Guest Editorial

J. Mech. Des. 2017;139(10):100901-100901-2. doi:10.1115/1.4037555.

Now in its 40th year of existence, ASME's Journal of Mechanical Design has covered a wide range of topics on behalf of the Design Engineering Division. The past 40 years have seen countless advances in mechanical design, developing new knowledge in areas ranging from simulation to representation to communication, among others. These advances have often been complemented by similar advances in manufacturing, and traditional manufacturing processes such as machining and injection molding have been investigated heavily by the engineering design community. Today, however, we are in the midst of a paradigm shift. Whereas design methods in the past sought to overcome the design constraints imposed by manufacturing technologies, emerging digital manufacturing processes are removing many of these barriers and introducing new ones that are not yet fully understood. As a result, the additional degrees-of-freedom offered via selective (multi-) material addition/subtraction have exceeded our current design proficiencies. Additive manufacturing (AM) is at the forefront of this shift, and our engineering design software, methods, and tools are struggling to keep pace.

Commentary by Dr. Valentin Fuster

Special Issue paper

J. Mech. Des. 2017;139(10):100902-100902-5. doi:10.1115/1.4037303.

Additive manufacturing (AM) has many potential industrial applications because highly complex parts can be fabricated with little or no tooling cost. One barrier to widespread use of AM, however, is that many designers lack detailed information about the capabilities and limitations of each process. To compile statistical design guidelines, comprehensive, statistically meaningful metrology studies need to be performed on AM technologies. In this paper, a test part is designed to evaluate the accuracy and resolution of the polymer powder bed fusion (PBF) or selective laser sintering process for a wide variety of features. The unique construction of this test part allows it to maximize feature density while maintaining a small build volume. As a result, it can easily fit into most existing selective laser sintering builds, without requiring dedicated builds, thereby facilitating the repetitive fabrication necessary for building statistical databases of design allowables. By inserting the part into existing builds, it is also possible to monitor geometric accuracy and resolution on a build- and machine-specific basis in much the same way that tensile bars are inserted to monitor structural properties. This paper describes the test part and its features along with a brief description of the measurements performed on it and a representative sample of the types of geometric data derived from it.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(10):100903-100903-8. doi:10.1115/1.4037250.

Prior research has shown that powder-bed fusion (PBF) additive manufacturing (AM) can be used to make functional, end-use components from powdered metallic alloys, such as Inconel® 718 superalloy. However, these end-use components and products are often based on designs developed for more traditional subtractive manufacturing processes and do not take advantage of the unique design freedoms afforded by AM. In this paper, we present a case study involving the redesign of NASA’s existing “pencil” thruster used for spacecraft attitude control. The initial pencil thruster was designed for and manufactured using traditional subtractive methods. The main focus in this paper is to (a) identify the need for and use of both opportunistic and restrictive design for additive manufacturing (DfAM) concepts and considerations in redesigning the thruster for fabrication with PBF AM and (b) compare the resulting DfAM thruster with a parallel development effort redesigning the original thruster to be manufactured more effectively using subtractive manufacturing processes. The results from this case study show how developing end-use AM components using specific DfAM guidelines can significantly reduce manufacturing time and costs while enabling new and novel design geometries.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(10):100904-100904-9. doi:10.1115/1.4037251.

Additive manufacturing (AM) technologies have become integral to modern prototyping and manufacturing. Therefore, guidelines for using AM are necessary to help users new to the technology. Many others have proposed useful guidelines, but these are rarely written in a way that is accessible to novice users. Most guidelines (1) assume the user has extensive prior knowledge of the process, (2) apply to only a few AM technologies or a very specific application, or (3) describe benefits of the technology that novices already know. In this paper, we present a one-page, visual design for additive manufacturing worksheet for novice and intermittent users which addresses common mistakes as identified by various expert machinists and additive manufacturing facilities who have worked extensively with novices. The worksheet helps designers assess the potential quality of a part made using most AM processes and indirectly suggests ways to redesign it. The immediate benefit of the worksheet is to filter out bad designs before they are printed, thus saving time on manufacturing and redesign. We implemented this as a go-no-go test for a high-volume AM facility where users are predominantly novices, and we observed an 81% decrease in the rate of poorly designed parts. We also tested the worksheet in a classroom, but found no difference between the control and the experimental groups. This result highlights the importance of motivation since the cost of using AM in this context was dramatically lower than real-world costs. This second result highlights the limitations of the worksheet.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(10):100905-100905-6. doi:10.1115/1.4037304.

An end-to-end development approach for space flight qualified additive manufacturing (AM) components is presented and demonstrated with a case study consisting of a system of five large, light-weight, topologically optimized components that serve as an engine mount in SpaceIL's GLPX lunar landing craft that will participate in the Google Lunar XPrize challenge. The development approach includes a preliminary design exploration intended to save numerical effort in order to allow efficient adoption of topology optimization and additive manufacturing in industry. The approach also addresses additive manufacturing constraints, which are not included in the topology optimization algorithm, such as build orientation, overhangs, and the minimization of support structures in the design phase. Additive manufacturing is carried out on the topologically optimized designs with powder bed laser technology and rigorous testing, verification, and validation exercises complete the development process.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(10):100906-100906-13. doi:10.1115/1.4037305.

The lattice structure is a type of cellular material with trusslike frames which can be optimized for specific loading conditions. The fabrication of its intricate architecture is restricted by traditional manufacturing technologies. However, additive manufacturing (AM) enables the fabrication of complex structures by aggregation of materials in a layer-by-layer fashion, which has unlocked the potential of lattice structures. In the last decade, lattice structures have received considerable research attention focusing on the design, simulation, and fabrication for AM techniques. And different modeling approaches have been proposed to predict the mechanical performance of lattice structures. This review introduces the aspects of modeling of lattice structures and the correlation between them, summarizes the existing modeling approaches for simulation, and discusses the strength and weakness in different simulation methods. This review also summarizes the characteristics of AM in manufacturing cellular materials and discusses their influence on the modeling of lattice structures.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(10):100907-100907-7. doi:10.1115/1.4037302.

A current issue in metal-based additive manufacturing (AM) is achieving consistent, desired process outcomes in manufactured parts. When process outcomes such as strength, density, or precision need to meet certain specifications, changes in process variable selection can be made to meet these requirements. However, the changes required to achieve a better part performance may not be intuitive, particularly because process variable changes can simultaneously improve some outcomes while worsening others. There is great potential to design the additive manufacturing process, tailoring process variables based on user requirements for a given part. In this work, the tradeoffs between multiple process outcomes are formalized and the design problem is explored throughout the design space of process variables. Based on user input for each process outcome considered, P–V (power–velocity) process design charts are introduced, which map the process space and identify the best combination of process variables to achieve a user's desired outcome.

Commentary by Dr. Valentin Fuster

Research Papers: Design Automation

J. Mech. Des. 2017;139(10):101401-101401-11. doi:10.1115/1.4037620.

This study presents an efficient multimaterial design optimization algorithm that is suitable for nonlinear structures. The proposed algorithm consists of three steps: conceptual design generation, clustering, and metamodel-based global optimization. The conceptual design is generated using a structural optimization algorithm for linear models or a heuristic design algorithm for nonlinear models. Then, the conceptual design is clustered into a predefined number of clusters (materials) using a machine learning algorithm. Finally, the global optimization problem aims to find the optimal material parameters of the clustered design using metamodels. The metamodels are built using sampling and cross-validation and sequentially updated using an expected improvement function until convergence. The proposed methodology is demonstrated using examples from multiple physics and compared with traditional multimaterial topology optimization (MTOP) method. The proposed approach is applied to a nonlinear, multi-objective design problems for crashworthiness.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(10):101402-101402-12. doi:10.1115/1.4037407.

Optimization of dynamic systems often requires system simulation. Several important classes of dynamic system models have computationally expensive time derivative functions, resulting in simulations that are significantly slower than real time. This makes design optimization based on these models impractical. An efficient two-loop method, based on surrogate modeling, is presented here for solving dynamic system design problems with computationally expensive derivative functions. A surrogate model is constructed for only the derivative function instead of the simulation response. Simulation is performed based on the computationally inexpensive surrogate derivative function; this strategy preserves the nature of the dynamic system, and improves computational efficiency and accuracy compared to conventional surrogate modeling. The inner-loop optimization problem is solved for a given derivative function surrogate model (DFSM), and the outer loop updates the surrogate model based on optimization results. One unique challenge of this strategy is to ensure surrogate model accuracy in two regions: near the optimal point in the design space, and near the state trajectory in the state space corresponding to the optimal design. The initial evidence of method effectiveness is demonstrated first using two simple design examples, followed by a more detailed wind turbine codesign problem that accounts for aeroelastic effects and simultaneously optimizes physical and control system design. In the last example, a linear state-dependent model is used that requires computationally expensive matrix updates when either state or design variables change. Results indicate an order-of-magnitude reduction in function evaluations when compared to conventional surrogate modeling. The DFSM method is expected to be beneficial only for problems where derivative function evaluation expense, and not large problem dimension, is the primary contributor to solution expense (a restricted but important problem class). The initial studies presented here revealed opportunities for potential further method improvement and deeper investigation.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(10):101403-101403-8. doi:10.1115/1.4037623.

The paper presents a framework for set-based design under uncertainty and demonstrates its viability through designing a super-cavitating hydrofoil of an ultrahigh speed vessel. The framework achieves designs that safely meet the requirements as quantified precisely by superquantile measures of risk (s-risk) and reduces the complexity of design under uncertainty. S-risk ensures comprehensive and decision-theoretically sound assessment of risk and permits a decoupling of parametric uncertainty and surrogate (model) uncertainty. The framework is compatible with any surrogate building technique, but we illustrate it by developing for the first time risk-adaptive surrogates that are especially tailored to s-risk. The numerical results demonstrate the framework in a complex design case requiring multifidelity simulation.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(10):101404-101404-10. doi:10.1115/1.4037344.

Solving optimal design problems through crowdsourcing faces a dilemma: On the one hand, human beings have been shown to be more effective than algorithms at searching for good solutions of certain real-world problems with high-dimensional or discrete solution spaces; on the other hand, the cost of setting up crowdsourcing environments, the uncertainty in the crowd's domain-specific competence, and the lack of commitment of the crowd contribute to the lack of real-world application of design crowdsourcing. We are thus motivated to investigate a solution-searching mechanism where an optimization algorithm is tuned based on human demonstrations on solution searching, so that the search can be continued after human participants abandon the problem. To do so, we model the iterative search process as a Bayesian optimization (BO) algorithm and propose an inverse BO (IBO) algorithm to find the maximum likelihood estimators (MLEs) of the BO parameters based on human solutions. We show through a vehicle design and control problem that the search performance of BO can be improved by recovering its parameters based on an effective human search. Thus, IBO has the potential to improve the success rate of design crowdsourcing activities, by requiring only good search strategies instead of good solutions from the crowd.

Commentary by Dr. Valentin Fuster

Research Papers: Design Innovation and Devices

J. Mech. Des. 2017;139(10):103501-103501-8. doi:10.1115/1.4037243.

This paper applies linear elastic theory and Castigliano's first theorem to design nonlinear (stiffening) flexures used as load cells with both large force range and large resolution. Low stiffness at small forces causes high sensitivity, while high stiffness at large forces prevents over-straining. With a standard 0.1 μm deflection sensor, the nonlinear load cell may detect 1% changes in force over five orders of force magnitude. In comparison, a traditional linear load cell functions over only three orders of magnitude. We physically implement the nonlinear flexure as a ring that increasingly contacts rigid surfaces with carefully chosen curvatures as more force is applied. We analytically describe the load cell performance as a function of its geometry. We describe methods for manufacturing the flexure from a monolithic part or multiple parts. We experimentally verify the theory for two load cells with different parameters.

Commentary by Dr. Valentin Fuster

Technical Brief

J. Mech. Des. 2017;139(10):104501-104501-6. doi:10.1115/1.4037625.

In order to improve the accuracy of skived gears by means of reducing vibrations that are often observed during the cutting process, a simple model for calculating the cutting forces of skiving process is presented and also its effectiveness was discussed. The model is characterized by simple geometrical calculations, and the cutting forces were assumed as sum of vectors that represent the penetration of cutting edges. In the model, multiple cutting edges that are simultaneously meshing with the workpiece were considered. Distinguished oscillating frequencies of the calculated cutting forces and the natural frequencies of the clamped workpiece and of the cutter were carefully analyzed in order to predict the cutter rotation speed that was most likely to reduce undesired vibrations. Processing experiments conducted at several cutter rotation speeds showed that the predicted cutter rotation speed which could significantly reduce undesired vibrations was very effective and enabled the quality of a skived gear to be improved. Consequently, the proposed calculation model was enough effective and useful for operation conditions such as cutter rotation speed being determined.

Topics: Vibration , Cutting , Gears
Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(10):104502-104502-6. doi:10.1115/1.4037630.

This paper addresses the problem of mapping a vector of input variables (corresponding to discrete samples from a time-varying input) to a vector of output variables (discrete samples of the time-dependent response). This mapping is typically performed by a mechanistic model. However, when the mechanistic model is complex and dynamic, the computational effort to iteratively generate the response for design purposes can be burdensome. Metamodels (or, surrogate models) can be computationally efficient replacements, especially when the input variables have some amplitude and frequency bounds. Herein, a simple metamodel in the form of a transfer matrix is created from a matrix of a few training inputs and a corresponding matrix of matching responses provided by simulations of the dynamic mechanistic model. A least-squares paradigm reveals a simple way to link the input matrix to the columns of the response matrix. Application of singular value decomposition (SVD) introduces significant computational advantages since it provides matrices whose properties give, in an elegant fashion, the transfer matrix. The efficacy of the transfer matrix is shown through an investigation of a nonlinear, underdamped, double mass–spring–damper system. Arbitrary excitations and selected sinusoids are applied to check accuracy, speed and robustness of the methodology. The sources of errors are identified and ways to mitigate them are discussed. When compared to the ubiquitous Kriging approach, the transfer matrix method shows similar accuracy but much reduced computation time.

Commentary by Dr. Valentin Fuster

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