Relationship between mirror dimensions and failure stress for optical fibers
Standard glass optical fiber was intentionally damaged and strength tested in tension for a wide range of failure stress values. The mirror/mist boundary on each specimen was measured and compared to the measured failure stress. When the size of the fracture mirror is small compared to the fiber diameter, the well-known linear square root dependence of strength on mirror size was reaffirmed. However, when the mirror size approaches the fiber diameter, this relationship does not hold. The classical relationship overestimates the failure stress for large mirrors. It was determined that the circular shape of the fiber contributes to, but can not fully account for, departure from the classical strength/mirror relationship. A new empirical relationship was developed to enable predictive determinations of failure stress to levels as low as 20 to 25 kpsi (0.14 to 0.17 GPa).
The Fracture Behavior of Glass Fiber/Recycled PET Composites
The potential offered by glass fiber reinforced composite materials based on a recycled PET matrix has been investigated. Laminates containing both woven glass fabrics and chopped strand mats have been manufactured and tested under both quasi-static and impact conditions. Polished sections from a number of laminates have highlighted the high degree of fiber wetting and the low level of voiding in the laminates. Three point bend tests and Charpy impact tests on simple beam-like samples have shown that recycled polymer composites offer a range of mechanical properties similar to those associated with corresponding laminates based on virgin polymer matrices. A series of interlaminar fracture tests on the woven fiber composites have shown that the delamination resistance of these materials is comparable to that exhibited by relatively tough systems such as carbon fiber reinforced PEEK.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.