A fuzzy basis material removal optimization strategy for sculptured surface machining using ball-nosed cutters

Abstract

A fuzzy basis material removal optimization strategy for sculptured surface machining using ball-nosed cutters W. L. R. IP²Optimizing the e ciency in cutting a sculptured surface using numerically controlled machining techniques needs to carefully consider the relationship between cutting edges and surface geometry. The actual cutting speed at the cutting edges of a ball-nosed cutter is a dependent parameter to the surface gradient of the machined surface. A fuzzy basis material removal optimization approach was suggested to compensate the variation of cutting speed due to the change of gradient on the sculptured surface in machining process. In this approach, a constant cutting force is maintained by adjusting the cutting feedrate for each cutting point in considering other machining parameters, such as tool life and surface gradient. The results from a machining trial indicated that the total amount of removed material collected at a ® xed time interval with feedrate adjustment from the machined surface was about 41% more than the surface machined at a constant feedrate. In considering computational time, using the fuzzy approach can also be a faster way than employing heuristic methods. In addition, using the fuzzy approach is simple due to its computational complexityindependent properties in operating a fuzzy inference system.

A review of optimization techniques in metal cutting processes

“…Number of operation stage(s) considered Special consideration in the approach Statistical regression (i) Lathe turning (Hassan & Suliman, 1990) (i) One (i) One (i) None (ii) Finished turning (Feng(Jack) and (ii) One (ii) One (ii) Fractional factorial design Artificial neural network (i) Creep feed grinding (Sathyanarayanan et al,1992) (i) Three (i) One (i) Generalized reduced gradient method (ii) Abrasive flow machining (Petri et al, 1998) (ii) Two (i) One (ii) None (iii) Honing (iii) Five (iii) One (iii) Paired t-test & Ftest Fuzzy set theory (i) End milling (Ip, 1998) (i) Two (i) One (i) None (ii) Down milling (Al-Wedyan et al, 2001) (ii) One (ii) One (ii) Surface plot Taguchi method (i) Lathe turning (Youssef et al, 1994) (i) One (i) One (i) Full factorial design (ii) Face milling (Lin, 2002) (ii) Three (ii) One (ii) None (iii) Surface grinding (Shaji and Radhakrisnan, 2003) (iii) Two (iii) One (iii) None…”

“…Hashmi, El Baradie, and Ryan (1998) apply fuzzy set theory logic for selection of cutting conditions in machining. Ip (1998) adopts a fuzzy rule based feedrate control strategy in mild steel bar surface milling operation for improvement in cutting efficiency and prolonging the tool life. Lee, Yang, and Moon (1999) use fuzzy set theory-based non-linear model for a turning process as a more effective tool than conventional mathematical modelling techniques if there exists 'fuzziness' in the process control variables.…”

A fuzzy system approach of feed rate determination for CNC milling

“…Specifically, Gaussian function is used to define the three membership functions for the radial depth of cut, and they are plotted in Figure 2. Since the three membership functions reach their maximums at different values of the radial depth of cut, the membership functions are identified as low, medium, and high according to these values ( [7]). Similarly, Gaussian function is adopted as the membership functions for both the radial depth of cut and the feed rate.…”

Comparing and combining off-line feedrate rescheduling strategies in free-form surface machining with feedrate acceleration and deceleration

“…Yazar et al [5] used maximum resultant cutting force to schedule feedrates with a proposed cutting force model. Ip [6] proposed a MRR optimization approach to compensate the variation of cutting speed and maintain a constant cutting force by adjusting the cutting feedrate considering tool life and surface gradient. Fussel et al [7] used tool deflection, surface finish, tool failure, and machine power to set constraints on the cutting force and feedper-tooth based on one discrete mechanistic end milling model.…”