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Abstract Computational approaches, especially finite element analysis (FEA), have been rapidly growing in both academia and industry during the last few decades. FEA serves as a powerful and efficient approach for simulating real-life experiments, including industrial product development, machine design, and biomedical research, particularly in biomechanics and biomaterials. Accordingly, FEA has been a “go-to” high biofidelic software tool to simulate and quantify the biomechanics of the foot–ankle complex, as well as to predict the risk of foot and ankle injuries, which are one of the most common musculoskeletal injuries among physically active individuals. This paper provides a review of the in silico FEA of the foot–ankle complex. First, a brief history of computational modeling methods and finite element (FE) simulations for foot–ankle models is introduced. Second, a general approach to build an FE foot and ankle model is presented, including a detailed procedure to accurately construct, calibrate, verify, and validate an FE model in its appropriate simulation environment. Third, current applications, as well as future improvements of the foot and ankle FE models, especially in the biomedical field, are discussed. Finally, a conclusion is made on the efficiency and development of FEA as a computational approach in investigating the biomechanics of the foot–ankle complex. Overall, this review integrates insightful information for biomedical engineers, medical professionals, and researchers to conduct more accurate research on the foot–ankle FE models in the future. 

Additive manufacturing (AM) processes present designers with creative freedoms beyond the capabilities of traditional manufacturing processes. However, to successfully leverage AM, designers must balance their creativity against the limitations inherent in these processes to ensure the feasibility of their designs. This feasible adoption of AM can be achieved if designers learn about and apply opportunistic and restrictive design for AM (DfAM) techniques at appropriate stages of the design process. Researchers have demonstrated the effect of the order of presentation of information on the learning and retrieval of said information; however, there is a need to explore this effect within DfAM education. In this paper, we explore this gap through an experimental study involving 195 undergraduate engineering students. Specifically, we compare two variations in DfAM education: (1) opportunistic DfAM followed by restrictive DfAM, and (2) restrictive DfAM followed by opportunistic DfAM, against only opportunistic DFAM and only restrictive DfAM training. These variations are compared through (1) differences in participants’ DfAM self-efficacy, (2) their self-reported DfAM use, and (3) the creativity of their design outcomes. From the results, we see that only students trained in opportunistic DfAM, with or without restrictive DfAM, present a significant increase in their opportunistic DfAM self-efficacy. However, all students trained in DfAM – opportunistic, restrictive, or both – demonstrated an increase in their restrictive DfAM self-efficacy. Further, we see that teaching restrictive DfAM first followed by opportunistic DfAM results in the generation of ideas with greater creativity – a novel research finding. These results highlight the need for educators to accountfor the effects of the order of presenting content to students, especially when educating students about DfAM. 

The capabilities of additive manufacturing (AM) open up designers’ solution space and enable them to build designs previously impossible through traditional manufacturing. To leverage AM, designers must not only generate creative ideas, but also propagate these ideas without discarding them in the early design stages. This emphasis on selecting creative ideas is particularly important in design for AM (DfAM), as ideas perceived as infeasible through the traditional design for manufacturing lens could now be feasible with AM. Several studies have discussed the role of DfAM in encouraging creative idea generation; however, there is a need to understand concept selection in DfAM. In this paper, we investigated the effect of two variations in DfAM education: 1) restrictive DfAM and 2) dual DfAM (opportunistic and restrictive) on students’ concept selection process. Specifically, we compared the creativity of the concepts generated by the students to the creativity of the concepts selected by them. Further, we performed qualitative analyses to explore the rationale provided by the students in making these design decisions. From the results, we see that teams from both educational groups select ideas of greater usefulness; however, only teams from the restrictive DfAM group select ideas of higher uniqueness and overall creativity. Further, we see that introducing students to opportunistic DfAM increases their emphasis on the complexity of designs when evaluating and selecting them. These results highlight the need for DfAM education to encourage AM designers to not just generate but also select creative ideas. 

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, which 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, limited research has investigated the effect of these interventions on students’ use of DfAM. Furthermore, limited research has explored how DfAM use affects the performance of students’ AM designs. This research explores this gap through an experimental study with 123 undergraduate students. Specifically, participants were exposed to either restrictive DfAM or dual DfAM (both opportunistic and restrictive) and then asked to participate in an AM design challenge. The students’ final designs were evaluated for (1) performance with respect the design objectives and constraints, and (2) the use of the various aspects of DfAM. The results showed that the use of certain DfAM considerations, such as minimum feature size and support material mass, successfully predicted the performance of the AM designs. Further, while the variations in DfAM education did not influence the performance of the AM designs, it did have an effect on the students’ use of certain DfAM concepts in their final designs. These results highlight the influence of DfAM education in bringing about an increase in students’ use of DfAM. Moreover, the results demonstrate the potential influence of DfAM in reducing build time and build material of the students’ AM designs, thus improving design performance and manufacturability. 

Research in additive manufacturing (AM) has increased the use of AM in many industries, resulting in a commensurate need for a workforce skilled in AM. In order to meet this need, educational institutions have undertaken different initiatives to integrate design for additive manufacturing (DfAM) into the engineering curriculum. However, limited research has explored the impact of these educational interventions in bringing about changes in the technical goodness of students’ design outcomes, particularly through the integration of DfAM concepts in an engineering classroom environment. This study explores this gap using an experimental study with 193 participants recruited from a junior-level course on mechanical engineering design. The participants were split into three educational intervention groups: (1) no DfAM, (2) restrictive DfAM, and (3) restrictive and opportunistic (dual) DfAM. The effects of the educational intervention on the participants’ use of DfAM were measured through changes in (1) participants’ DfAM self-efficacy, (2) technical goodness of their AM design outcomes, and (3) participants’ use of DfAM-related concepts when describing and evaluating their AM designs. The results showed that while all three educational interventions result in similar changes in the participants’ opportunistic DfAM self-efficacy, participants who receive only restrictive DfAM inputs show the greatest increase in their restrictive DfAM self-efficacy. Further, we see that despite these differences, all three groups show a similar decrease in the technical goodness of their AM designs, after attending the lectures. A content analysis of the participants’ design descriptions and evaluations revealed a simplification of their design geometries, which provides a possible explanation for the decrease in their technical goodness, despite the encouragement to utilize the design freedom of AM to improve functionality or optimize the weight of the structure. These results emphasize the need for more in-depth DfAM education to encourage the use of both opportunistic and restrictive DfAM during student design challenges. The results also highlight the possible influence of how the design problem is stated on the use of DfAM in solving it. 

The integration of additive manufacturing (AM) processes in many industries has led to the need for AM education and training, particularly on design for AM (DfAM). To meet this growing need, several academic institutions have implemented educational interventions, especially project- and problem-based, for AM education; however, limited research has explored how the choice of the problem statement influences the design outcomes of a task-based AM/DfAM intervention. This research explores this gap in the literature through an experimental study with 222 undergraduate engineering students. Specifically, the study compared the effects of restrictive and dual (restrictive and opportunistic) DfAM education, when introduced through either a simple or complex design task. The effects of the intervention were measured through (1) changes in student DfAM self-efficacy, (2) student self-reported emphasis on DfAM, and (3) the creativity of student AM designs. The results show that the complexity of the design task has a significant effect on the participants’ self-efficacy with, and self-reported emphasis on, certain DfAM concepts. The results also show that the complex design task results in participants generating ideas with greater median uniqueness compared to the simple design task. These findings highlight the importance of the chosen problem statement on the outcomes of a DfAM educational intervention, and future work is also discussed. 

This paper presents a retrospective of the benchmark testing methodologies developed and accumulated into the stretch sensor tool kit (SSTK) by the research team during the Closing the Wearable Gap series of studies. The techniques developed to validate stretchable soft robotic sensors (SRS) as a means for collecting human kinetic and kinematic data at the foot-ankle complex and at the wrist are reviewed. Lessons learned from past experiments are addressed, as well as what comprises the current SSTK based on what the researchers learned over the course of multiple studies. Three core components of the SSTK are featured: (a) material testing tools, (b) data analysis software, and (c) data collection devices. Results collected indicate that the stretch sensors are a viable means for predicting kinematic data based on the most recent gait analysis study conducted by the researchers (average root mean squared error or RMSE = 3.63°). With the aid of SSTK defined in this study summary and shared with the academic community on GitHub, researchers will be able to undergo more rigorous validation methodologies of SRS validation. A summary of the current state of the SSTK is detailed and includes insight into upcoming experiments that will utilize more sophisticated techniques for fatigue testing and gait analysis, utilizing SRS as the data collection solution. 

Background: An induced loss of balance resulting from a postural perturbation has been reported as the primary source for postural instability leading to falls. Hence; early detection of postural instability with novel wearable sensor-based measures may aid in reducing falls and fall-related injuries. The purpose of the study was to validate the use of a stretchable soft robotic sensor (SRS) to detect ankle joint kinematics during both unexpected and expected slip and trip perturbations. Methods: Ten participants (age: 23.7 ± 3.13 years; height: 170.47 ± 8.21 cm; mass: 82.86 ± 23.4 kg) experienced a counterbalanced exposure of an unexpected slip, an unexpected trip, an expected slip, and an expected trip using treadmill perturbations. Ankle joint kinematics for dorsiflexion and plantarflexion were quantified using three-dimensional (3D) motion capture through changes in ankle joint range of motion and using the SRS through changes in capacitance when stretched due to ankle movements during the perturbations. Results: A greater R-squared and lower root mean square error in the linear regression model was observed in comparing ankle joint kinematics data from motion capture with stretch sensors. Conclusions: Results from the study demonstrated that 71.25% of the trials exhibited a minimal error of less than 4.0 degrees difference from the motion capture system and a greater than 0.60 R-squared value in the linear model; suggesting a moderate to high accuracy and minimal errors in comparing SRS to a motion capture system. Findings indicate that the stretch sensors could be a feasible option in detecting ankle joint kinematics during slips and trips. 

The linearity of soft robotic sensors (SRS) was recently validated for movement angle assessment using a rigid body structure that accurately depicted critical movements of the foot–ankle complex. The purpose of this study was to continue the validation of SRS for joint angle movement capture on 10 participants (five male and five female) performing ankle movements in a non-weight bearing, high-seated, sitting position. The four basic ankle movements—plantar flexion (PF), dorsiflexion (DF), inversion (INV), and eversion (EVR)—were assessed individually in order to select good placement and orientation configurations (POCs) for four SRS positioned to capture each movement type. PF, INV, and EVR each had three POCs identified based on bony landmarks of the foot and ankle while the DF location was only tested for one POC. Each participant wore a specialized compression sock where the SRS could be consistently tested from all POCs for each participant. The movement data collected from each sensor was then compared against 3D motion capture data. R-squared and root-mean-squared error averages were used to assess relative and absolute measures of fit to motion capture output. Participant robustness, opposing movements, and gender were also used to identify good SRS POC placement for foot–ankle movement capture.