Carlye A. Lauff; Daria Kotys-Schwartz; Mark E. Rentschler
J. Mech. Des. 2018; 140(6):061102-061102-12; doi: 10.1115/1.4039340
 
Prototyping is an essential part of a company’s product development process. It is critical to launching new products to market; these products can range from the next generation iPhone to surgical devices for doctors to your favorite shoes. Currently, there are limited research studies conducted within companies, meaning we lack an understanding about how companies engage in prototyping activities. This research project observed the entire product development process within three companies in the fields of consumer electronics, medical devices, and footwear. Through our analysis, we uncovered that prototypes are tools for enhanced communication, increased learning, and informed decision-making.

​Zefang Shen; Garry Allison; Lei Cui
 J. Mech. Des. 2018; 140(9):092302-092302-12
doi: 10.1115/1.4040486

Exoskeletons are wearable robots developed to assist the wear’s motion. In rehabilitation, such devices can help patients relearn natural motion after surgery, spinal cord injury, stroke, etc. Compared with conventional rehabilitation, exoskeleton-based rehabilitation can provide highly stable and repetitive movements. However most current devices are bulky and heavy, which limits their application in clinical settings. In this paper, we propose a method to design compact and lightweight planar linkages for exoskeletons with multiple output joints, while requiring only one actuator. Candidate linkages are generated and then evaluated to obtain the optimal design of the linkages. Applying this method, we have developed an index finger exoskeleton and a leg exoskeleton for rehabilitation, both of which are compact and portable. Their simplicity in design also increase the affordability for exoskeleton devices, which can facilitate applications outside clinical settings.

​Dielectric elastomers (DEs) may be a more energy efficient, lightweight, and low-cost solution for many emerging mechatronics applications when compared against established actuation technologies (e.g. solenoids or pneumatic cylinders). DE actuators (DEA) are also highly scalable, have low power consumption, and offer high flexibility. The presented work proposes a systematic tool for quasi-static performance prediction of circular out-of-plane DEAs. The method is based on extracting material characteristics (in terms of a stress-strain behavior) from a set of training data. This is then used to calculate the force-displacement characteristic for arbitrary geometries. The method is validated using two different prediction scenarios: blocking force and stroke of various geometries. The prediction errors for stroke and blocking force are not larger than 8.3% and 3.1%, respectively. Additionally, this work demonstrates that the stroke output mainly depends on the electrode ring width, and that it increases linearly. Also, it is shown that the force scales linearly with the average electrode ring circumference. These two parameters can be individually used to tailor DEA stroke and force output. The proposed method can then be used by designers to adopt DEAs for certain applications without the need for complicated FE models or prototyping. 

​Delivering a variety of products with minimal lead time is a critical issue given today’s competitive manufacturing industry. Many design and production firms address the challenges of variety by adopting a product family manufacturing strategy. Product families are a broad range of artifacts, known as product variants, which share a number of common components. Thus, engineering changes in a product family affect the product under consideration and other product variants in the family. This increases the difficulty of predicting the change propagation within a family of products. This paper introduces a seven-step change propagation approach that predicts and evaluates the impact of change propagation across product variants. Interdependencies and logical relationships between directly connected components are captured using a Component-based Design Structure Matrix. This highlights the different change propagation paths that are available in the product’s structure. Risk analysis in terms of lead time is performed at the component level. The results demonstrate that avoiding project delays requires selecting suitable change propagation paths in a family of products.

Before the widespread use of modern computer systems, engineers worked in highly collaborative groups around large drawings tables. Today, the engineering design environment is more solitary, and collaboration often requires leaving the tools of the design environment. The highly distributed nature of today's workforce has caused a rapid proliferation of remote meetings and impeded the explanation of 3D information. While Virtual Reality (VR) has been proposed as a solution to these problems, the high cost and low availability of such systems has limited their impact. This paper presents a collaborative VR environment with support for hand gestures using readily-available, low-cost VR hardware. The environment allows multiple distributed participants to join a 3D virtual meeting, each with an independent view point, and walk around the virtual room to view the other participants as well as 3D engineering artifacts. The system supports natural communication gestures such as pointing, showing relative location, relative size, and orientation though physical hand motions. Additionally, participants can sketch in 3D using special input gestures. This allows for the communication of design concepts, design changes, and design issues. A user study is presented that demonstrates 45% faster communication compared against modern remote meeting software. Communication clarity and understanding are also improved. Future work will add deeper integration with modern engineering software and explore new design methods enabled by collaborative VR technology.  

Co-design is the integrated optimization of the physical plant and controller for an engineering system. The challenge in co-design is determining both time-invariant (physical design) variables and time-variant (control) variables. In co-design, as the size of the problem (number of variables) becomes large, the problem can become too difficult for an all-at-once solution. Our approach extends earlier research by creating a class of multi-subsystem co-design problems where both design and control are formulated and solved. A scalable test problem is used for comparing the proposed decentralized co-design optimization approach against a centralized approach. Results of this study show that the computational time of the proposed decentralized approach increases approximately linearly with respect to an increase in the number of subsystems (variables), while the computational cost of the centralized approach increases nonlinearly.

Sheng Yang and Yaoyao Fiona Zhao
J. Mech Des 140(3):031702-031702-12. doi:10.1115/1.4038922

​Part count reduction (PCR) is one motivation for using additive manufacturing (AM) processes. PCR helps simplify product structure, eliminate auxiliary connecters, and reduce assembly difficulties and cost. However, PCR may also increase manufacturing difficulty and the irreplaceability of failed subcomponents. This paper presents a pioneering investigation of how AM-enabled PCR (AM-PCR) impacts lifecycle activities. A new set of design rules and principles are proposed for PCR that lead to lowered cost and enhanced performance. The PCR problem is formulated as a combinatory optimization problem where the objective is minimizing lifecycle cost/performance ratio while ensuring conformance to all constraints (e.g. manufacturing, maintenance, and recycling). To address the challenge of computational cost, a dual-level screening and refinement product redesign framework is presented that first searches for the minimum grouping solution and then refines the remaining combinations using design optimization. This approach will help designers automate the part count reduction process enabled by additive manufacturing while exploring new design innovation opportunities. 

Power-split hybrid electric vehicles embody two electric machines in addition to the internal combustion engine, and it employs one or more planetary gear sets (PG) while disposing of the transmission. Most of the prior studies on the design of power-split hybrids focused on finding optimal powertrain configurations, which are configurations specifying the components connections. However, a selected powertrain configuration cannot be physically realized as it does not specify the components arrangements in three dimensional space. Therefore, a given powertrain configuration should be depicted into feasible kinematic diagrams, which are used to generate the three dimensional drawings used for manufacturing. Multiple kinematic diagrams can be depicted for a given powertrain configuration as each kinematic diagrams specifies the exact components arrangements in addition to their connections. In this work, an automatic approach is developed to generate all the feasible kinematic diagrams for any given power-split powertrain configuration with a single PG. First, all the possible components arrangements, i.e. positioning diagrams, are generated. Then, a set of developed feasibility rules are applied on each positioning diagram in order to filter out infeasible components arrangements. Lastly, feasible kinematic diagrams are depicted for each feasible positioning diagram, and a set of preferred design criteria are used to select arrangements that best suit the vehicle’s manufacturability, packaging, maintenance, and cost. The proposed methodology guarantees automatically finding the components arrangements that best suit the desired vehicle through the search of the entire design space.

Configuration design problems are common in everyday life as well as engineering, with examples ranging from the selection and arrangement of furniture for a living room to the type of problem-solving used by NASA engineers to return Apollo 13 safely to Earth. There are many theoretical approaches for solving configuration design problems but few studies have examined how humans naturally solve them. This work used data-mining techniques (specifically hidden Markov models) to study the behavioral patterns shown by humans solving two distinct configuration design problems. Mining this data revealed beneficial process heuristics that are potentially generalizable to the entire class of configuration design problems. The trained models indicate that designers proceed through four procedural states, beginning in a state dominated by topology design and progressing to a final state with a focus on parameter design. The mined models also indicate that high-performing designers opportunistically tune parameters early in the process, enabling a more effective and nuanced search for good solutions.