Examination of contacts between strands by electrical measurements and topographical analysis

The contact resistance (crossing and adjacent) between the strands of Rutherford type superconducting cables has been proven to be an essential parameter for the behaviour of the main magnets in accelerators like the LHC. A strong development program has been launched at CERN. Contact resistances were measured by means of a DC method at 4.2 K. The strand deformation and the chemical conditions at the contacts were analyzed in order to interpret the electrical resistances measured by a 3 contacts method on individual strands as well as the resistances measured independently on cables

DC measurement of electrical contacts between strands in superconducting cables for the LHC main magnets

In the LHC main magnets, using Rutherford type cable, the eddy current loss and dynamic magnetic field error depend largely on the electrical resistance between crossing (Rc) and adjacent (Ra) strands. Cables made of strands with pre-selected coatings have been studied at low temperature using a DC electrical method. The significance of the inter-strand contact is explained. The properties of resistive barriers, the DC method used for the resistance measurement on the cable, and sample preparation are described. Finally the resistances are presented under various conditions, and the effect is discussed that the cable treatment has on the contact resistance

An instrument for in situ time-resolved X-ray imaging and diffraction of laser powder bed fusion additive manufacturing processes

In situ X-ray-based measurements of the laser powder bed fusion (LPBF) additive manufacturing process produce unique data for model validation and improved process understanding. Synchrotron X-ray imaging and diffraction provide high resolution, bulk sensitive information with sufficient sampling rates to probe melt pool dynamics as well as phase and microstructure evolution. Here, we describe a laboratory-scale LPBF test bed designed to accommodate diffraction and imaging experiments at a synchrotron X-ray source during LPBF operation. We also present experimental results using Ti-6Al-4V, a widely used aerospace alloy, as a model system. Both imaging and diffraction experiments were carried out at the Stanford Synchrotron Radiation Lightsource. Melt pool dynamics were imaged at frame rates up to 4 kHz with a ∼1.1 μm effective pixel size and revealed the formation of keyhole pores along the melt track due to vapor recoil forces. Diffraction experiments at sampling rates of 1 kHz captured phase evolution and lattice contraction during the rapid cooling present in LPBF within a ∼50 × 100 μm area. We also discuss the utility of these measurements for model validation and process improvement

Subsurface Cooling Rates and Microstructural Response during Laser Based Metal Additive Manufacturing

Laser powder bed fusion (LPBF) is a method of additive manufacturing characterized by the rapid scanning of a high powered laser over a thin bed of metallic powder to create a single layer, which may then be built upon to form larger structures. Much of the melting, resolidification, and subsequent cooling take place at much higher rates and with much higher thermal gradients than in traditional metallurgical processes, with much of this occurring below the surface. We have used in situ high speed X-ray diffraction to extract subsurface cooling rates following resolidification from the melt and above the β-transus in titanium alloy Ti-6Al-4V. We observe an inverse relationship with laser power and bulk cooling rates. The measured cooling rates are seen to correlate to the level of residual strain borne by the minority β-Ti phase with increased strain at slower cooling rates. The α-Ti phase shows a lattice contraction which is invariant with cooling rate. We also observe a broadening of the diffraction peaks which is greater for the β-Ti phase at slower cooling rates and a change in the relative phase fraction following LPBF. These results provide a direct measure of the subsurface thermal history and demonstrate its importance to the ultimate quality of additively manufactured materials

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

Abstract In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance

Laser‐Induced Keyhole Defect Dynamics during Metal Additive Manufacturing

Laser powder bed fusion (LPBF) metal additive manufacturing provides distinct advantages for aerospace and biomedical applications. However, widespread industrial adoption is limited by a lack of confidence in part properties driven by an incomplete understanding of how unique process parameters relate to defect formation and ultimately mechanical properties. To address that gap, high‐speed X‐ray imaging is used to probe subsurface melt pool dynamics and void‐formation mechanisms inaccessible to other monitoring approaches. This technique directly observes the depth and dynamic behavior of the vapor depression, also known as the keyhole depression, which is formed by recoil pressure from laser‐driven metal vaporization. Also, vapor bubble formation and motion due to melt pool currents is observed, including instances of bubbles splitting before solidification into clusters of smaller voids while the material rapidly cools. Other phenomena include bubbles being formed from and then recaptured by the vapor depression, leaving no voids in the final part. Such events complicate attempts to identify defect formation using surface‐sensitive process‐monitoring tools. Finally, once the void defects form, they cannot be repaired by simple laser scans, without introducing new defects, thus emphasizing the importance of understanding processing parameters to develop robust defect‐mitigation strategies based on experimentally validated models