AERONAUTICAL AND MECHANICAL ENGINEERING Abrasive Flow Machining : Major Research Areas & Analysis
Abrasive flow machining also known as abrasive flow deburring or extrude honing, is an interior surfac e finishing process characterized by flowing an abras ive-laden fluid through a work piece. This fluid is typically very viscous, having the consistency of p utty, or dough. In this paper, the main purpose of the process is to investigate the surface finishing of die steel with the use of abrasive flow machining. Grinding medium is pressed along the contours at a defined p r ssure and temperature. Depending on the respectiv e machining task, different specifications of media a re used. The surface finishing is better with use o f abrasive flow machining process as compared to other flow ma chining processes.
Abrasive Flow Machining
The chapter presents some elements related to Abrasive Flow Machining (AFM), specifically state of the art, elements of technological system, and construction of equipment as well as experimental results. The technological system comprises machine, tooling, working media, carrier, abrasive grains. This equipment could be the base for further researches in AFM field, comprising two opposed cylinders, which assures extrusion with semisolid abrasive media back and forth through the work piece or through passages formed by the work piece and a fixture. By repeatedly extruding the media from one cylinder to the other, an abrasive action is produced as the media enter a restrictive passage and travel through or across the work piece. In this way, the work piece can be nanofinished under Ra = 0.1 μm.
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.