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110 results found

Optimal Bayesian Experimental Design Version 1.2.0

Data provided by  National Institute of Standards and Technology

Python module 'optbayesexpt' uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given an parametric model - analogous to a fitting function - Bayesian inference uses each measurement 'data point' to refine model parameters. Using this information, the software suggests measurement settings that are likely to efficiently reduce uncertainties. A TCP socket interface allows the software to be used from experimental control software written in other programming languages.

Tags: GitHub pages template,experimental design,Bayesian,optbayesexpt,python,adaptive measurement,

Modified: 2024-02-22

Views: 0

Thermal Drift Monitoring Experiment 01

Data provided by  National Institute of Standards and Technology

An experiment was set up within a machine tool at the National Institute of Standards and Technology (NIST) to test vision-based thermal drift tracking methods. A wireless microscope within a tool holder in the spindle is used to capture videos of image targets attached to the worktable. For each target, one video is captured during spindle rotation orthogonal to the worktable and another video is captured during axis translation orthogonal to the worktable.

Tags: Thermal error,machine tool,monitoring,manufacturing,Microscope,

Modified: 2024-02-22

Views: 0

Simantha: Simulation for Manufacturing

Data provided by  National Institute of Standards and Technology

Simantha is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines with finite buffers. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Classes for five basic manufacturing objects are included: source, machine, buffer, sink, and maintainer. These objects can be defined by the user and configured in different ways to model various real-world manufacturing systems.

Tags: discrete-event simulation,manufacturing,production,maintenance,python,

Modified: 2024-02-22

Views: 0

Linear Axis Testbed at IMS Center - Run-to-Failure Experiment 01

Data provided by  National Institute of Standards and Technology

A linear axis testbed at the Center for Intelligent Maintenance Systems (IMS Center) at the University of Cincinnati was run to failure (the detection of backlash) over one year with periodic data collected from an inertial measurement unit (IMU) on the carriage, two triaxial accelerometers on the ball nut, and the controller.

Tags: manufacturing,Industry 4.0,smart manufacturing,linear axis,machine tool,ball screw,backlash,sensor,accelerometer,inertial measurement unit,IMU,error motion,data analysis,monitoring,diagnostics,

Modified: 2024-02-22

Views: 0

Python tools for measuring filament defects in embedded 3D printing

Data provided by  National Institute of Standards and Technology

In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. This repository includes Python code for analyzing and managing images and videos of the printing process during extrusion of single filaments. The zip file contains the state of the code when the associated paper was submitted. The link to the GitHub page goes to version 1.0.0, which is the same as the code attached here. From there, you can also access the current state of the code.Associated with: L. Friedrich, R. Gunther, J.

Tags: python,digital image analysis,computer vision,3D printing,additive manufacturing,openCV,

Modified: 2024-02-22

Views: 0

Entropy Source Validation Client

Data provided by  National Institute of Standards and Technology

This tool is a Python client that can interact with the NIST Entropy Source Validation Test System.

Tags: python,Entropy Source Validation Test System,NIST,CMVP,ESV Client,

Modified: 2024-02-22

Views: 0

Cost Assessment Tool for Sustainable Manufacturing (CATS)

Data provided by  National Institute of Standards and Technology

This tool uses techniques from ASTM E3200 for evaluating manufacturing investments from the perspective of environmentally sustainable manufacturing by pairing economic methods of investment analysis with environmental aspect of manufacturing. The economic techniques used include net present value, internal rate of return, payback period, and hurdle rate. These four techniques are deterministic, meaning that they deal with known values that are certain. The tool also conducts a sensitivity analysis using Monte Carlo techniques.

Tags: investment analysis,manufacturing,net present value,internal rate of return,environmental impact,sustainability,

Modified: 2024-02-22

Views: 0

RGTM 10169

Data provided by  National Institute of Standards and Technology

NIST developed a reference material consisting of synthetic fragments of the SARS-CoV-2 virus RNA, which is the target of molecular diagnostic tests. These RNA fragments can assist in the development and validation of RT-qPCR assays for the detection SARS-CoV-2. The RNA fragments are characterized for concentration using digital PCR methods, may be used to assess limits of detection for SARS-CoV-2 assays, and may calibrate other in-house or commercial SARS-CoV-2 controls. This dataset includes data used for RTGM 10169 SARs-Cov-2 Research Grade Test Material validation.

Tags: virus,SRM,reference material,DNA,sequencing,PCR,digital PCR,Biosciences and Health,

Modified: 2024-02-22

Views: 0

Sim-PROCESD: Simulated-Production Resource for Operations and Conditions Evaluation to Support Decision-making

Data provided by  National Institute of Standards and Technology

Sim-PROCESD is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Sim-PROCESD provides class definitions for manufacturing devices/components that can be configured by the user to model various real-world manufacturing systems.

Tags: discrete-event simulation,manufacturing,production,maintenance,python,

Modified: 2024-02-22

Views: 0

FCpy: Feldman-Cousins Confidence Interval Calculator

Data provided by  National Institute of Standards and Technology

Python scripts and Python+Qt graphical user interface for calculating Feldman-Cousins confidence intervals for low-count Poisson processes in the presence of a known background and for Gaussian processes with a physical lower limit of 0.

Tags: python,SIMS,statistics,mass spectrometry,Confidence Interval,CI,Feldman,Cousins,Poisson,Gaussian,

Modified: 2024-02-22

Views: 0