel design approach to improve SoS network resilience. We hypothesize that biologically inspired network design can improve SoS resilience. To illustrate this, a systems dynamics model of a Forestry Industry is presented and an optimization search over potential hard and soft resilience approaches is compared to a biologically inspired network improvement. SoS network resilience is measured through the newly developed System of System Resilience Measurement (SoSRM). Our first result provides evidence that biologically inspired network design provides an approach to increase SoS resilience beyond hard and soft resilience improvements alone. Second, this work provides evidence that having a SoS constituent fulfill the ecosystem role of detrital actor increases resilience. Third, this paper documents the first case study using the new SoSRM metric to justify a design decision. Finally, this case study provides a counter-example to the theory that increased sustainability always results in increased resilience. By comparing biologically inspired network redesign and optimized traditional resilience improvements, this paper provides evidence that biologically inspired intervention may be the needed strategy to increase sociotechnical SoS network resilience, improve SoS performance, and overcome the limitations of traditional resilience improvement approaches. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) enables engineers to improve the functionality and performance of their designs by adding complexity at little to no additional cost. However, AM processes also exhibit certain unique limitations, such as the presence of support material. These limitations must be accounted for to ensure that designs can be manufactured feasibly and cost-effectively. Given these unique process characteristics, it is important for an AM-trained workforce to be able to incorporate both opportunistic and restrictive design for AM (DfAM) considerations into the design process. While AM/DfAM educational interventions have been discussed in the literature, few studies have objectively assessed the integration of DfAM in student engineering designers' design outcomes. Furthermore, limited research has explored how the use of DfAM affects the students' AM designs' achievement of design task objectives. This research explores this gap in literature through an experimental study with 301 undergraduate students. Specifically, participants were exposed to either restrictive DfAM or dual DfAM (both opportunistic and restrictive) and then asked to participate in a design challenge. The participants' final designs were evaluated for (1) build time and build material (2) the use of the various DfAM concepts, and (3) the features used to manifest these DfAM concepts. The results show that the use of certain DfAM considerations, such as part complexity, number of parts, support material mass, and build plate contact area (corresponding to warping tendency), correlated with the build material and build time of the AM designs--minimizing both of which were objectives of the design task. The results also show that introducing participants to opportunistic DfAM leads to the generation of designs with higher part complexity and lower build plate contact area but a greater presence of inaccessible support material. [ABSTRACT FROM AUTHOR]

Though academic research for identifying and considering the social impact of products is emerging, additional insights can be gained from engineers who design products every day. This paper explores current practices in industries used by design engineers to consider the social impact of products. Forty-six individuals from 34 different companies were interviewed to discover what disconnects exist between academia and industry when considering a product's social impact. These interviews were also used to discover how social impact might be considered in a design setting moving forward. This is not a study to find "the state of the art," but considers the average engineering professional's work to design products in various industries. Social impact assessments (SIA) and social life cycle assessments (SLCA) are two of the most common processes discussed in the literature to evaluate social impact, both generally and in products. Interestingly, these processes did not arise in any discussion in interviews, despite respondents affirming that they do consider social impact in the product design. Processes used to predict social impact, rather than simply evaluate, were discussed by the respondents. These tended to be developed within the company and often related to industry imposed government regulations. To build on this study, the findings herein should be further validated for executives, managers, and engineers. A study specific to these roles should be designed to understand the disconnect better. Additionally, processes should be developed to assist engineers in considering the social impact of their products. Work should also be done to help educate engineers and their leaders on the value of considering the social impact in product design. [ABSTRACT FROM AUTHOR]

Process plans in additive manufacturing (AM) have a profound impact on the performance of fabricated parts such as geometric accuracy and mechanical properties. Due to its layer-based, additive nature, AM processes can be controlled at multiple scales starting from the scan vector/pixel scale. However, most process planning methods in AM configure process settings at the part scale. This leaves large unexplored regions in the design space that may include optimal designs. To address these untapped potentials, we present a process planning strategy based on the concept of manufacturing elements (MELs) to harness process variables at low scales for design. First, we decompose a part design into multiple MELs that contain geometric and manufacturing information. Two-scale process-structure-property (PSP) relationships are then constructed for MELs and their assembly. Decision tools, including the compromise decision support problem, are employed to navigate two-scale PSP relationships for supporting designers in design exploration on process variables and optimization of process plans. The proposed strategy is illustrated with a process planning example for a lattice structure, which has multiple design goals and is to be fabricated using material extrusion. [ABSTRACT FROM AUTHOR]

Engineered products often have more social impacts than are realized. A product review was conducted to bring this to light. In this paper, we show the extent to which different social impacts in 11 impact categories are co-present in 150 products and how this can help engineers and others during the product development process. Specifically, we show how social impact categories not previously considered can be identified. The product review resulted in 13,200 data points that were divided into two data sets, one with 8800 data points from which a social impact probability table was created. The remaining data points were then used to validate the table. All data points were then combined to create a final social impact probability table. This table provides insight for how various social impact categories correlate and can assist engineers in expanding their views to include additional social impact objectives and thus achieve a design with broader social impact or a design with minimized unwanted negative social impact. A simple method for predicting social impact is also created in order to assist engineers when developing products with social impacts in mind. [ABSTRACT FROM AUTHOR]

All products impact the lives of their users, this is called social impact. Some social impacts are commonly recognized by the engineering community, such as impacts to a user's health and safety, while other social impacts can be more difficult to recognize, such as impacts on families and gender roles. When engineers make design decisions, without considering social impacts, they can unknowingly cause negative social impacts. Even harming the user and/or society. Despite its challenges, measuring a program's or policy's social impact is a common practice in the field of social sciences. These measurements are made using social impact indicators, which are simply the things observed to verify that true progress is being made. While there are clear benefits to predicting the social impact of an engineered product, it is unclear how engineers should select indicators and build predictive social impact models that are functions of engineering parameters and decisions. This paper introduces a method for selecting social impact indicators and creating predictive social impact models that can help engineers predict and improve the social impact of their products. As a first step in the method, an engineer identifies the product's users, objectives, and requirements. Then, the social impact categories that are related to the product are determined. From each of these categories, the engineer selects several social impact indicators. Finally, models are created for each indicator to predict how a product's parameters will change these indicators. The impact categories and indicators can be translated into product requirements and performance measures that can be used in product development processes. This method is used to predict the social impact of the proposed, expanded U.S. Mexico border wall. [ABSTRACT FROM AUTHOR]

Creating product ecosystems has been one of the strategic ways to enhance user experience and business advantages. Among many, customer needs analysis for product ecosystems is one of the most challenging tasks in creating a successful product ecosystem from both the perspectives of marketing research and product development. In this paper, we propose a machine-learning approach to customer needs analysis for product ecosystems by examining a large amount of online user-generated product reviews within a product ecosystem. First, we filtered out uninformative reviews from the informative reviews using a fastText technique. Then, we extract a variety of topics with regard to customer needs using a topic modeling technique named latent Dirichlet allocation. In addition, we applied a rule-based sentiment analysis method to predict not only the sentiment of the reviews but also their sentiment intensity values. Finally, we categorized customer needs related to different topics extracted using an analytic Kano model based on the dissatisfaction-satisfaction pair from the sentiment analysis. A case example of the Amazon product ecosystem was used to illustrate the potential and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]