Graduate Thesis Or Dissertation

 

Characterization of Two Novel Machining Processes for Difficult to Machine Materials Public Deposited

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/9593tz03r

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  • New materials with superior mechanical properties, such as increased strength and hardness, have been developed to allow for new and improved products. However, the mechanical properties that make these materials useful also makes them difficult to process using conventional methods. New machining processes need to be developed to generate products from these materials. Thus, such processes need to be characterized better understand the process effects on the workpiece. Characterization of manufacturing processes can be done empirically or mechanistically. In this research, two machining process were investigated to compare the advantages and drawbacks of each characterization method. The first involved electrically-assisted grinding (EAG) of tool steel. In EAG, an electrical spark is generated between a metal-bonded grinding wheel and a workpiece to aid in material removal. An EAG prototype was successfully developed to empirically investigate the effects of current and electricity on hardness, surface roughness, cutting force, and tool wear. Applied current and voltage were found to cause deleterious tool wear, but were found to have no measurable effect on other process attributes. The second machining process investigated was laser ablation of a technical ceramic. A mechanistic model based on conservation of energy was developed to characterize the process. The model was used to predict depth of cut when fabricating microchannels in silicon carbide. Experiments were conducted to further refine the model, by examining the variance between the model and experimental results. Variance was found to be based on scan speed, with larger variance at low scan speeds and little variance at higher scan speeds. A variance correction term was empirically derived and used to refine the model. Over scan speeds from 100 to 1500 mm/sec and a 100-1000 W power range, the refined model predicted average variation of 9% from experimental data.
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  • description.provenance : Made available in DSpace on 2016-10-04T16:28:21Z (GMT). No. of bitstreams: 2 license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5) DoranMichaelP2016.pdf: 1250606 bytes, checksum: 970eb685b4003eb28722525f5d1d7949 (MD5) Previous issue date: 2016-09-14
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