scholarly journals Determination of the Factors Controlling Crystallography Non-Conformance in Single Crystal Turbine Blade Production on an Industrial Scale

2007 ◽  
Vol 558-559 ◽  
pp. 695-700
Author(s):  
J. Cameron ◽  
P.W. Shelton
Keyword(s):  
Single Crystal ◽  
Physical And Mechanical Properties ◽  
Structure Design ◽  
Grain Structure ◽  
Industrial Scale ◽  
Optimum Process ◽  
Production Volume ◽  
Single Crystal Turbine Blade ◽  
High Production Volume ◽  
Input Variables

The arduous conditions to which hot section turbine components are subjected in service, dictate the superior physical and mechanical properties demanded of them. The demand for both high temperature and creep resistance, and anisotropic property requirements of the components has lead to developments in alloy composition, component geometry and single, oriented grain structure design. The slim tolerances and high quality standards imposed on such design features, combined with the high production volume in industry means that component non-conformances to the customer specifications occur. The input variables contributing to crystallography non-conformance in single crystal production have been investigated with a view to defining optimum process parameters for the successful manufacture of single crystal investment cast components on an industrial scale.

scholarly journals Estimating the Impact of High-Production-Volume Chemicals on Remote Ecosystems by Toxic Pressure Calculation

2006 ◽  
Vol 40 (5) ◽  
pp. 1573-1580 ◽  
Author(s):  
Jasper V. Harbers ◽  
Mark A. J. Huijbregts ◽  
Leo Posthuma ◽  
Dik van de Meent
Keyword(s):  
Production Volume ◽  
High Production Volume ◽  
Pressure Calculation ◽  
High Production ◽  
The Impact

scholarly journals Animal Use and Lessons Learned in the U.S. High Production Volume Chemicals Challenge Program

2012 ◽  
Vol 120 (12) ◽  
pp. 1631-1639 ◽  
Author(s):  
Patricia L. Bishop ◽  
Joseph R. Manuppello ◽  
Catherine E. Willett ◽  
Jessica T. Sandler
Keyword(s):  
Lessons Learned ◽  
Production Volume ◽  
High Production Volume ◽  
Animal Use ◽  
High Production ◽  
The U.S

The High Production Volume Challenge Program

2006 ◽  
pp. 1-19
Author(s):  
Richard Hefter ◽  
Barbara Leczynski ◽  
Charles Auer
Keyword(s):  
Production Volume ◽  
High Production Volume ◽  
High Production ◽  
Volume Challenge

Integrating Impedance Control and Learning Based Search Scheme for Robotic Assemblies Under Uncertainty

2018 ◽  
Author(s):  
Rishi K. Malhan ◽  
Yash Shahapurkar ◽  
Ariyan M. Kabir ◽  
Brual Shah ◽  
Satyandra K. Gupta
Keyword(s):  
Search Strategy ◽  
Small Volume ◽  
Impedance Control ◽  
Production Volume ◽  
Control Parameters ◽  
Robotic Arms ◽  
High Production Volume ◽  
High Production ◽  
Assembly Operations ◽  
Assembly Tasks

Using fixtures for assembly operations is a common practice in manufacturing processes with high production volume. For automated assembly cells using robotic arms, trajectories are programmed manually and robots follow the same path repeatedly. It is not economically feasible to build fixed fixtures for small volume productions as they require high accuracy and are part specific. Moreover, hand coding robot trajectories is a time consuming task. The uncertainties in part localization and inaccuracy in robot motions make it challenging to automate the task of assembling two parts with tight tolerances. Researchers in past have developed methods for automating the assembly task using contact-based search schemes and impedance control-based trajectory execution. Both of these approaches may lead to undesired collision with critical features on the parts. Our method guarantees safety for parts with delicate features during the assembly process. Our approach enables us to select optimum impedance control parameters and utilizes a learning-based search strategy to complete assembly tasks under uncertainties in bounded time. Our approach was tested on an assembly of two rectangular workpieces using KUKA IIWA 7 manipulator. The method we propose was able to successfully select the optimal control parameters. The learning-based search strategy successfully estimated the uncertainty in pose of parts and converged in few iterations.

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