A sphere precessions polishing (SPP) method is proposed to perform the precessions polishing function like the bonnet polishing technique. A compliant inflated hollow ball is adopted as the sphere polishing tool, and three motors are used to collaboratively drive the sphere tool rotating around a desired axis tilting with a precessions angle. A prototype is developed to validate the technical feasibility and demonstrate the polishing removal functions. The polishing spots of vertical polishing show a typical W shape-like profile, and tilted polishing results reveal a D shape-like profile. This proposed SPP method can be regarded as a potential candidate technique to realize the precision precessions polishing in the optical precision engineering.
This paper is dedicated to feature extraction of acoustic emission (AE) signals from machining hard brittle materials. A diamond intender is used to scratch BK7 and sapphire. Typical characteristics of AE signals from the two materials of are all concentrated in [100-200]KHz frequency span, and band-filtered signals represent obvious local burst-type waveforms, which is closely related to the crack and fragmentation phenomena that occur when brittle material is removed. Locations and oscillation form of burst-type AE are useful information for process monitoring and quality prediction of machined surface. In order to acquire the information, the theory of shift-invariant sparse coding(SISC) is introduced to analysis AE RMS signals. Experimental results of the two typical hard brittle materials show that oscillation form and locations of burst-type AE can be sparsely expressed by a self-learned atom and the corresponding coefficients. From the aspects of machining process monitoring, taking burst-type AE event as a monitor parameter is more accurate and objective in the reflection of locations and scales of cracks on the machined surface induced by machining.
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