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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Autonomous nondestructive evaluation of resistance spot welded joints

The application of non-destructive evaluation approaches has attracted strong interests in modern automotive industries. Here, we present an autonomous deep-computing framework to analyze raw videos from infrared systems and to predict weld nugget shape and size with unprecedented accuracy and speed. In a comprehensive training and testing experiment with 90 videos (seven sets of welding material stack-ups), a new method was developed to assemble sufficient datasets for neural network training. Our framework successfully predicts all the nugget shapes with F1 scores that range from 0.84 to 0.92. The total training time on Nvidia DGX station takes less than 10 min for each set of welding material stack-up. The real inference time of an individual dataset (with 30 video frames) takes about 0.005 s. The procedure and methods developed in the study can be applied to other image-based weld property prediction, as well as other manufacturing processes. Furthermore, our well-trained neural networks take limited memory resources (2.3 MB) and are suitable for embedded microprocessors for in-situ welding quality control as edge computing within an intelligent welding framework.

42 ENGINEERING↗
DOE OSTI2025

A Semi-supervised Hybrid Machine Learning Framework for the Qualification of Resistance Spot Welds

• Industries requiring high structural integrity, including automotive, aerospace, and construction, place considerable significance on weld quality classification. • The inspection normally involves human expertise through predefined quality metrics that are subjective, error-prone, and time-intensive • The challenge to classification model development is the scarcity of labeled data and imbalanced distributions in the data that are labeled. • This work develops a new hybrid methodology that achieves clustering using KMeans++ together with supervised classification to overcome these challenges. • The ensemble-based classifiers were identified as optimal, with accuracy enhancements of up to 8% using the pseudo-labeled dataset. • The work provides practical insight into feature engineering and machine learning integration in industrial quality assurance applications.

Rogers, Jeremy K. [Savannah River National Laborat↗
NASA NTRS1982

Adaptive materials processing

New sensors for both arc welding and resistance spot welding processes were studied. For arc welding, the arc itself is used as the sensor. Minor changes in both composition and geometry of the plasma result in fluctuations in the arc voltage. While the process as currently employed is an off-line system, development of an on-line process controller would be possible once the optimum analysis technique is developed. It has been shown that for resistance spot welding, the dynamic resistance of the weld joint can be correlated to growth of the fusion zone. It is indicated that this technique is not as useful for projection welds as for spot welds.

Eager, T. W.↗
DOE OSTI2021

A review of technologies for welding magnesium alloys to steels

Here we review various state-of-the-art methods for welding magnesium alloys and steels using different joint configurations. Microstructural characterizations indicate that four microstructures may form at the Mg/steel interface after welding: unwelded gap, metal oxides, solid solutions, or intermetallic compounds. Reaction products at the Mg/steel interface vary with different welding methods, alloying elements in base materials, interlayers or coatings applied, and preparations of base material before welding. Regarding mechanical property characterization, (a) lap tensile shear testing for lap-welded and spot-welded joints, (b) tensile testing for butt-welded joints and (c) fatigue properties of lap-welded and spot-welded joints are summarized and compared, separately. Reaction products at the Mg/steel interface are correlated with mechanical properties. Finally, ways to enhance Mg/steel joint strength, such as introducing interlocking features during friction stir lap and butt welding, are discussed.

Wang, Tianhao↗
DOE OSTI2024

Spot pattern welding scanning strategy for sensor embedding and residual stress reduction in laser-foil-printing additive manufacturing

Here, this paper aims to present spot pattern welding (SPW) as a scanning strategy for laser-foil-printing (LFP) additive manufacturing (AM) in place of the previously used continuous pattern welding (CPW) (line-raster scanning). The SPW strategy involves generating a sequence of overlapping spot welds on the metal foil, allowing the laser to form dense and uniform weld beads. This in turn reduces thermal gradients, promotes material consolidation and helps mitigate process-related risks such as thermal cracking, porosity, keyholing and Marangoni effects. 304L stainless steel (SS) feedstock is used to fabricate test specimens using the LFP system. Imaging techniques are used to examine the melt pool dimensions and layer bonding. In addition, the parts are evaluated for residual stresses, mechanical strength and grain size. Compared to CPW, SPW provides a more reliable heating/cooling relationship that is less dependent on part geometry. The overlapping spot welds distribute heat more evenly, minimizing the risk of elevated temperatures during the AM process. In addition, the resulting dense and uniform weld beads contribute to lower residual stresses in the printed part. To the best of the authors’ knowledge, this is the first study to thoroughly investigate SPW as a scanning strategy using the LFP process. In general, SPW presents a promising strategy for securing embedded sensors into LFP parts while minimizing residual stresses.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Controlled-Pinch Spot Welder

Handheld spot-welding gun clamps workpiece under air pressure instead of under operator's hand pressure. Trigger actuates both air pressure and welding current. Makes spot welds more repeatable and reliable. Reduces amount of manual labor required and enables welds to be made at faster rate. Compact and light in weight, reaches restricted area that ordinary commercial welding guns cannot.

Morgen, Gene E.↗