This research presents a method of optimizing the consolidation of parts in an assembly using metal additive manufacturing (MAM). The method generates candidates for consolidation, filters them for feasibility and structural redundancy, finds the optimal build layout of the parts, and optimizes which parts to consolidate using a genetic algorithm. Results are presented for both minimal production time and minimal production costs, respectively. The production time and cost models consider each step of the manufacturing process, including MAM build, post-processing steps such as support structure removal, and assembly. It accounts for costs affected by part consolidation, including machine costs, material, scrap, energy consumption, and labor requirements. We find that developing a closed-loop filter that excludes consolidation candidates that are structurally redundant with others dramatically reduces the number of candidates, thereby significantly reducing convergence time. Results show that when increasing the number of parts that are consolidated, the production cost and time at first decrease due to reduced assembly steps, and then increase due to additional support structures needed to uphold the larger, consolidated parts. We present a rationale and evidence justifying that this is an important tradeoff of part consolidation that generalizes to many types of assemblies. Subsystems that are smaller, or can be oriented with very little support structures or have low material costs or fast deposition rates can have an optimum at full consolidation; for other subsystems, the optimum is less than 100%. The presented method offers a promising pathway to minimize production time and cost by consolidating parts using MAM. In our test-bed results for an aircraft fairing produced with powder-bed electron beam melting, the solution for minimizing production cost (time) is to consolidate 17 components into four (two) discrete parts, which leads to a 20% (25%) reduction in unit production cost (time).

We introduce a method to analyze and modify a shape to make it manufacturable for a given additive manufacturing (AM) process. Different AM technologies, process parameters, or materials introduce geometric constraints on what is manufacturable or not. Given an input 3D model and minimum printable feature size dictated by the manufacturing process characteristics and parameters, our algorithm generates a corrected geometry that is printable with the intended AM process. A key issue in model correction for manufacturability is the identification of critical features that are affected by the printing process. To address this challenge, we propose a topology aware approach to construct the allowable space for a print head to traverse during the 3D printing process. Combined with our build orientation optimization algorithm, the amount of modifications performed on the shape is kept at minimum while providing an accurate approximation of the as-manufactured part. We demonstrate our method on a variety of 3D models and validate it by 3D printing the results.

This paper presents the design, fabrication, and testing of a novel single stack-based piezoelectric energy harvester (PEH) for harvesting energy from three-degree-of-freedom (3-DOF) force excitation. One uniqueness lies in that the 3-DOF energy harvesting is implemented by using one piezoelectric stack. To scavenge energy from the 3-DOF input force, the proposed PEH is constructed with several force transmission mechanisms and slider mechanisms. The direction of the input force is first changed by the force transmission mechanisms, and the redundant force components are eliminated by the slider mechanisms. The transmitted force is then amplified by a two-stage force amplifier mechanism to improve the electric power output. The key parameters were found by establishing an analytical model of the proposed PEH. The best output performance of the PEH is achieved by selecting and optimally designing the key parameters. A prototype harvester was fabricated, and several experimental studies were conducted to verify the device performance. Results show the effectiveness of the developed 3-DOF PEH under the input force applied in x-axis, y-axis or z-axis. Furthermore, the issues that affect the practical application are discussed.

Research in modularization of product families reveals numerous individual cause and effect impacts of modularity on a firm. There are clearly many interrelated positive and negative economic impacts arising from different activities of the firm impacted by the modular product structures. This makes the construction of an economic business case for modularity difficult, where often the benefits are reduced indirect costs. This paper presents a literature-based network model of how modular product structures affect firm’s economics across the design-to-manufacturing life cycle phases. It shows how (1) changes on modularity properties may lead to (2) different effects within the product’s life cycle phases that (3) have an economic impact on the firm. For instance, modularization can prolong development time of a platform, while shortening the subsequent development times of product variants and lowering manufacturing costs. To validate the proposed model, the given effect chains were compared by industrial experts against nine case study modularization projects by marking effects that were experienced and observed in their projects. The results first revealed that in design, an increase of commonality drove component reuse leading to lower development costs per unit. Second, in procurement, it was found that increased modularity caused better predictability, less purchasing orders, and better purchasing conditions that ultimately lead to lower costs. Third, in production, it was found that a smaller variety of components allowed less process variety, leading to fewer and more optimized processes and therefore lower production costs. We present these cause and effect impacts of modularity as drivers for quantifying the economic impact of modularity.

This study explores the value of visualizing the prototyping activities in a new product development process from idea to production. Through a case study of a hardware startup, we present a visualization of their prototyping processes, from early idea to the introduction of several product generations to market. We call this visualization technique ProtoMapping. Analyzing qualitative and quantitative parameters of the ProtoMap allows us to understand how prototyping strategies can change over the course of a product development process. Specifically, we focus on the prototyping of parallel concepts, iterations of concepts, manufacturing processes used for prototypes, prototype media, prototype tests, as well as prototyping of isolated or integrated systems. Based on this longitudinal analysis, we present a number of insights that are otherwise difficult to extract. We observe how parallel prototypes of isolated systems can be used to explore the solution space and later be synthesized into prototypes for integrated systems. Furthermore, we understand how the need to scale up production can lead to increased prototyping activities, as several parts of a product must be redesigned. This study illustrates the utility of a retrospective visualization approach and serves as the first step in formulation of generalized guidelines for prototyping strategies for holistic product development, through further case studies.

Nowadays, increasing awareness of environmental protection has evoked the adoption of green technologies in design and manufacturing. As a revolutionizing manufacturing technology that produces components in a layer-by-layer fashion, additive manufacturing (AM) has followed this trend. Among a variety of AM processes, fused filament fabrication (FFF) is one of the most commonly used technologies. However, AM (including FFF) is inherently energy expensive and energy inefficient compared with the conventional manufacturing. Thus, an urgent investigation is needed to reduce the energy consumption for AM production. On the other hand, part geometric accuracy is an important aspect for the quality of additively manufactured components. It is not meaningful to improve AM’s energy consumption performance with compromised part geometric accuracy. Therefore, it is necessary to jointly consider energy consumption as well as part geometric accuracy in the AM process design. This study applies the statistical regression approach to model AM energy consumption and part geometric accuracy. The nondominated sorting genetic algorithm II (NSGA-II) and the technique for order of preference by similarity to ideal solution (TOPSIS) method together are used to locate the compromised optimal solution for AM process parameter settings. The effectiveness of the proposed approach is demonstrated through a case study developed with the FFF process and a specific part design. The results of this study are significant to both AM energy consumption and part geometric accuracy in terms of qualitative and quantitative analyses. Furthermore, the study can potentially guide the future AM sustainability model development and be extended to future AM process improvement.

The world is changing and demanding stronger, lighter, and more versatile materials. Taking advantage of the full potential of these materials also requires versatile manufacturing processes. The in situ forming of a liquid-infused preform (ISFLIP) is a new manufacturing process for fiber-reinforced polymer (FRP) parts with shell shapes. ISFLIP is a hybrid process between vacuum infusion (VI) and diaphragm forming. This paper focuses on the mechanical design and experimental validation of a functional prototype of ISFLIP. The novelty of the design lies especially in a double-diaphragm system that is fundamental to carrying out the forming just after the infusion stage. The double-diaphragm system and other two major subsystems, a vacuum table and an infrared heating grid, were devised to benefit from the operational advantages of ISFLIP. The whole prototype, once constructed, was tested by forming some demonstration components. The result of one of these components, a “C” cross-section FRP profile with two sharp joggles, is finally obtained, proving the feasibility of the prototype.

While many studies have characterized the costs of product variety in assembly production, there is little research detailing the sources and costs of increased product variety on a nonassembled (fabrication) production line, despite nonassembled products accounting for over 50% of U.S. manufacturing. Our research examines the production-level costs, benefits, and margins associated with producing a variety of nonassembled products, and how design attributes affect these outcomes. We propose a theoretical framework of nonassembled product variety, identifying five general design attributes of nonassembled products that influence product-variety outcomes, and identify potential sources of variety costs and benefits. We then conduct a case study of a plant that produces a large variety of unique products in a single year. We develop a new process-based cost modeling (PBCM) technique to capture the impacts of product variety. Leveraging design of experiments (DOE), we model fourteen representative products, altering the mix of products to focus on each design attribute. In our case study, which has relatively large lot sizes, less customized designs, and less flexible equipment, we find that cost increases related to changeovers between product designs are small relative to cost benefits derived from sharing equipment and labor. We provide a framework illustrating how these results generalize to other contexts, which shows that changeover costs will dominate sharing benefits in environments with more customized designs, produced in smaller lot sizes, and processed on flexible equipment.

We present a system for 3D printing large-scale objects using natural biocomposite materials, which comprises a precision extruder mounted on an industrial six-axis robot. This paper highlights work on controlling process settings to print filaments of desired dimensions while constraining the operating point to a region of maximum tensile strength and minimum shrinkage. Response surface models relating the process settings to the geometric and physical properties of extruded filaments are obtained through face-centered central composite designed experiments. Unlike traditional applications of this technique that identify a fixed operating point, the models are used to uncover dimensions of filaments obtainable within the operating boundaries of our system. Process-setting predictions are then made through multi-objective optimization of the models. An interesting outcome of this study is the ability to produce filaments of different shrinkage and tensile strength properties by solely changing process settings. As a follow-up, we identify optimal lateral overlap and interlayer spacing parameters to define toolpaths to print structures. If unoptimized, the material’s anisotropic shrinkage and nonlinear compression characteristics cause severe delamination, cross-sectional tapering, and warpage. Finally, we show the linear scalability of the shrinkage model in 3D space, which allows for suitable toolpath compensation to improve the dimensional accuracy of printed artifacts. We believe this first-ever study on the parametrization of the large-scale additive manufacture technique with biocomposites will serve as reference for future sustainable developments in manufacturing.

Three-dimensional (3D) weaving has recently arisen as viable means for manufacturing metallic, architected microlattices. Herein, we describe a topology optimization approach for designing the architecture of such 3D woven lattices. A ground structure design variable representation is combined with linear manufacturing constraints and a projection mapping to realize lattices that satisfy the rather restrictive topological constraints associated with 3D weaving. The approach is demonstrated in the context of inverse homogenization to design lattices with maximized fluid permeability. Stokes flow equations with no-slip conditions governing unit cell flow fields are interpolated using the Darcy–Stokes finite element model, leveraging existing work in the topology optimization of fluids. The combined algorithm is demonstrated to design manufacturable lattices with maximized permeability whose properties have been experimentally measured in other published work.

The polytope-based tolerance analysis in design process uses a finite set of constraints to represent specifications and propagates these constraints to any objective point in the Euclidean space. The operations of Minkowski sum and intersection on polytopes are well suited to serial and parallel assemblies. The polytope model has been applied to complex assemblies which contain a large number of joints and geometrical tolerances. However, the previous studies on this model consider toleranced features as surfaces of perfect form. The ignorance of form defects in tolerance analysis would result in a significant loss in accuracy and reliability. In this paper, an extension of the polytope model for tolerance analysis considering form defects is described in which the skin model shape representing the physical shape of the product is adopted to simulate the actual toleranced feature in place of the substitute one used conventionally. The combination of polytope model and skin model shape is expected to inherit many of the advantages of each model, combining easy-to-use tolerance propagation and form defects representation with accuracy guarantees. To demonstrate the method and its respective application, a case study of an assembly is illustrated in detail. The proposed method further enhances the capability of the polytope model in handling form defects and provides more realistic assembly results that approximate the actual assembly conditions for design evaluation.

Advances in three-dimensional (3D) printing are enabling the design and fabrication of tailored lattices with high mechanical efficiency. Here, we focus on conducting experiments to mechanically characterize lattice structures to measure properties that inform an integrated design, manufacturing, and experiment framework. Structures are configured as beam-based lattices intended for use in novel spinal cage devices for bone fusion, fabricated with polyjet printing. Polymer lattices with 50 % and 70 % porosity were fabricated with beam diameters of 0.4 – 1.0 m m , with measured effective elastic moduli from 28 M P a to 213 M P a . Effective elastic moduli decreased with higher lattice porosity, increased with larger beam diameters, and were highest for lattices compressed perpendicular to their original build direction. Cages were designed with 50 % and 70 % lattice porosities and included central voids for increased nutrient transport, reinforced shells for increased stiffness, or both. Cage stiffnesses ranged from 4.1 k N / m m to 9.6 k N / m m with yielding after 0.36 – 0.48 m m displacement, thus suggesting their suitability for typical spinal loads of 1.65 k N . The 50 % porous cage with reinforced shell and central void was particularly favorable, with an 8.4 k N / m m stiffness enabling it to potentially function as a stand-alone spinal cage while retaining a large open void for enhanced nutrient transport. Findings support the future development of fully integrated design approaches for 3D printed structures, demonstrated here with a focus on experimentally investigating lattice structures for developing novel biomedical devices.

The array structure is widely used in precise electronic products such as large phased array antennas and large optical telescopes, the main components of which are a large surface base and a large number of high-precision discrete elements mounted on the surface base. The geometric error of discrete elements is inevitable in the manufacturing process and will seriously degrade the product performance. To deal with the tolerance design of discrete elements, a region-division-based tolerance design method is proposed in this paper. The whole array was divided into several regions by our method and the tolerance of discrete elements was correlated with the region importance on the performance. The method specifically includes the following steps: first, the sensitivity of the product performance to geometric errors was analyzed and the statistical relationship between the performance and geometric errors was established. Then, based on the sensitivity matrix, the regional division scheme was developed, and the corresponding tolerance was optimized according to the established relationship function. Finally, the optimal tolerance was selected among the multiple solutions to achieve the best performance. Taking a large phased array as an example, a simulation experiment was performed to verify the effectiveness of the proposed method.

While the combination of 3D scanning and printing processes holds much promise for the field of new product development, problems with repeatability and accuracy have limited the wider spread adoption of some digital prototyping tools, such as 3D scanners. Studies have explored the errors inherent in higher fidelity scan to print (S2P) processes, yet few have explored the errors in S2P processes that leverage affordable rapid noncontact scanners. Studies have yet to explore the strategies that designers, who are experienced with additive manufacturing, employ to mitigate errors. To address these gaps, a controlled study was conducted using data from 27 scans collected with a prototypical off-the-shelf noncontact optical scanner. The geometric and dimensional integrity of the digital models was found to be significantly out of tolerance at various phases of the S2P process, as compared to the original physical model. Larger errors were found more consistently in the data acquisition phase of the S2P process, but results indicate these errors were not sufficiently filtered out during the remainder of the process. A behavioral study was conducted with 13 experienced designers in digital fabrication to determine strategies for manually cleaning Point Clouds. Actions such as increase or decrease in brush size and select or de-select points were recorded. These actions were analyzed using hidden Markov modeling, which revealed distinct patterns of behavior. Designer strategies were not beneficial and digital models produced by designers were found to be significantly smaller than original physical models.

With the rapid developments of advanced manufacturing and its ability to manufacture microscale features, architected materials are receiving ever increasing attention in many physics fields. Such a design problem can be treated in topology optimization as architected material with repeated unit cells using the homogenization theory with the periodic boundary condition. When multiple architected materials with spatial variations in a structure are considered, a challenge arises in topological solutions, which may not be connected between adjacent material architecture. This paper introduces a new measure, connectivity index (CI), to quantify the topological connectivity, and adds it as a constraint in multiscale topology optimization to achieve connected architected materials. Numerical investigations reveal that the additional constraints lead to microstructural topologies, which are well connected and do not substantially compromise their optimalities.

One of the challenges in designing metamaterials for additive manufacturing (AM) is accounting for the differences between as-designed and as-built geometries and material properties. From a designer's perspective, these differences can lead to degradation of part and metamaterial performance, which is especially difficult to accommodate in small-lot or one-of-a-kind production. In this context, each part is unique, and therefore, extensive iteration is costly. Designers need a means of exploring the design space while simultaneously considering the reliability of additively manufacturing particular candidate designs. In this work, a design exploration approach, based on Bayesian network classifiers (BNC), is extended to incorporate manufacturing variation into the design exploration process and identify designs that reliably meet performance requirements when this variation is taken into account. The example application is the design of negative stiffness (NS) metamaterials, in which small volume fractions of NS inclusions are embedded within a host material. The resulting metamaterial or composite exhibits macroscopic mechanical stiffness and loss properties that exceed those of the base matrix material. The inclusions are fabricated with microstereolithography with features on the scale of tens of microns, but variability is observed in material properties and dimensions from specimen to specimen. This variability is measured and modeled via design, fabrication, and characterization of metrology parts. The quantified manufacturing variability is incorporated into the BNC approach as a manufacturability classifier to identify candidate designs that achieve performance targets reliably, even when manufacturing variability is taken into account.

Many engineering applications utilize periodic lattice structures to take advantage of their favorable and tailorable mechanical properties. However, manufacturing the structures and evaluating their mechanical properties are still challenging. Additive manufacturing (AM) processes offer an alternative method to fabricate periodic lattice structures but the processes only approximate bounding part surfaces. Periodic lattice structures generally have two important geometrical characteristics, large bounding surfaces, and a large number of joints. Since geometric approximation errors on large bounding surfaces critically affect mechanical properties of the structures, designers and engineers should incorporate this degradation into mechanical property estimation procedures. In addition, the effects of joints should be analyzed in the estimation process, because joints reduce struts lengths, and as a result, they add stiffness to lattice structures. This paper presents a new homogenization approach to estimate mechanical properties of additively manufactured periodic lattice structures that is based on semirigid joint frame elements, and it takes into account effects of geometric approximation errors and joint stiffening. Effective structural parameters of a semirigid joint frame element are calculated from an as-fabricated voxel model to incorporate the geometric approximation errors. The semirigid joint frame element is integrated into a discrete homogenization process to evaluate joint stiffening effects. This paper reports results of parametric studies that investigate effects of AM process and joint properties on periodic lattice structures fabricated by material extrusion. This paper also compares estimates from the proposed approach and conventional homogenization approaches with test results. The comparison shows that the proposed method provides estimates that are more accurate.

A material's design revolution is underway with a focus to design the material microstructure and processing paths to achieve certain performance requirements of products. A host of manufacturing processes are involved in producing a product. The processing carried out in each process influences its final properties. To couple the material processing-structure-property-performance (PSPP) spaces, models of specific manufacturing processes must be enhanced and integrated using multiscale modeling techniques (vertical integration) and then the input and output of the various manufacturing processes must be integrated to facilitate the flow of information from one process to another (horizontal integration). Together vertical and horizontal integration allows for the decision-based design exploration of the manufacturing process chain in an inverse manner to realize the end product. In this paper, we present an inverse method to achieve the integrated design exploration of materials, products, and manufacturing processes through the vertical and horizontal integration of models. The method is supported by the concept exploration framework (CEF) to systematically explore design alternatives and generate satisficing design solutions. The efficacy of the method is illustrated for a hot rod rolling (HRR) and cooling process chain problem by exploring the processing paths and microstructure in an inverse manner to produce a rod with specific mechanical properties. The proposed method and the exploration framework are generic and support the integrated decision-based design exploration of a process chain to realize an end product by tailoring material microstructures and processing paths.

Additive manufacturing (AM) has enabled the creation of a near infinite set of functionally graded materials (FGMs). One limitation on the manufacturability and usefulness of these materials is the presence of undesirable phases along the gradient path. For example, such phases may increase brittleness, diminish corrosion resistance, or severely compromise the printability of the part altogether. In the current work, a design methodology is proposed to plan an FGM gradient path for any number of elements that avoids undesirable phases at a range of temperatures. Gradient paths can also be optimized for a cost function. A case study is shown to demonstrate the effectiveness of the methodology in the Fe–Ni–Cr system. Paths were successfully planned from 316 L Stainless Steel (316 L SS) to pure Cr that either minimize path length or maximize separation from undesirable phases. Examinations on the stochastic variability, parameter dependency, and computational efficiency of the method are also presented. Several avenues of future research are proposed that could improve the manufacturability, utility, and performance of FGMs through gradient path design.

For a number of emerging mechatronics applications, dielectric elastomers (DEs) appear as a more energy efficient, lightweight, and low-cost solution with respect to established actuation technologies based, e.g., on solenoids or pneumatic cylinders. In addition to large strain, low power consumption, and high flexibility, DE actuators (DEA) are also highly scalable. Since DE membranes can be easily manufactured in different sizes and shapes, an effective approach to scale their performance is based on properly designing the material geometry. Clearly, to perform an optimal scaling the relation between material geometry and performance has to be properly investigated. In this paper, performance scaling by means of geometry is studied for circular out-of-plane (COP) DEAs. Such actuators consist of a silicone elastomer membrane sandwiched between two electrodes (carbon black silicone mixture). DEAs with six different geometries are manufactured, and a model-based strategy is used to find an experimental relationship between geometry and electro-mechanical behavior. In addition, an effective and computationally efficient method for predicting force–displacement characteristics of different geometries is presented. The proposed method allows to easily adapt DEAs to different applications in terms of stroke and force requirement, while minimizing at the same time both characterization and prototyping effort.