Monitoring and planning for open architecture manufacturing of precision machining using acoustic emission

The core contribution of this dissertation is in the investigation of an AE sensor as an effective monitoring tool in precision machining, specifically, in diamond turning. A monitoring strategy is developed and monitoring experiments are conducted using the energy component of acoustic emission sensors. Using the experimental data, microprocess planning algorithm is developed for sensing the unique features, critical to precision machining process.; Strategy for the sensor based machining is investigated under the umbrella of an open manufacturing concept. Open architecture manufacturing principle provides the flexibility to integrate advanced sensors and monitoring methods to processes that require unconventional monitoring tools and methods. Some of the key issues in the application of the open architecture concept to precision manufacturing are presented and an open architecture strategy for precision manufacturing is developed.; Two different types of materials with three different material conditions, coarse grain Oxygen Free High Conductivity (OFHC) copper, cold worked OFHC copper, and silicon, are used in the monitoring experiments for detecting the precision machining features. To monitor the effects of ductile micro-sources in diamond turning, using coarse grain OFHC copper and a broadband AE sensor, surface topography changes due to different crystallographic orientation and the grain boundary effect are monitored. For AE monitoring the influence of macro-source in precision machining, cold worked OFHC copper is used to monitor the preferred orientation change during diamond turning of cold worked OFHC copper. For the detection of brittle micro-sources in diamond machining, advanced semiconductor material, silicon, and a mid-range (650KHz peak detection frequency) AE sensor is used. With an optimized monitoring strategy, the ductile-regime and the ductile/brittle transition during silicon diamond machining is successfully monitored using only the energy component of the AE signal.; Finally, a micro-process planning technique is developed. The algorithm is simulated using the experimental data obtained during the ductile/brittle transition monitoring experiment. Enhanced statistical method using Bayesian statistics is proposed in adjusting the depth of cut for minimizing the brittle subsurface damage created in machining of brittle materials.