The demands of modern manufacturing technology require that machining operations be performed in a manner so as to efficiently enhance economic, time and equipment aspects of the manufacturing process. Recent trends toward "near net shape" manufacturing have significantly reduced the need for multipass rough machining operations while placing more emphasis on single pass finish machining. Therefore, it is important to consider the finish machining requirements in process planning for machining operations.
Generally, the overall machining performance can be qualitatively described by the surface roughness, machining accuracy, cutting force/power, tool wear/tool life and chip breakability. Key factors which are known to effect machining performance include the machine tool, cutting tool, cutting parameters, work material, and cutting fluid. The various machining performance criteria are highly interactive and the relationships describing the interaction are very complex. Therefore, due to these complexities, many of these highly desirable features are very difficult to achieve in combinations as the trade off in achieving one normally results in significant loss in others. For example, in finish turning operations, increasing productivity through higher material removal rates requires that cutting tools be provided which are capable of longer tool life while still being capable of maintaining the required surface finish level and acceptable chip-forms/shapes or chip breakability. In addition, increased cutting speeds impose a power constraint.
U.S. Pat. No. 4,833,617 to Wang discloses a method for modeling an adaptive feed rate control for numerically controlled machining. This method takes into consideration such operating parameters as material removal rate, cutting force and tool deflection. However, there are numerous other operating parameters, as described above, which if taken into consideration would increase the accuracy of the modeling method.
Similarly, U.S. Pat. No. 4,926,309 to Wu et al. discloses a method that utilizes artificial intelligence for adaptive machining control of surface finish in a machining operation. The Wu et al. method takes into consideration a variety of operating parameters, such as, tool wear, depth of cut, work material hardness and material removal rate. Again, however, only a limited number of operating parameters and machining performance criteria are taken into consideration, thus significantly limiting the reliability and overall accuracy provided by this method.
The Wang and Wu et al. patents are representative of the prior art which typically rely upon a limited number of operating parameters and consider limited number of machining performance criteria. In other words, the prior art has essentially ignored important machinability criteria and key factors which form the machining performance. This may be attributed to the limited availability of quantitatively reliable machining performance models relating the surface finish, dimensional accuracy, chip breakability, and other machining performance variables to the cutting or process parameters. More specifically, currently available metal cutting theories are unable to explicitly present all relationships between input variables and machining behavior, especially for complex grooved tools which are primarily used in finish turning operations today. While there have been many attempts to build phenomenological models of metal cutting processes, these theories are not yet able to solve all the problems which currently exist during the machining operation. Moreover, the large number of variables involved in the machining process results in the need for considering an almost infinite number of machining combinations.
Also, all past research on machining optimization is based on the critical assumption that the machining process occurs with fresh or unworn cutting tools. Of course, in actual machining, the performance may vary significantly with the progression of overall tool wear during the process.
Accordingly, while much is known and much work has been done towards improving the machining process and its efficiency for finish turning operations, a need is identified for further improving the machining process through the selection of the most suitable cutting conditions and/or tool insert types for given quality requirements. Such an optimization method would be able to take all machinability parameters simultaneously into consideration. Furthermore, such an optimization method would be able to take into account the varying tool wear state during the machining process.