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Topology optimization (TO) has been a useful engineering tool over the last decades. The benefits of this optimization method are several, such as the material and cost savings, the design inspiration, and the robustness of the final products. In addition, there are educational benefits. TO is a combination of mathematics, design, statics, and the finite element method (FEM); thus, it can provide an integrative multi-disciplinary knowledge foundation to undergraduate students in engineering. This paper is focused on the educational contributions from TO and identifies effective teaching methods, tools, and exercises that can be used for teaching. The result of this research is the development of an educational framework about TO based on the CDIO (Conceive, Design, Implement, and Operate) Syllabus for CAD engineering studies at universities. TO could be easily adapted for CAD designers in every academic year as an individual course or a module of related engineering courses. Lecturers interested in the introduction of TO to their courses, as well as engineers and students interested in TO in general, could use the findings of this paper.

This paper demonstrates the unpredictability of outcomes that result from compounding variabilities when using generative design (GD) coupled with additive manufacturing (AM). AM technologies offer the greatest design freedom and hence are most able to leverage the full capability of generative design (GD) tools and thus maximize potential improvements, such as weight, waste and cost reduction, strength, and part consolidation. Implicit in all studies reported in the literature is the fundamental assumption that the use of GD, irrespective of user experience or approach followed, yields high-performing and/or comparable design outputs. This work demonstrates the contrary and shows that achieving high performance with GD tools requires careful consideration of study setup and initial conditions. It is further shown that, when coupled with the inherent variability of AM parts, the potential variation in the performance of the design output can be significant, with poorer designs achieving only a fraction of that of higher-performing designs. This investigation shows how AM by Material Extrusion (MEX), which is used to manufacture components with polylactic acid (PLA), varies through different design pathways, bridging MEX and GD. Through a practical study across nine independently generated designs, the breadth of performance—due to initial GD conditions and MEX part strength unpredictability—is shown to reach 592%. This result suggest that current GD tools, including their underlying workflows and algorithms, are not sufficiently understood for users to be able to generate consistent solutions for an input case. Further, the study purports that training and consideration on GD setup are necessary to apply GD toolsets to achieve high-performing designs, particularly when applied in the context of MEX. [ABSTRACT FROM AUTHOR]

This paper presents the experienced difficulties of students participating in the multidisciplinary, remote collaborating engineering design course challenge-based innovation at CERN. This is with the aim to identify learning barriers and improve future learning experiences. We statistically analyse the rated differences between distinct design activities, educational background and remote vs. co-located collaboration. The analysis is based on a quantitative and qualitative questionnaire (N = 37). Our analysis found significant ranking differences between remote and co-located activities. This questions whether the remote factor might be a barrier for the originally intended learning goals. Further a correlation between analytical and converging design phases was identified. Hence, future facilitators are suggested to help students in the transition from one design phase to the next rather than only teaching methods in the individual design phases. Finally, we discuss how educators address the identified learning barriers when designing future courses including multidisciplinary or remote collaboration.

Additive manufacturing (AM) offers new possibilities in soft robotics as materials can easily be combined in multi-material designs. Proper sensing is essential for the soft actuators to interact with the surroundings successfully. By fabricating sensors through AM, sensors can be embedded directly into the components during manufacturing. This paper investigates NinjaTek Eels electrical resistance response to strain and the feasibility of using the material to create strain sensors. Strain sensors were 3D-printed out of NinjaTek Eel, a soft conductive TPU, and was tested during cyclic loading. A custom resistance–strain test rig was developed for measuring sensor behavior. The rig was calibrated for electric resistance, able to measure electric resistance as a function of strain. A parabolic response curve was observed during cyclic loading, which led to ambiguous readings. A 10-specimen validation test was conducted, evaluating the statistical variation for the first 100 loading cycles. The validation test showed that the sensor is capable of accurate and predictable readings during single load cases and cyclic loading, with the overall root mean square error being 66.9 Ω. Combining two sensors of different cross-sections gave promising results in terms of calibrating. By monitoring load cycles and strain rates, calibration can also be achieved by machine learning models by the microcontroller used to extract data. The presented work in this article explores the potential of using conductive TPUs as sensors embedded in products such as soft robotics, life monitoring of products with structural, and digital twins for live product to user feedback. [ABSTRACT FROM AUTHOR]

Due to environmental concerns regarding single-use plastic materials, major efforts are being made to develop new material concepts based on biodegradable and renewable resources, e.g., wood pulp. In this study, we assessed two types of wood pulp fibres, i.e., thermomechanical pulp (TMP) and Kraft pulp fibres, and tested the performance of the fibres in wet-moulding and thermopressing trials. Kraft pulp fibres appeared to retain more water than TMP, increasing the dewatering time during wet-moulding and apparently increasing the compression resistance of the pulp during thermoforming. Additionally, cellulose nanofibres (CNF) were added to the pulps, which improved the mechanical properties of the final thermopressed specimens. However, the addition of CNF to the pulps (from 2 to 6%) had a further decrease in the dewatering efficiency in the wet-moulding process, and this effect was more pronounced in the Kraft pulp specimens. The mechanical performance of the thermoformed specimens was in the same range as the plastic materials that are conventionally used in food packaging, i.e., modulus 0.6–1.2 GPa, strength 49 MPa and elongation 6–9%. Finally, this study demonstrates the potential of wood pulps to form three-dimensional thermoformed products. [ABSTRACT FROM AUTHOR]

This is an introduction to the special issue of California Management Review on Design Thinking (DT). This special issue joins the growing body of work exploring the idea of DT and whether DT makes a difference in terms enhancing or augmenting the impact of technology—and, as a result, innovation—in a positive way. We have chosen an interesting, relevant, and useful array of papers that provide different approaches, views, and interpretations of applied design thinking. These articles provide both management and scholarly readers with insights in how DT is used, as well as its impact and usefulness in a variety of contexts. [ABSTRACT FROM AUTHOR]