Machining process model for intelligent rough machining of sculptured parts
| dc.contributor.author | Pop, Sorin Ion | en_US |
| dc.date.accessioned | 2024-08-15T17:16:32Z | |
| dc.date.available | 2024-08-15T17:16:32Z | |
| dc.date.copyright | 1996 | en_US |
| dc.date.issued | 1996 | |
| dc.degree.department | Department of Mechanical Engineering | |
| dc.degree.level | Master of Applied Science M.A.Sc. | en |
| dc.description.abstract | In recent years, the advent of new thermosetting materials (i.e. powders) and the widespread use of plastic materials has pushed forward the traditional sector of milling. Due to the vast amount of time required for producing moulds and dies, interest was manifested in reducing the machining time, of which rough machining represents an important component. This is usually proportional to the quantity of material that has to be removed. In the case of moulds and dies that quantity can be significant. One way to reduce the rough machining time is by operating the milling machine as close as possible to its maximum capacity. This thesis presents a strategy that allows the determination of such cutting data. Based on the specific requirements of the Intelligent Rough Machining Approach, together with the formulations laid out by the Mechanistic Force Modeling Method, a cutting force model is introduced. A combination of dependency testing and surface generation procedures is employed to generate the model, which is used to plan the feed rates required to maintain a constant load on the milling machine. A clustering of the feed rates intervals is applied for reducing the total machining time, which results in time savings of up to 16 percent. The features incorporated in the cutting force model are: easy to customize, requires few cutting tests to develop, produces fast and accurate predictions and provisions are made for future upgrading. Testing has shown good agreement between predictions and actual cutting data. Published values for differences between predictions and actual cutting forces range from -15.8 to 28.9 percent. Verification tests have produced values in the range -13 to 3.5 percent, which proves the quality of the concept. Future work could include an investigation in the effect of tool wear on the magnitude of Kt and Kr and possibly an improvement in the design of the rotative dynamometer. The use of optimization techniques for clustering feed rates could provide additional reduction of the machining time. | |
| dc.format.extent | 110 pages | |
| dc.identifier.uri | https://hdl.handle.net/1828/19330 | |
| dc.rights | Available to the World Wide Web | en_US |
| dc.title | Machining process model for intelligent rough machining of sculptured parts | en_US |
| dc.type | Thesis | en_US |
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