Marker-free identification of turned, ground and deep rolled workpieces using wavelet transformation

This paper presents a marker-free component identification of cylindrical workpieces produced by the manufacturing processes turning, grinding and deep rolling. The position of unique features from a 2-D profile in the 3-D frequency is detected for identification. Therefore, this work presents an approach using an industrial camera for surface measuring to clearly identify individual cylindrical components. In addition, wear tests are carried out to investigate the method's robustness. The results after the wear tests indicate a false positive rate of 10-2

Effect of mechanical finishing on residual stresses and application behavior of wire arc additive manufactured aluminum components

Wire arc additive manufacturing (WAAM) offers great potential for the production of automobile components due to its high material deposition rates, low costs and design freedom. In order to meet the requirements regarding surface integrity and lifetime, the parts must be mechanically finished after WAAM. However, the subsurface properties (e.g. residual stresses) and consequently the application behavior are significantly influenced by the mechanical finishing process. Thus, the effects of deep rolling and heat treatment on the residual stresses and lifetime of AlSi12 and AlSi10Mg parts produced by WAAM are investigated and the results are presented in this paper. Deep rolling was found to induce compressive residual stresses into additive manufactured AlSi12 and AlSi10Mg workpieces. Furthermore, deep rolling increased the lifetime of the WAAM components significantly. With a combination of a heat treatment and subsequent deep rolling AlSi10Mg workpieces were manufactured securely fail-safe

Laser Scanning Based Object Detection to Realize Digital Blank Shadows for Autonomous Process Planning in Machining

The automated process chain of an unmanned production system is a distinct challenge in the technical state of the art. In particular, accurate and fast raw-part recognition is a current problem in small-batch production. This publication proposes a method for automatic optical raw-part detection to generate a digital blank shadow, which is applied for adapted CAD/CAM (computer-aided design/computer-aided manufacturing) planning. Thereby, a laser-triangulation sensor is integrated into the machine tool. For an automatic raw-part detection and a workpiece origin definition, a dedicated algorithm for creating a digital blank shadow is introduced. The algorithm generates adaptive scan paths, merges laser lines and machine axis data, filters interference signals, and identifies part edges and surfaces according to a point cloud. Furthermore, a dedicated software system is introduced to investigate the created approach. This method is integrated into a CAD/CAM system, with customized software libraries for communication with the CNC (computer numerical control) machine. The results of this study show that the applied method can identify the positions, dimensions, and shapes of different raw parts autonomously, with deviations less than 1 mm, in 2.5 min. Moreover, the measurement and process data can be transferred without errors to different hardware and software systems. It was found that the proposed approach can be applied for rough raw-part detection, and in combination with a touch probe for accurate detection

Gentelligent processes in biologically inspired manufacturing

Production systems have to meet quality requirements despite increasing product individuality, varying batch sizes and a scarcity of resources. The transfer of experience-based knowledge in a flexible and self-optimizing production and process planning offers the potential to meet these challenges. Biological systems solve conceptually similar challenges pertaining to the transfer of knowledge, flexibility of individual reactions and adaptation over time. Thus, in the context of digital transformation, mechanisms derived from biology are interpreted and applied to the knowledge domain of production technology. To be able to exploit the potential of bio-inspired production systems, genetic and intelligent properties of technical components and machines were identified and brought together under the concept of “Gentelligence”. Expanding upon this concept with the new idea of process-DNA and biologically inspired optimization algorithms facilitates a more flexible, learning and self-optimizing production, which is shown in three different applications. By using the new concept of gentelligent process planning it is possible to determine machine-specific process parameters in turning processes in order to ensure appropriate roughness within the requirements. Furthermore, the combination of the concept with a material removal simulation allows the determination of the resulting process force in tool grinding for subsequent unknown workpiece geometries. As a result of using the process-DNA, a workpiece-independent knowledge transfer and thus process adaptation for shape error compensation becomes possible. Gentelligent production scheduling enables a process-parallel, holistically optimized machine allocation, and as a result, a significantly reduced lead time. © 2020 The Author

Automated process planning in milling of hybrid components

Hybrid material composites can meet the increasing demands for high strength and low weight due to their different workpiece properties. Usually, hybrid components require post-machining after their fabrication. Due to the different material properties, new challenges arise in the machining process. It is essential to recognize the course of the material boundary in order to adapt the process planning accordingly and to enable a uniform material transition during machining. This paper presents a method for automated material recognition and automatic adaptation of the process parameters considering a uniform force level during the milling of hybrid materials. This way, the load on the milling tool in the material transition area can be reduced by up to 71%, which prevents premature tool failure. An optical laser line scanner is used to localize of material transitions within hybrid components. This enables a digital mapping of the material distribution in the discretized workpiece model. In combination with an empirical force model, it is possible to predict the cutting forces of the different materials and determine the material transition area for adapting them to specified target values

Modeling the wetting behavior of grinding wheels

Helical flute grinding is an important process step in the manufacturing of cylindrical cemented carbide tools where the use of cooling lubricants is a defining factor determining process performance. Finding optimal parameters and cooling conditions for the efficient use of lubricant is essential in reducing energy consumption and in controlling properties of the boundary zone like residual stresses. Any mathematical model describing the interactions between grinding wheel, lubricant and workpiece during the process has to account for the complex microstructure of the wheel; however, this renders the identification of parameters like slip or heat exchange coefficients numerically prohibitively expensive. In this paper, results from grinding oil droplet experiments are compared with simulation results for the wetting behavior of grinding wheels. More specifically, finite element simulations of the thin-film equation are used to identify slip parameters for different grinding wheel specifications (grain size, bonding structure, wetting status). Our results show that both the bonding and the grain size have an influence on the wetting behavior. The slip parameters that we identified account for the fluid-microstructure interactions and will be used to effectively model those interactions in more complex 3D fluid-dynamic simulations via the Beavers-Joseph condition

Effectiveness of the AS03-Adjuvanted Vaccine against Pandemic Influenza Virus A/(H1N1) 2009 – A Comparison of Two Methods; Germany, 2009/10

During the autumn wave of the pandemic influenza virus A/(H1N1) 2009 (pIV) the German population was offered an AS03-adjuvanted vaccine. The authors compared results of two methods calculating the effectiveness of the vaccine (VE). The test-negative case-control method used data from virologic surveillance including influenza-positive and negative patients. An innovative case-series methodology explored data from all nationally reported laboratory-confirmed influenza cases. The proportion of reported cases occurring in vaccinees during an assumed unprotected phase after vaccination was compared with that occurring in vaccinees during their assumed protected phase. The test-negative case-control method included 1,749 pIV cases and 2,087 influenza test-negative individuals of whom 6 (0.3%) and 36 (1.7%), respectively, were vaccinated. The case series method included data from 73,280 cases. VE in the two methods was 79% (95% confidence interval (CI) = 35–93%; P = 0.007) and 87% (95% CI = 78–92%; P<0.001) for individuals less than 14 years of age and 70% (95% CI = −45%–94%, P = 0.13) and 74% (95% CI = 64–82%; P<0.001) for individuals above the age of 14. Both methods yielded similar VE in both age groups; and VE for the younger age group seemed to be higher

Extended analysis of a genome-wide association study in primary sclerosing cholangitis detects multiple novel risk loci.

A limited number of genetic risk factors have been reported in primary sclerosing cholangitis (PSC). To discover further genetic susceptibility factors for PSC, we followed up on a second tier of single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS). We analyzed 45 SNPs in 1221 PSC cases and 3508 controls. The association results from the replication analysis and the original GWAS (715 PSC cases and 2962 controls) were combined in a meta-analysis comprising 1936 PSC cases and 6470 controls. We performed an analysis of bile microbial community composition in 39 PSC patients by 16S rRNA sequencing. Seventeen SNPs representing 12 distinct genetic loci achieved nominal significance (p(replication) <0.05) in the replication. The most robust novel association was detected at chromosome 1p36 (rs3748816; p(combined)=2.1 × 10(-8)) where the MMEL1 and TNFRSF14 genes represent potential disease genes. Eight additional novel loci showed suggestive evidence of association (p(repl) <0.05). FUT2 at chromosome 19q13 (rs602662; p(comb)=1.9 × 10(-6), rs281377; p(comb)=2.1 × 10(-6) and rs601338; p(comb)=2.7 × 10(-6)) is notable due to its implication in altered susceptibility to infectious agents. We found that FUT2 secretor status and genotype defined by rs601338 significantly influence biliary microbial community composition in PSC patients. We identify multiple new PSC risk loci by extended analysis of a PSC GWAS. FUT2 genotype needs to be taken into account when assessing the influence of microbiota on biliary pathology in PSC.Norwegian PSC Research Center German Ministry of Education and Research (BMBF) through the National Genome Research Network (NGFN) Integrated Research and Treatment Center - Transplantation 01EO0802 PopGen biobank NIH DK 8496