What is it about?
This work focuses on statistical interpretation of experimental results. Focus is on the usage of Principal Component Analysis (PCA) to interpret the experiments.
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Why is it important?
In fretting wear experiments, it is a new way of interpreting them using statistical tools like Principal Component Analysis (PCA)
Perspectives
After interpretation of experimental results by using Principal Component Analysis (PCA), it becomes possible to reduce the parameters under observations, focus more on the parameters of interests and design new, cost-effective and faster experiments.
Waqar Ahmed Qureshi
Politecnico di Torino
Read the Original
This page is a summary of: Principal Component Analysis for Characterization of Fretting wear Experiments on Spline Couplings, Procedia Engineering, January 2015, Elsevier,
DOI: 10.1016/j.proeng.2015.06.209.
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