Sheet-metal press line parameter tuning using a combined DIRECT and Nelder-Mead algorithm

It is a great challenge to obtain an efficient algorithm for global optimisation of nonlinear, nonconvex and high dimensional objective functions. This paper shows how the combination of DIRECT and Nelder-Mead algorithms can improve the efficiency in the parameter tuning of a sheet-metal press line. A combined optimisation algorithm is proposed that determines and utilises all local optimal points from DIRECT algorithm as Nelder-Mead starting points. To reduce the total optimisation time, all Nelder-Mead optimisations can be executed in parallel. Additionally, a Collision Inspection Method is implemented in the simulation model to reduce the evaluation time. Altogether, this results in an industrially useful parameter tuning method. Improvements of an increased production rate of 7% and 40% smoother robot motions have been achieved

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

A faster collision detection method applied on a sheet metal press line

Geometrical collision detection is a time and resource consuming simulation task. In order to decrease time and resources, a general method applicable for 2D motions has been developed. The method is useful in simulation cases where 3D CAD data is part of an iterative method, e.g. optimization. The method is based on a transformation of a general 3D problem to a 2D problem, eliminating the need of 3D CAD models. Press Line simulations during the last decade have been accepted as a quality improvement method. Today simulations of automated press lines are done for internal collision checks in dies and external collision checks against dies and material handling equipment. If these collisions are not detected in simulations, they result in delays, in introduction of a new product in the line, so called line tryout or later when the line is ramped up to decide rate. The results of these collisions are used for pre-die design, design of grippers, maintenance and production planning. In this paper a new method, based on 2D simplifications, is developed and tested successfully in a virtual model of a press line at Volvo Car Manufacturing. Die Uppers 2 917 708 triangles and Die Lowers 602 686 triangles where reduced to 58 and 90 points. The result of the method shows substantial reduction of geometry data and considerable improvement in collision detection evaluation time over general 3D algorithms in the tested case