Tham khảo tài liệu ' p6', kỹ thuật - công nghệ, tự động hoá phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Chen Joseph C. Neural Networks and Neural-Fuzzy Approaches in an In-Process Surface Roughness Recognition System for End Milling Operations Computational Intelligence in Manufacturing Handbook Edited by Jun Wang et al Boca Raton CRC Press LLC 2001 16 Neural Networks and Neural-Fuzzy Approaches in an In-Process Surface Roughness Recognition System for End Milling Operations Joseph C. Chen Iowa State University Introduction Methodologies Experimental Setup and Design The In-Process Surface Roughness Recognition Systems Testing Results and Conclusions Introduction Different machining processes produce different products with varying qualities. When evaluating the quality of a finished piece surface roughness is the most important result of the machining process to consider because many product attributes can be determined by how well the surface finish is produced. The quality of the surface finish or surface roughness affects several functional attributes of parts such as surface friction wear reflectivity heat transmission porosity coating adherence and fatigue resistance. The desired surface roughness value is usually specified for individual parts and a particular process is selected in order to achieve the specified roughness. Typically surface roughness measurement has been carried out by manually inspecting machined surfaces at fixed intervals. A surface profilometer containing a contact stylus is used in the manual inspection procedure. This procedure is both time-consuming and labor-intensive. In addition a number of defective parts could be produced during the time needed to complete an off-line surface inspection thereby creating additional production costs. Another disadvantage of using surface profilometers is that they register the serious interference of extraneous vibration generated in the surrounding environment. This extraneous vibration might significantly influence the accuracy of surface measurements. For these .
