A Novel Approach to the Holistic 3D Characterization of Weld Seams—Paving the Way for Deep Learning-Based Process Monitoring

In an industrial environment, the quality assurance of weld seams requires extensive efforts. The most commonly used methods for that are expensive and time-consuming destructive tests, since quality assurance procedures are difficult to integrate into production processes. Beyond that, available test methods allow only the assessment of a very limited set of characteristics. They are either suitable for determining selected geometric features or for locating and evaluating internal seam defects. The presented work describes an evaluation methodology based on microfocus X-ray computed tomography scans (µCT scans) which enable the 3D characterization of weld seams, including internal defects such as cracks and pores. A 3D representation of the weld contour, i.e., the complete geometry of the joint area in the component with all quality-relevant geometric criteria, is an unprecedented novelty. Both the dimensions of the weld seam and internal defects can be revealed, quantified with a resolution down to a few micrometers and precisely assigned to the welded component. On the basis of the methodology developed within the framework of this study, the results of the scans performed on the alloy AA 2219 can be transferred to other aluminum alloys. In this way, the data evaluation framework can be used to obtain extensive reference data for the calibration and validation of inline process monitoring systems employing Deep Learning-based data processing in the scope of subsequent work

Challenges and Opportunities for Laser Applications in Electric Vehicle Manufacturing

The ever-growing demand for electric vehicles in the world and Europe will result in a significant socio-economical change. The electrification changes the material types, usage, manufacturing, and vehicle design. The contemporary electric drives are being used in various vehicles, such as automobiles, drones, trains, airplanes, and ships. These vehicles will require a lower weight, an extended maximum range, and faster recharging as the number of vehicles in use increases. Compared to vehicles powered with combustion engines, fewer components will be placed with increased demand in flexible welding, heat treatment, cutting, trimming, and texturing applications. The need for rapid changes in the vehicle models and the variety of components will be resolved through highly digitalized, flexible, adaptable, and reliable manufacturing processes. From this point of view, laser-based manufacturing is an essential solution, placing this family of processes as the conventional method in electric vehicle manufacturing. Today, lasers are used in various applications, such as hairpin stripping and welding, cutting and texturing of Lithium-ion electrodes, welding of battery busbars, and cutting of composite materials. The rapid reduction of the costs of laser sources, optics, and components in the last decade facilitated the adoption of laser systems in electric vehicle manufacturing. Although laser technology has reached the required maturity, the system developers and the end-users still need to catch up with the pace of the growing demand in electric vehicle manufacturing. This white paper highlights the challenges and opportunities regarding the main laser-based manufacturing processes for electric vehicle production

Dual guidance structure for evaluation of patients with unclear diagnosis in centers for rare diseases (ZSE-DUO): study protocol for a controlled multi-center cohort study

Background: In individuals suffering from a rare disease the diagnostic process and the confirmation of a final diagnosis often extends over many years. Factors contributing to delayed diagnosis include health care professionals' limited knowledge of rare diseases and frequent (co-)occurrence of mental disorders that may complicate and delay the diagnostic process. The ZSE-DUO study aims to assess the benefits of a combination of a physician focusing on somatic aspects with a mental health expert working side by side as a tandem in the diagnostic process. Study design: This multi-center, prospective controlled study has a two-phase cohort design. Methods: Two cohorts of 682 patients each are sequentially recruited from 11 university-based German Centers for Rare Diseases (CRD): the standard care cohort (control, somatic expertise only) and the innovative care cohort (experimental, combined somatic and mental health expertise). Individuals aged 12 years and older presenting with symptoms and signs which are not explained by current diagnoses will be included. Data will be collected prior to the first visit to the CRD's outpatient clinic (T0), at the first visit (T1) and 12 months thereafter (T2). Outcomes: Primary outcome is the percentage of patients with one or more confirmed diagnoses covering the symptomatic spectrum presented. Sample size is calculated to detect a 10 percent increase from 30% in standard care to 40% in the innovative dual expert cohort. Secondary outcomes are (a) time to diagnosis/diagnoses explaining the symptomatology; (b) proportion of patients successfully referred from CRD to standard care; (c) costs of diagnosis including incremental cost effectiveness ratios; (d) predictive value of screening instruments administered at T0 to identify patients with mental disorders; (e) patients' quality of life and evaluation of care; and f) physicians' satisfaction with the innovative care approach. Conclusions: This is the first multi-center study to investigate the effects of a mental health specialist working in tandem with a somatic expert physician in CRDs. If this innovative approach proves successful, it will be made available on a larger scale nationally and promoted internationally. In the best case, ZSE-DUO can significantly shorten the time to diagnosis for a suspected rare disease