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The feature level operation planning decides what needs to be machined by what cutting tool and on what machine tool. Usually, machine tools are capable of mounting various types and sizes of cutting tools. Therefore, their selection, though important, may be regarded as secondary to that of cutting tools. While selecting a cutting tool, various factors such as roughing or finishing, tool approach direction and machining

depth, minimum machining radius and pinch off areas, cutting parameters and machining power requirements, are considered to be important. These factors should be systematically analysed to implement the optimum tool selection strategy.

Certain type of tool selection could be straight-forward, e.g. in hole finishing operations the tool parameters could be determined easily from machining feature attributes. The suitability of a cutting tool for a given machine tool should be evaluated and may involve complex procedures. For finishing operations one may check the machine tool process capability parameters such as positioning and repeatability tolerance and required spindle speed. For hole roughing operations such as drilling, detailed power calculations may be necessary. For example, the volume of material removed in drilling operation can be computed as:

Vol = Vi(II x (toolDia)2 ) x N x F mm3/min.

Where, Vol = Volume of material removed in mmVmin. toolDia = Drill diameter.

N = Machine tool spindle RPM. F = Cutting Feed in mm per revolution. Therefore, HP = Vol x K

Where, HP = Horse Power required for drilling operation. K = Specific power consumption for a given material.

The tool selection method should check the power availability on machine tools and append the mcForOpr attribute with the list of valid machine tools. If the machine tool constraint is forced, due to optimisation strategy at component level, and the power requirement exceeds the available machine tool horse power, the operation may be split into multiple drilling operations.

For machining cost calculation, the machining and set-up time calculation methods should tie implemented. Cost calculation capability can be demonstrated easily in the CAPP system for hole machining operations For non hole machining operations,

especially for interacting feature clusters, the machining tool path length, and cutting time calculation methods should be developed. Provision has been made to assign machine hour rate attributes to the machine tool (refer to section 5.4, page 99). The Fixture class should be modified to assign setupTime attribute. A more detailed costing may need labour costing data, etc. which has not been implemented.

The problems with detailed tool selection criteria is that the information such as cutting parameters, specific power consumption for various materials, etc. should be available within the resource knowledge-base of a CAPP system. It may be that the most commonly used range of cutting tools and machining parameters could be pre­ defined. The system should derive the parameteis for a new tool by searching the knowledge-base and interpolating the higher and lower diameter tool parameters. This strategy could be easily implemented in the CAPP system. The cutting parameter interface has already been developed. The workpiece material class will have to be modified to add the instance variable, spPower to input specific power consumption attribute.

In the case of pocket and profile milling, the basic tool parameter selection may not be as simple as hole machining. However, certain heuristics should be used (as discussed in section 8.2.3) to decide the cutting tool parameters. In addition it may also need a strategy to minimise the number of tools to machine the entire feature cluster for a given machining datum. To summarise, the feature level optimum tool selection strategy should implement the following steps:

1. Read part level optimisation goals, e.g. cutting time, set-up time, cost, etc. 2. Read part level constraints, e.g. machine tool, fixture machining datum, etc. 3. Select tool parameters based on feature geometry attributes.

4. Check tool parameter validity against part level constraints. 5. If necessary, modify tool parameters to satisfy constraints.

6. Experienced based heuristics rules may be used whenever appropriate.

As mentioned above in step 2, the fixture machining datum attribute should be useful in deciding the degree of freedom in tool approach direction. The manual fixture planning has been assumed in this research. The fixture class should specify the allowable machining datum list for a given component which would help in feature level plan optimisation. For example, if a fixture can be used with front or rear machining datums, and horizontal machining centre with indexible table is a machine tool constraint, then features such as through holes, cutouts, and feature clusters with opposite end openings may provide optimum machining plans. If the feature depth forbids the tool approach in one datum because of tool length/diameter ratio, and tool chatter, then the partial depths of the feature should be approached in complementary datums by indexing the machine tool table through 180 degrees.

Machining Features such as notches, and complementary types may have multiple approach directions normal to each other. In this case, tool collusion detection capability with workpiece and fixture will be necessary. The Fixture planning and modelling capability should be developed.