In situ process quality monitoring and defect detection for direct metal laser melting

Quality control and quality assurance are challenges in Direct Metal Laser Melting (DMLM). Intermittent machine diagnostics and downstream part inspections catch problems after undue cost has been incurred processing defective parts. In this paper we demonstrate two methodologies for in-process fault detection and part quality prediction that can be readily deployed on existing commercial DMLM systems with minimal hardware modification. Novel features were derived from the time series of common photodiode sensors along with standard machine control signals. A Bayesian approach attributes measurements to one of multiple process states and a least squares regression model predicts severity of certain material defects.Comment: 16 pages, 4 figure

CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes

ABSTRACT: A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) aims to collect available data from industry and academia which may be used for this purpose (www.csardock.org). Also, CSAR is charged with organizing community-wide exercises based on the collected data. The first of these exercises was aimed to gauge the overall state of docking and scoring, using a large and diverse data set of protein ligand complexes. Participants were asked to calculate the affinity of the complexes as provided and then recalculate with changes which may improve their specific method. This first data set was selected from existing PDB entries which had binding data (Kd or Ki) in Binding MOAD, augmented with entries from PDBbind. The final data set contains 343 diverse protein ligand complexes and spans 14 pKd. Sixteen proteins have three or more complexes in the data set, from which a user could start an inspection of congeneric series. Inherent experimental error limits the possible correlation between scores and measured affinity; R 2 is limited to ∼0.9 when fitting to the data set without over parametrizing. R 2 is limited to ∼0.8 when scoring the data set with a method trained on outside data. The details of how the data set was initially selected, and the process by which it matured t

A Collective Variable for the Rapid Exploration of Protein Druggability

An efficient molecular simulation methodology has been developed for the evaluation of the druggability (ligandability) of a protein. Previously proposed techniques were designed to assess the druggability of crystallographic structures and cannot be tightly coupled to molecular dynamics (MD) simulations. By contrast, the present approach, JEDI (<u>J</u>ust <u>E</u>xploring <u>D</u>ruggability at protein <u>I</u>nterfaces), features a druggability potential made of a combination of empirical descriptors that can be collected “on-the-fly” during MD simulations. Extensive validation studies indicate that JEDI analyses discriminate druggable and nondruggable protein binding site conformations with accuracy similar to alternative methodologies, and at a fraction of the computational cost. Since the JEDI function is continuous and differentiable, the druggability potential can be used as collective variable to rapidly detect cryptic druggable binding sites in proteins with a variety of MD free energy methods. Protocols for applications to flexible docking problems are outlined