The availability of big data has made the role of digital twins in manufacturing more prominent. This paper introduces a geometry assurance digital twin - created from the scanned data of individual components - to define and improve an assembly’s geometrical quality. The joining sequence in a sheet metal assembly impacts geometrical quality and determining the optimal joining sequence is computationally expensive. Meta-heuristic optimization techniques like genetic algorithms often require many simulations, which can increase computational cost. This work improves the optimization process by combining a model-based heuristic algorithm - based on contact displacement minimization – with the meta-heuristic algorithm. Contact modeling avoids part penetration in adjacent areas, and the joining sequences that provide minimal penetration states are used to populate the initial solution for the meta-heuristic algorithm. This approach is demonstrated on two sheet metal assemblies and a reduction in sequence time of 60-80% is achieved. By using a digital twin, optimal joining solutions can be achieved with greater efficiency. ​

Philip Odonkor; Kemper Lewis
J. Mech. Des. 2018; 141(2):021704-021704-9.
doi: 10.1115/1.4041629

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. 

Nathan Knerr and Daniel Selva
J. Mech. Des 138(9), 091403; doi: 10.1115/1.4033987​

Cityplot is a new visualization technique for engineering design that uses a dimensionally-reduced representation of the design decisions to represent the mapping from the decisions to the criteria upon which a design is judged. The shown Cityplot depicts possible CubeSat constellations to support the 2007 Earth Science Decadal Survey. Each constellation is comprised of up to 4 CubeSats and each CubeSat can select from a list of 7 instruments. Possible CubeSat constellations are “cities” and are placed in a 2d space to be visualized. An individual constellation can also be seen as a table of instruments (rows) being present (black) on a given CubeSat (columns). The benefits, costs and risks of each possible constellation are represented as color-coded “buildings” in each “city”. The criteria in this example are: a tiered count of satisfied Decadal objectives (blue), the average CubeSat Technology Readiness Level (red), lifecycle cost (green), maximum number of lost instruments upon loss of a single satellite (black). A taller building indicates the possible constellation performs better in that criteria. Dark purple “roads” between two designs indicate that only one instrument is either added to or removed from one CubeSat to make one constellation identical to the other. Cityplot simultaneously shows sensitivity of criteria to decisions, criteria tradeoffs and design families via a quick intuitive view of the design space.

The synthesis of functional molecular mechanisms is constrained by the notorious difficulties in fabricating nano-links of prescribed shapes and sizes. Thus, the classical mechanism synthesis methods, which assume the ability to manufacture any designed links, cannot provide a systematic process for designing molecular mechanisms. We propose a new approach to build functional mechanisms with prescribed mobility by only using elements from a predefined "link soup". The resulting synthesis procedure is the first of its kind that is capable of systematically synthesizing functional linkages with prescribed mobility constructed from a soup of primitive entities. Furthermore, the proposed systematic approach outputs the ATLAS of candidate mechanisms, which can be further processed for downstream applications. Although the scope of this technique is rather general, its immediate application is the design of molecular machines assembled from nano-links that either exist in nature or can be fabricated.

For the Full Research Paper see ASME's Digital Collection.

​This work is the first demonstration of the use of a parameterized approach to design optimization on a complex engineering problem. Parameterized optimization solves a family of optimization problems as a function of exogenous parameters. When applied to a subsystem of interest, it results in general knowledge about the capabilities of the subsystem rather than a restrictive point solution. The motivation is that it often is necessary to advance the development of a subsystem independent of system-level specifics. This is true during initial research and development efforts or in sensitive military and competitive industrial design environments in which compartmentalization of information is common and necessary. It also is important in systems development projects when the need for concurrency often requires subsystem designers to make progress in the absence of full information about other interfacing subsystems. We solve this specialized design problem using the predictive parameterized Pareto genetic algorithm (P3GA). The approach is demonstrated for the multifunctional design of a structurally-integrated liquid metal circuit intended to provide integrated cooling functionality.  A family of optimal design solutions associated with values of external parameters (bounded real numbers) is computed efficiently using P3GA. The demonstration employs both high- and low-fidelity multi-physical engineering models seamlessly and results in general knowledge about the subsystem as a function of parameters associated with other interfacing subsystems.