We present a high-level architecture for how artificial intelligences might advance and accumulate scientific and technological knowledge, inspired by emerging perspectives on how human intelligences advance and accumulate such knowledge. Agents advance knowledge by exercising a technoscientific method—an interacting combination of scientific and engineering methods. The technoscientific method maximizes a quantity we call “useful learning” via more-creative implausible utility (including the “aha!” moments of discovery), as well as via less-creative plausible utility. Society accumulates the knowledge advanced by agents so that other agents can incorporate and build on to make further advances. The proposed architecture is challenging but potentially complete: its execution might in principle enable artificial intelligences to advance and accumulate an equivalent of the full range of human scientific and technological knowledge.
Future machine learning strategies for materials process optimization will likely replace human capital-intensive artisan research with autonomous and/or accelerated approaches. Such automation enables accelerated multimodal characterization that simultaneously minimizes human errors, lowers costs, enhances statistical sampling, and allows scientists to allocate their time to critical thinking instead of repetitive manual tasks. Previous acceleration efforts to synthesize and evaluate materials have often employed elaborate robotic self-driving laboratories or used specialized strategies that are difficult to generalize. Herein we describe an implemented workflow for accelerating the multimodal characterization of a combinatorial set of 915 electroplated Ni and Ni–Fe thin films resulting in a data cube with over 160,000 individual data files. Our acceleration strategies do not require manufacturing-scale resources and are thus amenable to typical materials research facilities in academic, government, or commercial laboratories. The workflow demonstrated the acceleration of six characterization modalities: optical microscopy, laser profilometry, X-ray diffraction, X-ray fluorescence, nanoindentation, and tribological (friction and wear) testing, each with speedup factors ranging from 13–46x. In addition, automated data upload to a repository using FAIR data principles was accelerated by 64x.
Mechanical metamaterials are regularly implemented in engineering applications due to their unique properties derived from their structural geometry and material composition. This study incorporates deep reinforcement learning, a subset of machine learning that teaches an agent to complete a task through interactive experiences, into mechanical metamaterial design. The approach creates a design environment for the reinforcement learning agent to iteratively construct metamaterials with tailorable deformation and hysteretic characteristics. Validation involved producing metamaterials with a thermoplastic polyurethane (TPU) base material that exhibited the deformation response of expanded thermoplastic polyurethane (E-TPU) while maximizing or minimizing hysteresis in cyclic compression. This alignment confirmed the feasibility of tailoring deformation and energy manipulation using mechanical metamaterials. The agent's generalizability was tested by tasking it to create various metamaterials with distinct loading deformation responses and specific hysteresis goals in a simulated setting. The agent consistently delivered metamaterials that met loading curve criteria and demonstrated favorable energy return. This work demonstrates the potential of deep reinforcement learning as a rapid and effective tool for designing mechanical metamaterials with customizable traits. It ushers in the possibility of on-demand metamaterial design solutions, opening avenues across industries like footwear, wearables, and medical equipment.
Mechanical metamaterials are artificial materials with unique global properties due to the structural geometry and material composition of their unit cell. Typically, mechanical metamaterial unit cells are designed such that, when tessellated, they exhibit unique mechanical properties such as zero or negative Poisson's ratio and negative stiffness. Beyond these applications, mechanical metamaterials can be used to achieve tailorable nonlinear deformation responses. Computational methods such as gradient-based topology optimization (TO) and size/shape optimization (SSO) can be implemented to design these metamaterials. However, both methods can lead to suboptimal solutions or a lack of generalizability. Therefore, this research used deep reinforcement learning (DRL), a subset of deep machine learning that teaches an agent to complete tasks through interactive experiences, to design mechanical metamaterials with specific nonlinear deformation responses in compression or tension. The agent learned to design the unit cells by sequentially adding material to a discrete design domain and being rewarded for achieving the desired deformation response. After training, the agent successfully designed unit cells to exhibit desired deformation responses not experienced during training. This work shows the potential of DRL as a high-level design tool for a wide array of engineering applications.
Disastrous consequences can result from defects in manufactured parts—particularly the high consequence parts developed at Sandia. Identifying flaws in as-built parts can be done with nondestructive means, such as X-ray Computed Tomography (CT). However, due to artifacts and complex imagery, the task of analyzing the CT images falls to humans. Human analysis is inherently unreproducible, unscalable, and can easily miss subtle flaws. We hypothesized that deep learning methods could improve defect identification, increase the number of parts that can effectively be analyzed, and do it in a reproducible manner. We pursued two methods: 1) generating a defect-free version of a scan and looking for differences (PandaNet), and 2) using pre-trained models to develop a statistical model of normality (Feature-based Anomaly Detection System: FADS). Both PandaNet and FADS provide good results, are scalable, and can identify anomalies in imagery. In particular, FADS enables zero-shot (training-free) identification of defects for minimal computational cost and expert time. It significantly outperforms prior approaches in computational cost while achieving comparable results. FADS’ core concept has also shown utility beyond anomaly detection by providing feature extraction for downstream tasks.
Advances in machine learning algorithms and increased computational efficiencies give engineers new capabilities and tools to apply to engineering design. Machine learning models can approximate complex functions and, therefore, can be useful for various tasks in the engineering design workflow. This paper investigates using reinforcement learning (RL), a subset of machine learning that teaches an agent to complete a task through accumulating experiences in an interactive environment, to automate the designing of 2D discretized topologies. RL agents use past experiences to learn sequential sets of actions to best achieve some objective. In the proposed environment, an RL agent can make sequential decisions to design a topology by removing elements to best satisfy compliance minimization objectives. After each action, the agent receives feedback by evaluating how well the current topology satisfies the design objectives. After training, the agent was tasked with designing optimal topologies under various load cases. The agent's proposed designs had similar or better compliance minimization performance to those produced by traditional gradient-based topology optimization methods. These results show that a deep RL agent can learn generalized design strategies to satisfy multi-objective design tasks and, therefore, shows promise as a tool for arbitrarily complex design problems across many domains.
With the proliferation of additive manufacturing and 3D printing technologies, a broader palette of material properties can be elicited from cellular solids, also known as metamaterials, architected foams, programmable materials, or lattice structures. Metamaterials are designed and optimized under the assumption of perfect geometry and a homogeneous underlying base material. Yet in practice real lattices contain thousands or even millions of complex features, each with imperfections in shape and material constituency. While the role of these defects on the mean properties of metamaterials has been well studied, little attention has been paid to the stochastic properties of metamaterials, a crucial next step for high reliability aerospace or biomedical applications. In this work we show that it is precisely the large quantity of features that serves to homogenize the heterogeneities of the individual features, thereby reducing the variability of the collective structure and achieving effective properties that can be even more consistent than the monolithic base material. In this first statistical study of additive lattice variability, a total of 239 strut-based lattices were mechanically tested for two pedagogical lattice topologies (body centered cubic and face centered cubic) at three different relative densities. The variability in yield strength and modulus was observed to exponentially decrease with feature count (to the power −0.5), a scaling trend that we show can be predicted using an analytic model or a finite element beam model. The latter provides an efficient pathway to extend the current concepts to arbitrary/complex geometries and loading scenarios. These results not only illustrate the homogenizing benefit of lattices, but also provide governing design principles that can be used to mitigate manufacturing inconsistencies via topological design.
In-situ additive manufacturing (AM) diagnostic tools (e.g., optical/infrared imaging, acoustic, etc.) already exist to correlate process anomalies to printed part defects. This current work aimed to augment existing capabilities by: 1) Incorporating in-situ imaging w/ machine learning (ML) image processing software (ORNL- developed "Peregrine") for AM process anomaly detection 2) Synchronizing multiple in-situ sensors for simultaneous analysis of AM build events 3) Correlating in-situ AM process data, generated part defects and part mechanical properties The key R&D question investigated was to determine if these new combined hardware/software tools could be used to successfully quantify defect distributions for parts build via SNL laser powder bed fusion (LPBF) machines, aiming to better understand data-driven process-structure-property- performance relationships. High resolution optical cameras and acoustic microphones were successfully integrated in two LPBF machines and linked to the Peregrine ML software. The software was successfully calibrated on both machines and used to image hundreds of layers of multiple builds to train the ML software in identifying printed part vs powder. The software's validation accuracy to identify this aspect increased from 56% to 98.8% over three builds. Lighting conditions inside the chamber were found to significantly impact ML algorithm predictions from in-situ sensors, so these were tailored to each machine's internal framework. Finally, 3D part reconstructions were successfully generated for a build from the compressed stack of layer-wise images. Resolution differences nearest and furthest from the optical camera were discussed. Future work aims to improve optical resolution, increase process anomalies identified, and integrate more sensor modalities.
With the rapid proliferation of additive manufacturing and 3D printing technologies, architected cellular solids including truss-like 3D lattice topologies offer the opportunity to program the effective material response through topological design at the mesoscale. The present report summarizes several of the key findings from a 3-year Laboratory Directed Research and Development Program. The program set out to explore novel lattice topologies that can be designed to control, redirect, or dissipate energy from one or multiple insult environments relevant to Sandia missions, including crush, shock/impact, vibration, thermal, etc. In the first 4 sections, we document four novel lattice topologies stemming from this study: coulombic lattices, multi-morphology lattices, interpenetrating lattices, and pore-modified gyroid cellular solids, each with unique properties that had not been achieved by existing cellular/lattice metamaterials. The fifth section explores how unintentional lattice imperfections stemming from the manufacturing process, primarily sur face roughness in the case of laser powder bed fusion, serve to cause stochastic response but that in some cases such as elastic response the stochastic behavior is homogenized through the adoption of lattices. In the sixth section we explore a novel neural network screening process that allows such stocastic variability to be predicted. In the last three sections, we explore considerations of computational design of lattices. Specifically, in section 7 using a novel generative optimization scheme to design novel pareto-optimal lattices for multi-objective environments. In section 8, we use computational design to optimize a metallic lattice structure to absorb impact energy for a 1000 ft/s impact. And in section 9, we develop a modified micromorphic continuum model to solve wave propagation problems in lattices efficiently.
The advanced materials team investigated the use of additively manufactured metallic lattice structures for mitigating impact response in a Davis gun earth penetrator impact experiment. High-fidelity finite element models were developed and validated with quasistatic experiments. These models were then used to simulate the response of such lattices when subjected to the acceleration loads expected in the Davis gun experiment. Results reveal how the impact mitigation performance of lattices can change drastically at a certain relative density. Based on these observations, an experiment deck was designed to probe the response of lattices with different relative densities during the Davis gun phase 2 shots. The expected performance of these lattices is predicted before testing based on simulation results. The results of the Davis gun phase 2 shots are expected to provide data which will be used to assess the predictive capability of the finite element simulations in such a complex impact environment.
Garland, Anthony G.; Brown, Nathan; Owen, Meredith K.; Desjardins, John D.; Fadel, Georges M.
While using a prosthesis, transtibial amputees can experience pain and discomfort brought on by large pressure gradients at the interface between the residual limb and the prosthetic socket. Current prosthetic interface solutions attempt to alleviate these pressure gradients using soft homogenous liners to reduce and distribute pressures. This research investigates an additively manufactured metamaterial inlay with a tailored mechanical response to reduce peak pressure gradients around the limb. The inlay uses a hyperelastic behaving metamaterial (US10244818) comprised of triangular pattern unit cells, 3D printed with walls of various thicknesses controlled by draft angles. The hyperelastic material properties are modeled using a Yeoh third-order model. The third-order coefficients can be adjusted and optimized, which corresponds to a change in the unit cell wall thickness to create an inlay that can meet the unique offloading needs of an amputee. Finite element analysis simulations evaluated the pressure gradient reduction from (1) a standard homogenous silicone liner, (2) a prosthetist's inlay prescription that utilizes three variations of the metamaterial, and (3) a metamaterial solution with optimized Yeoh third-order coefficients. Compared to a traditional homogenous silicone liner for two unique limb loading scenarios, the prosthetist prescribed inlay and the optimized material inlay can achieve equal or greater pressure gradient reduction capabilities. These preliminary results show the potential feasibility of implementing this metamaterial as a method of personalized medicine for transtibial amputees by creating a customizable interface solution to meet the unique performance needs of an individual patient.