Hardware based analysis and process control for laser brazing applications

Laser brazing is widely used for joining metal sheets in industrial applications, in particular in the automotive sector, where the requirements on surface quality are extremely high. Therefore, quality control and process observation cannot be omitted. This paper presents the current works of a camera based process control system. Hardware-based algorithms for estimation of machine parameters during the process are implemented on FPGA technology. In particular the process velocity is measured in real time which makes the system suitable for controlling tasks to react instantaneously on changes of the velocity.First experimental results on a controlled laser brazing process are presented. Additionally an evaluation of the accuracy of the hardware-based velocity measurement is given

Sports Orthopedics

Background: Different methods for heart rate (HR)-determination are used in routine performance diagnostics. Aim of the study was to compare different HR measurement methods during treadmill performance diagnostics.Methods: 76 athletes (28.614.7 years, 38% female) performed a treadmill lactate threshold test. HR during testing was simultaneously assessed by analysis of a 12-lead electrocardiogram (ECG) both automatically (aECG) and manually (mECG) and a heart rate monitor (HRM). ECGs and HRM measurements were analyzed by two diagnosticians and finally, three different HR curves (aECG, mECG, HRM) were generated and compared at different time points.Results: ECG-based HR detection revealed excellent reproducibility and reliability. Concerning HRM/aECG, faulty measurements were detected in 14.5%/36.8% of all athletes. However, constructions of HR/lactate curves were still possible in 84.6%/73.7% of all athletes. HR at different corresponding time points did not differ significantly between mECG and HRM/aECG (intraclass correlation coefficient >0.9/0.8 and coefficient of variation <5%/5%). In Bland-Altman analysis HRM/mECG and aECG/mECG, mean differences were usually low (3-5 bpm). Limits of agreement were relatively high (approx.10 bpm).Conclusions: Training areas defined by mECG may be used for home training control with HRM. If HRM measurements are used for the athletes training recommendations, HRs determined should be checked for plausibility and comparability with corresponding ECG measurements by physicians with appropriate expertise. Due to comparably high error susceptibility, aECG HR detection should not be used in performance diagnostics. KEY WORDS: Heart Rate Detection, Heart Rate Monitors, Lactate Curve, Performance Diagnosi