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Earlier sources: Manufacturing
This hardware thread follows manufacturing through original research, public projects, and source reports. Read the evidence below to see which developments are represented in SYMTRI’s archive.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗48 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- Hacker News · Posted on Hacker News · First archived
Empirical results fine-tuning π0.5 on a real manufacturing task ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: An open-source manufacturing ERP/MES/QMS ↗
Hacker News discussion.
- OpenAlex · Published · First archived
Investigating nozzle-based additive manufacturing dynamics with high-speed imaging and machine learning ↗
Abstract Additive manufacturing (AM) offers great design flexibility but remains constrained by the complexity of tuning processing parameters from a vast parameter search space either by traditional trial-and-error experimental approach or expensive computational fluid dynamics simulations. Further, AM processes involve critical transient phenomena at very small timescales ranging from microsecond to millisecond, which are difficult to observe and characterize experimentally. This work presents a novel approach that combines high-speed photography with machine learning (ML) to model and predi…
- OpenAlex · Published · First archived
Optimization of Downskin Surface Quality in Laser Powder Bed Fusion of IN718 for Supportless Manufacturing ↗
Support structures remain a major limitation of laser powder bed fusion (LPBF), particularly for downward-facing surfaces prone to powder adhesion, dross formation, melt-pool instability, and staircase effects. This study investigates the downskin optimization of Inconel 718 (IN718), fabricated at a 35° inclination using a Renishaw RenAM 500S Flex. A Taguchi L25 orthogonal array was employed to evaluate three process parameters: hatch power (225–425 W), remelting times (0–4), and printing velocity (1100–1900 mm/s). Surface topography was characterized using an Alicona InfiniteFocus G5, with Sa…
- OpenAlex · Published · First archived
Neuroergonomic Assessment of Virtual Reality Training in Additive Manufacturing Using Wearable EEG ↗
Virtual reality (VR) has emerged as a promising training platform for additive manufacturing (AM) training. Yet, its neuroergonomic effects remain underexplored. This study addressed the gap using a multimodal approach combining subjective measures with wearable electroencephalography (EEG). Twenty-six participants were randomly assigned to either a VR or a lecture-based training condition, and all completed both declarative and procedural learning tasks in AM. Results showed that VR training significantly increased subjective engagement, cognitive control, and attention compared to lecture-ba…
- OpenAlex · Published · First archived
Additive manufacturing in rocket propulsion systems: a literature review ↗
Abstract Additive manufacturing (AM), commonly referred to as 3D printing, enables the layer-by-layer construction of components and offers distinct advantages over traditional subtractive manufacturing methods. These include reduced lead times, lower material waste, cost savings and the ability to fabricate complex geometries that are otherwise difficult or impossible to achieve. These benefits have made AM particularly attractive for aerospace applications, especially in the production of rocket propulsion systems. This review examines the role of AM in manufacturing propulsion-specific comp…
- OpenAlex · Published · First archived
Vibration Effects and Mitigation Strategies for In-Transit Additive Manufacturing: A Scoping Review ↗
Additive manufacturing (AM) is increasingly being considered for deployment in dynamic operating environments to enable distributed, on-demand manufacturing closer to the point of need. However, sustained environmental vibration presents challenges to print quality, process stability, and operational reliability, particularly for material-extrusion systems intended for in-transit operation. This scoping review maps evidence on vibration-induced effects, vibration characteristics, and mitigation strategies relevant to in-transit AM using the PRISMA-ScR framework. Systematic searches of Web of S…
- OpenAlex · Published · First archived
Optimisation of wire-arc additive manufacturing parameters for processing SS316L using finite element analysis and artificial neural networks ↗
This study investigated the influence of WAAM process parameters on the thermo-mechanical behaviour of SS316L using finite element analysis and a bidirectional Artificial Neural Network (ANN). Simufact Welding was used to simulate multi-layer wall deposition and predict distortion behaviour. Experimental validation showed that the unclamped substrate ends experienced a maximum distortion of 4.50 mm due to thermal shrinkage and bending effects. The ANN demonstrated high prediction capability, with the forward model achieving a root mean square error of 1.24 mm. The backward model predicted opti…
- OpenAlex · Published · First archived
Dynamic Skill Demand Prediction for New Energy Manufacturing Using a Knowledge Graph-Enhanced Spatiotemporal Graph Neural Network ↗
Accurate and forward-looking skill demand prediction is critical for workforce planning, curriculum development, and talent cultivation in the rapidly evolving new energy manufacturing sector. Existing approaches often rely on static occupational categories, retrospective skill taxonomies, or purely temporal models and therefore struggle to capture heterogeneous semantic dependencies, temporal evolution, and event-driven demand shocks. This study proposes an integrated framework that combines a point-in-time New Energy Manufacturing Skill Knowledge Graph (NEM-SKG) with a knowledge-graph-enhanc…
- OpenAlex · Published · First archived
Machine learning enabled cross-stage prediction of cathode performance using manufacturing parameters and measured electrode descriptors ↗
Optimisation of lithium-ion battery manufacturing has traditionally relied on time-consuming and resource-intensive experimental trial-and-error. Here, we fabricated LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) cathodes using varied manufacturing parameters, generating an experimentally measured laboratory dataset comprising 160 electrodes and 99 coin cells across varied manufacturing conditions. Group A and Group B cathodes were matched within manufacturing batches. Group A was uncalendered, whereas Group B was calendered. We built gradient boosting regressor (GBR) models to predict the cathode proper…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Manufacturing
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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