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The development of the best properties in polyester composite from pineapple leaf fiber (PALF) as a reinforcing material is a subject of interest. The properties of PALF are reliant upon fiber length, wherein technical difficulties in production of long fibers and processing for better characteristics in polyester composites possess inherent challenges. The PALFs are subjected to silane treatment for altering fiber properties. This research attempts to analyze the impact of silane-treated PALF with varying fiber lengths (5, 10, 15, 20, and 25 mm) on the performance of natural fiber composites (NFC) properties. Open mold and hand lay-up techniques were employed to develop the polyester composites. The prepared PALF-based polyester composites were examined for different properties (impact, flexural, tensile strength, and wear rate). Coefficient of friction and wear studies are performed on the prepared composites subjected to different loads (10, 20, and 30 N) via a pin on disc test rig. Polymer composite fracture surfaces were analyzed to observe the interfacial bonding between fibers and matrix via scanning electron microscopy (SEM). SEM results showed that the application of silane treatment resulted in better surface topography (fiber length of 5–10 mm showed smooth surface resulted in crack proliferation possessing low fracture toughness of 15–32 MPa; whereas a 15–20 mm fiber length resulted in better fiber–matrix bonding, improving the fracture toughness from 42–55 MPa) as a result of change in chemical structure in PALF. The 20 mm length of PALF resulted in better properties (flexural, tensile, impact, and wear resistance) which are attributed to fiber–matrix interfacial bonding. These properties ensure the developed polymer composites can be applied to walls, building insulation, and artificial ceilings.

In this study, the burr and slot widths formed after the micro-milling process of Inconel 718 alloy were investigated using a rapid and accurate image processing method. The measurements were obtained using a user-defined subroutine for image processing. To determine the accuracy of the developed imaging process technique, the automated measurement results were compared against results measured using a manual measurement method. For the cutting experiments, Inconel 718 alloy was machined using several cutting tools with different geometry, such as the helix angle, axial rake angle, and number of cutting edges. The images of the burr and slots were captured using a scanning electron microscope (SEM). The captured images were processed with computer vision software, which was written in C++ programming language and open-sourced computer library (Open CV). According to the results, it was determined that there is a good correlation between automated and manual measurements of slot and burr widths. The accuracy of the proposed method is above 91%, 98%, and 99% for up milling, down milling, and slot measurements, respectively. The conducted study offers a user-friendly, fast, and accurate solution using computer vision (CV) technology by requiring only one SEM image as input to characterize slot and burr formation.