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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Automated Generation of Integrated Digital and Spiking Neuromorphic Machine Learning Accelerators

The growing numbers of application areas for artificial intelligence (AI) methods have led to an explosion of domain-specific accelerators that could support every new machine learning (ML) algorithm advancement, clearly highlighting the need for a capability to quickly and automatically transition from algorithm definition to hardware implementation and explore design space along a variety of SWaP (size, weight and Power). The software defined architectures (SODA) synthesizer implements a compiler-based modular infrastructure for the end-to-end generation of machine learning accelerators from high-level frameworks to hardware description language. At the same time, neuromorphic computing, by mimicking how the brain operates, promises to perform artificial intelligence tasks at efficiencies orders of magnitude higher than the current conventional tensor-processing based accelerators, as demonstrated by a variety of specialized designs leveraging Spiking Neural Networks (SNNs). Nevertheless, the mapping of an artificial neural network (ANN) to solutions supporting SNNs is still a non-trivial and very device-specific task, and completely lack the possibility to design hybrid systems that integrate conventional and spiking neural models. In this paper we discuss the support for such an integrated generation leveraging the SODA Synthesizer framework and its modular structure. In particular, we present a new MLIR dialect (part of the SODA frontend) that allows expressing spiking neural network features (e.g., available resources, spiking sequences, analog signal reading, etc.) and illustrate how it enables mapping to Spiking Neurons and deployment to the related specialized hardware (which, in the digital domain, could be generated through the other existing layers of the SODA Synthesizer). We then discuss the opportunities for even deeper integration afforded by the hardware compilation infrastructure, providing a path towards the generation of complex heterogeneous artificial intelligence systems.

Curzel, Serena↗
DOE OSTI2021

Genomic characterization of three marine fungi, including Emericellopsis atlantica sp. nov. with signatures of a generalist lifestyle and marine biomass degradation

ABSTRACT Marine fungi remain poorly covered in global genome sequencing campaigns; the 1000 fungal genomes (1KFG) project attempts to shed light on the diversity, ecology and potential industrial use of overlooked and poorly resolved fungal taxa. This study characterizes the genomes of three marine fungi: Emericellopsis sp. TS7, wood-associated Amylocarpus encephaloides and algae-associated Calycina marina. These species were genome sequenced to study their genomic features, biosynthetic potential and phylogenetic placement using multilocus data. Amylocarpus encephaloides and C. marina were placed in the Helotiaceae and Pezizellaceae (Helotiales) , respectively, based on a 15-gene phylogenetic analysis. These two genomes had fewer biosynthetic gene clusters (BGCs) and carbohydrate active enzymes (CAZymes) than Emericellopsis sp. TS7 isolate. Emericellopsis sp. TS7 ( Hypocreales , Ascomycota ) was isolated from the sponge Stelletta normani . A six-gene phylogenetic analysis placed the isolate in the marine Emericellopsis clade and morphological examination confirmed that the isolate represents a new species, which is described here as E. atlantica . Analysis of its CAZyme repertoire and a culturing experiment on three marine and one terrestrial substrates indicated that E. atlantica is a psychrotrophic generalist fungus that is able to degrade several types of marine biomass. FungiSMASH analysis revealed the presence of 35 BGCs including, eight non-ribosomal peptide synthases (NRPSs), six NRPS-like, six polyketide synthases, nine terpenes and six hybrid, mixed or other clusters. Of these BGCs, only five were homologous with characterized BGCs. The presence of unknown BGCs sets and large CAZyme repertoire set stage for further investigations of E. atlantica . The Pezizellaceae genome and the genome of the monotypic Amylocarpus genus represent the first published genomes of filamentous fungi that are restricted in their occurrence to the marine habitat and form thus a valuable resource for the community that can be used in studying ecological adaptions of fungi using comparative genomics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Comparison of Auxenochlorella protothecoides and Chlorella spp. Chloroplast Genomes: Evidence for Endosymbiosis and Horizontal Virus-like Gene Transfer

Resequencing of the chloroplast genome (cpDNA) of Auxenochlorella protothecoides UTEX 25 was completed (GenBank Accession no. KC631634.1), revealing a genome size of 84,576 base pairs and 30.8% GC content, consistent with features reported for the previously sequenced A. protothecoides 0710, (GenBank Accession no. KC843975). The A. protothecoides UTEX 25 cpDNA encoded 78 predicted open reading frames, 32 tRNAs, and 4 rRNAs, making it smaller and more compact than the cpDNA genome of C. variabilis (124,579 bp) and C. vulgaris (150,613 bp). By comparison, the compact genome size of A. protothecoides was attributable primarily to a lower intergenic sequence content. The cpDNA coding regions of all known Chlorella species were found to be organized in conserved colinear blocks, with some rearrangements. The Auxenochlorella and Chlorella species genome structure and composition were similar, and of particular interest were genes influencing photosynthetic efficiency, i.e., chlorophyll synthesis and photosystem subunit I and II genes, consistent with other biofuel species of interest. Phylogenetic analysis revealed that Prototheca cutis is the closest known A. protothecoides relative, followed by members of the genus Chlorella. The cpDNA of A. protothecoides encodes 37 genes that are highly homologous to representative cyanobacteria species, including rrn16, rrn23, and psbA, corroborating a well-recognized symbiosis. Several putative coding regions were identified that shared high nucleotide sequence identity with virus-like sequences, suggestive of horizontal gene transfer. Despite these predictions, no corresponding transcripts were obtained by RT-PCR amplification, indicating they are unlikely to be expressed in the extant lineage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Properties of protein unfolded states suggest broad selection for expanded conformational ensembles

Much attention is being paid to conformational biases in the ensembles of intrinsically disordered proteins. However, it is currently unknown whether or how conformational biases within the disordered ensembles of foldable proteins affect function in vivo. Recently, we demonstrated that water can be a good solvent for unfolded polypeptide chains, even those with a hydrophobic and charged sequence composition typical of folded proteins. These results run counter to the generally accepted model that protein folding begins with hydrophobicity-driven chain collapse. Here we investigate what other features, beyond amino acid composition, govern chain collapse. We found that local clustering of hydrophobic and/or charged residues leads to significant collapse of the unfolded ensemble of pertactin, a secreted autotransporter virulence protein from Bordetella pertussis , as measured by small angle X-ray scattering (SAXS). Sequence patterns that lead to collapse also correlate with increased intermolecular polypeptide chain association and aggregation. Crucially, sequence patterns that support an expanded conformational ensemble enhance pertactin secretion to the bacterial cell surface. Similar sequence pattern features are enriched across the large and diverse family of autotransporter virulence proteins, suggesting sequence patterns that favor an expanded conformational ensemble are under selection for efficient autotransporter protein secretion, a necessary prerequisite for virulence. More broadly, we found that sequence patterns that lead to more expanded conformational ensembles are enriched across water-soluble proteins in general, suggesting protein sequences are under selection to regulate collapse and minimize protein aggregation, in addition to their roles in stabilizing folded protein structures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

V-HAMSTeR v1.0.0

V-HAMSTeR is a bioinformatics software tool designed to predict the hosts of viruses directly from genomic sequences. It can be used by researchers to predict animal, prokaryotic, plant, protist or fungal viral hosts including viruses that may be fragmented or discovered in environmental metagenomic datasets. Features & Uses: The software employs a novel dual-stream deep learning architecture that dynamically fuses implicit sequence embeddings from a genomic foundation model with 13 explicit, handcrafted biological features (e.g., coding density and strand switch rates). To ensure maximum reliability, V=HAMSTeR deploys a 5-fold deep ensemble calibrated via Joint Temperature Scaling, providing users with statistically rigorous confidence probabilities. It also features an automated sequence chunking and mean-pooling module to seamlessly process variable-length contigs. Advantages Over Similar Technologies: Existing tools (e.g., IPEV, RNAVirHost) typically rely on either basic k-mers or isolated neural networks. V-HAMSTeR's hybrid architecture captures both broad genomic context and specific biological motifs that standalone foundation models often miss. Furthermore, unlike competitor tools that struggle with incomplete data or exhibit extreme overconfidence, V-HAMSTeR is explicitly benchmarked and mathematically calibrated for fragmented assemblies (1kb–10kb). This makes it uniquely robust, accurate, and trustworthy for the messy reality of real-world environmental viromics.

Grigson, Susie [Lawrence Berkeley National Laborat↗
DOE OSTI2024

Genomic factors shaping codon usage across the Saccharomycotina subphylum

Codon usage bias, or the unequal use of synonymous codons, is observed across genes, genomes, and between species. It has been implicated in many cellular functions, such as translation dynamics and transcript stability, but can also be shaped by neutral forces. We characterized codon usage across 1,154 strains from 1,051 species from the fungal subphylum Saccharomycotina to gain insight into the biases, molecular mechanisms, evolution, and genomic features contributing to codon usage patterns. We found a general preference for A/T-ending codons and correlations between codon usage bias, GC content, and tRNA-ome size. Codon usage bias is distinct between the 12 orders to such a degree that yeasts can be classified with an accuracy >90% using a machine learning algorithm. We also characterized the degree to which codon usage bias is impacted by translational selection. We found it was influenced by a combination of features, including the number of coding sequences, BUSCO count, and genome length. Our analysis also revealed an extreme bias in codon usage in the Saccharomycodales associated with a lack of predicted arginine tRNAs that decode CGN codons, leaving only the AGN codons to encode arginine. Analysis of Saccharomycodales gene expression, tRNA sequences, and codon evolution suggests that avoidance of the CGN codons is associated with a decline in arginine tRNA function. Consistent with previous findings, codon usage bias within the Saccharomycotina is shaped by genomic features and GC bias. However, we find cases of extreme codon usage preference and avoidance along yeast lineages, suggesting additional forces may be shaping the evolution of specific codons.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A machine-learning-aided data recovery approach for predicting multi-material thermal behaviors in advanced test reactor capsules

Instrumented experiments conducted at test reactors are essential to the deployment of new advanced reactor systems. Designing new experiments and generating data on specific reactor conditions require significant investments in terms of both time and cost. Finite element analysis software can be used to create high-fidelity models of experiment environments in order to support the actual experiments, but computation time remains a concern in terms of applying outcomes to real-time usage of data (e.g., a digital twin [DT]). Here, the present research proposes a machine-learning (ML) aided approach to making temperature and displacement predictions based on the thickness of the outer gas gap on the experimental capsule used for in-pile demonstration of a novel new thermal conductivity probe in the Advanced Test Reactor (ATR). This capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. Gas gaps existed between the fuel and the rodlet, and between the inner and the outer capsule. The learning data pertained to an experimental capsule's radial distributions of temperature and displacement, as obtained based on Abaqus and the physical features. For the first step of ML sequence, the temperature was predicted using three positional parameters. Next, the displacement was predicted using seven additional parameters. Each physical feature was normalized in order to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement with the simulation results in all cases involving interpolation and extrapolation. Furthermore, data similarity enhancement increased the similarity between the training and the target data, thereby increasing the predictive accuracy of the ML models. In certain extrapolation cases involving limited original ML model accuracy, data similarity enhancement and data recovery was able to somewhat improve this accuracy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Methods for rule-based genome design

Methods and systems for designing, testing, and validating genome designs based on rules or constraints or conditions or parameters or features and scoring are described herein. A computer-implemented method includes receiving data for a known genome and a list of alleles, identifying and removing occurrences of each allele in the known genome, determining a plurality of allele choices with which to replace occurrences in the known genome, generating a plurality of alternative gene sequences for a genome design based on the known genome, wherein each alternative gene sequence comprises a different allele choice, applying a plurality of rules or constraints or conditions or parameters or features to each alternative gene sequence by assigning a score for each rule or constraint or condition or parameter or feature in each alternative gene sequence, resulting in scores for the applied plurality of rules or constraints or conditions or parameters or features, scoring each alternative gene sequence based on a weighted combination of the scores for the plurality of rules or constraints or conditions or parameters or features, and selecting at least one alternative gene sequence as the genome design based on the scoring.

Kuznetsov, Gleb↗
DOE OSTI2023

Unsupervised acoustic detection of fatigue-induced damage modes from wind turbine blades

This paper proposes a new in-situ damage detection approach for wind turbine blades, which leverages blade-internal non-stationary acoustic pressure fluctuations caused by the mechanical loading as the main source of excitation. This acoustic excitation was leveraged for the detection of fatigue-related damage modes on a full-scale wind turbine blade undergoing edgewise fatigue testing. An unsupervised, data-driven structural health monitoring strategy was developed to learn the normal cavity-internal acoustic sequences generated by the blade’s load cycles and to detect damage-related anomalies in the context of those sequences. A linear cepstral-coefficient based feature set was used to characterize the cavity-internal acoustics and LSTM-autoencoders were trained to accurately reconstruct healthy-case sequences. The reconstruction error was then used to characterize anomalous acoustic patterns within the blade cavity. The technique was able to detect a damage event earlier than a strain-based system by 120,000 load cycles.

17 WIND ENERGY↗
DOE OSTI2016

HUBBLE TARANTULA TREASURY PROJECT. V. THE STAR CLUSTER HODGE 301: THE OLD FACE OF 30 DORADUS

Based on color–magnitude diagrams (CMDs) from the Hubble Space Telescope Hubble Tarantula Treasury Project (HTTP) survey, we present the star formation history of Hodge 301, the oldest star cluster in the Tarantula Nebula. The HTTP photometry extends faint enough to reach, for the first time, the cluster pre-main sequence (PMS) turn-on, where the PMS joins the main sequence. Using the location of this feature, along with synthetic CMDs generated with the latest PARSEC models, we find that Hodge 301 is older than previously thought, with an age between 26.5 and 31.5 Myr. From this age, we also estimate that between 38 and 61 Type II supernovae exploded in the region. The same age is derived from the main sequence turn-off, whereas the age derived from the post-main sequence stars is younger and between 20 and 25 Myr. Other relevant parameters are a total stellar mass of ≈8800 ± 800 M {sub ⊙} and average reddening E ( B − V ) ≈ 0.22–0.24 mag, with a differential reddening δE ( B − V ) ≈ 0.04 mag.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Using Explainable Artificial Intelligence to Predict Perovskite Solar Cell Electrical Metastability from Operando Photoluminescence Images in Accelerated Stress Testing

Metal halide perovskite (MHP) solar cells exhibit a metastable response to bias governed by coupled ionic–electronic processes, complicating the conventional reciprocity relation between luminescence intensity and device open-circuit voltage (V oc ). This limits the use of luminescence as a diagnostic for device screening or accelerated stress testing, motivating new approaches that can interpret photoluminescence (PL) signals under nonequilibrium conditions. From the artificial intelligence perspective, we develop an explainable deep learning framework that integrates convolutional neural networks (CNN), long short-term memory (LSTM) layers, and an attention mechanism to learn spatiotemporal features from operando photoluminescence PL image sequences. The model achieves a mean absolute error of ±0.027 V in predicting open-circuit voltage transients and reduces extreme-tail errors by up to 78% compared to physics-based reciprocity calculations. Gradient-weighted Class Activation Mapping (Grad-CAM) provides interpretability by highlighting physically meaningful regions such as electrode edges and emergent defect features. From the engineering application perspective, this framework enables accurate, contactless prediction of device V oc and identification of degradation-relevant features during accelerated aging of perovskite solar cells. This approach demonstrates how explainable AI can enhance operando diagnostics and reliability analysis in photovoltaic devices under nonequilibrium conditions.

14 SOLAR ENERGY↗
DOE OSTI2018

MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples

Microbial communities play important roles in the function and maintenance of various biosystems, ranging from the human body to the environment. A major challenge in microbiome research is the classification of microbial communities of different environments or host phenotypes. The most common and cost-effective approach for such studies to date is 16S rRNA gene sequencing. Recent falls in sequencing costs have increased the demand for simple, efficient and accurate methods for rapid detection or diagnosis with proved applications in medicine, agriculture and forensic science. We describe a reference- and alignment-free approach for predicting environments and host phenotypes from 16S rRNA gene sequencing based on k-mer representations that benefits from a bootstrapping framework for investigating the sufficiency of shallow sub-samples. Deep learning methods as well as classical approaches were explored for predicting environments and host phenotypes. A k-mer distribution of shallow sub-samples outperformed Operational Taxonomic Unit (OTU) features in the tasks of body-site identification and Crohn’s disease prediction. Aside from being more accurate, using k-mer features in shallow sub-samples allows (i) skipping computationally costly sequence alignments required in OTU-picking and (ii) provided a proof of concept for the sufficiency of shallow and short-length 16S rRNA sequencing for phenotype prediction. In addition, k-mer features predicted representative 16S rRNA gene sequences of 18 ecological environments, and 5 organismal environments with high macro-F1 scores of 0.88 and 0.87. For large datasets, deep learning outperformed classical methods such as Random Forest and Support Vector Machine.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Genomic Language model for Annotation of Repetitive Elements (GLARE) v1.0

GLARE (Genomic Language model for Annotation of Repetitive Elements) is a tool that classifies transposable elements (TEs)—the mobile, repetitive DNA sequences that make up large fractions of eukaryotic genomes. GLARE fine-tunes the NTv3-650M genomic language model on a harmonized collection of curated TE sequences from the PanTEon and Repbase reference databases, assigning each input sequence to one of 11 orders and 32 superfamilies in a Wicker-compatible taxonomy. Features. From nucleotide FASTA input, GLARE outputs per-sequence predictions, class summaries, composition figures, and an annotated FASTA. It provides calibrated confidence scores with optional abstention and runs on CPU or GPU. Uses. GLARE serves as a classification component in genome-annotation pipelines, downstream of TE discovery, supporting genome annotation and comparative and evolutionary genomics. Advantages. GLARE is the first repeat-element classifier to leverage a pretrained genomic language model. Combined with multi-database training, this approach outperformed all nine classifiers in the PanTEon benchmark, generalized better to unseen taxonomic clades, and remained robust to sequence orientation—a common failure mode of existing tools.

Bruna, Tomas [Lawrence Berkeley National Laborator↗
DOE OSTI2020

Sequencing and Analysis of the Sex Determination Region of Populus trichocarpa

The ages and sizes of a sex-determination region (SDR) are difficult to determine in non-model species. Due to the lack of recombination and enrichment of repetitive elements in SDRs, the quality of assembly with short sequencing reads is universally low. Unique features present in the SDRs help provide clues about how SDRs are established and how they evolve in the absence of recombination. Several Populus species have been reported with a male heterogametic configuration of sex (XX/XY system) mapped on chromosome 19, but the exact location of the SDR has been inconsistent among species, and thus far, none of these SDRs has been fully assembled in a genomic context. Here we identify the Y-SDR from a Y-linked contig directly from a long-read PacBio assembly of a Populus trichocarpa male individual. We also identified homologous gene sequences in the SDR of P. trichocarpa and the SDR of the W chromosome in Salix purpurea. We show that inverted repeats (IRs) found in the Y-SDR and the W-SDR are lineage-specific. We hypothesize that, although the two IRs are derived from the same orthologous gene within each species, they likely have independent evolutionary histories. Furthermore, the truncated inverted repeats in P. trichocarpa may code for small RNAs that target the homologous gene for RNA-directed DNA methylation. These findings support the hypothesis that diverse sex-determining systems may be achieved through similar evolutionary pathways, thereby providing a possible mechanism to explain the lability of sex-determination systems in plants in general.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Dynamic Graph Sequence Data from Simulated Neutron Reflectometry Measurements

This dataset comprises dynamic graph sequences derived from simulated in-situ neutron reflectometry measurements, capturing the gradual evolution of a layer structure over time. Each graph sequence represents a synthetic sample, with node features detailing the scattering vector and corresponding reflectivity measurements, while adjacency matrices have corresponding reference material parameters attached as metadata. The dataset spans multiple sets, each with a different number of sequences, offering a comprehensive basis for training models that handle dynamic input sequences with embedded physics. This dataset is particularly suited for tackling inverse problems with hidden physical states that evolve over time, challenges that are typically difficult to address using conventional iterative fitting methods.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Phyllosticta paracitricarpa is synonymous with the EU quarantine fungus P. citricarpa based on phylogenomic analyses

Phyllosticta citricarpa is an important citrus-pathogen and a quarantine organism in the European Union. Its recently described relative, P. paracitricarpa, is very closely related and not listed as a quarantine organism. P. paracitricarpa is very difficult to distinguish from P. citricarpa, since its morphological features overlap and the barcoding gene sequences that were originally used to delimit them as distinct species have a low number of species-specific polymorphisms that have subsequently been shown to overlap between the two clades. Therefore, we performed extensive genomic analyses to determine whether the genetic variation between P. citricarpa and P. paracitricarpa strains should be considered to represent infraspecific variation within P. citricarpa, or whether it is indicative of distinct species. Using a phylogenomic analysis with 3,000 single copy ortholog genes and whole-genome comparisons, we determined that the variation between P. citricarpa and P. paracitricarpa can be considered as infraspecies variation within P. citricarpa. We also determined the level of variation in mitochondrial assemblies of several Phyllosticta species and concluded there are only minimal differences between the assemblies of P. citricarpa and P. paracitricarpa. Thus, using several orthogonal approaches, we here demonstrate that variation within the nuclear and mitochondrial genomes of other Phyllosticta species is larger than variation between genomes obtained from P. citricarpa and P. paracitricarpa strains. Thus, P. citricarpa and P. paracitricarpa should be considered as conspecific.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Proteogenomic characterization of difficult-to-treat breast cancer with tumor cells enriched through laser microdissection

Abstract Background Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer death among women globally. Despite advances, there is considerable variation in clinical outcomes for patients with non-luminal A tumors, classified as difficult-to-treat breast cancers (DTBC). This study aims to delineate the proteogenomic landscape of DTBC tumors compared to luminal A (LumA) tumors. Methods We retrospectively collected a total of 117 untreated primary breast tumor specimens, focusing on DTBC subtypes. Breast tumors were processed by laser microdissection (LMD) to enrich tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor preparation, followed by whole genome sequencing, paired-end RNA sequencing, global proteomics and phosphoproteomics. Differential feature analysis, pathway analysis and survival analysis were performed to better understand DTBC and investigate biomarkers. Results We observed distinct variations in gene mutations, structural variations, and chromosomal alterations between DTBC and LumA breast tumors. DTBC tumors predominantly had more mutations inTP53,PLXNB3, Zinc finger genes, and fewer mutations inSDC2,CDH1,PIK3CA,SVIL, andPTEN. Notably, Cytoband 1q21, which contains numerous cell proliferation-related genes, was significantly amplified in the DTBC tumors. LMD successfully minimized stromal components and increased RNA–protein concordance, as evidenced by stromal score comparisons and proteomic analysis. Distinct DTBC and LumA-enriched clusters were observed by proteomic and phosphoproteomic clustering analysis, some with survival differences. Phosphoproteomics identified two distinct phosphoproteomic profiles for high relapse-risk and low relapse-risk basal-like tumors, involving several genes known to be associated with breast cancer oncogenesis and progression, includingKIAA1522,DCK,FOXO3,MYO9B,ARID1A,EPRS,ZC3HAV1, andRBM14. Lastly, an integrated pathway analysis of multi-omics data highlighted a robust enrichment of proliferation pathways in DTBC tumors. Conclusions This study provides an integrated proteogenomic characterization of DTBC vs LumA with tumor cells enriched through laser microdissection. We identified many common features of DTBC tumors and the phosphopeptides that could serve as potential biomarkers for high/low relapse-risk basal-like BC and possibly guide treatment selections.

Oncology↗
DOE OSTI2016

Additive Manufacturing of Tooling for Refrigeration Cabinet Foaming Processes

The primary objective of this project was to leverage the Big Area Additive Manufacturing (BAAM) process and materials into a long term, quick change tooling concept to drastically reduce product lead and development timelines and costs. Current refrigeration foam molds are complicated to manufacture involving casting several aluminum parts in an approximate shape, machining components of the molds and post fitting and shimming of the parts in an articulated fixture. The total process timeline can take over 6 months. The foaming process is slower than required for production, therefore multiple fixtures, 10 to 27, are required per refrigerator model. Molds are particular to a specific product configuration making mixed model assembly challenging for sequencing, mold changes or auto changeover features. The initial goal was to create a tool leveraging the ORNL materials and additive process to build a tool in 4 to 6 weeks or less. A secondary goal was to create common fixture cores and provide lightweight fixture sections that could be revised in a very short time to increase equipment flexibility reduce lead times, lower the barriers to first production trials, and reduce tooling costs.

3d Printing↗