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The tempered hardness equation becomes RT = (RQ 5.3 10)0.34 + 10 + 11.91 = 0.34RQ + 16.71 The Jominy curve is predicted by noting that the Rockwell C-scale hardness at Jominy station 1 is (RQ)1 = 32 + 60(%C) = 32 + 60(0.40) = 56.0 and a table is prepared as depicted in Table 29.9 for 1000 F tempering temperature. The variation of surface strength with size of round is prepared using equivalent Jominy distances as depicted in Table 29.10. Table 29.11 shows an ultimate-strength traverse of a 11 8-in round. There is only a mild strength profile in the traverse. The significant strength for bending and torsion is at the surface. For the 11 8-in round, the surface ultimate strength is estimated by interpolation to be 164.3 kpsi. The R. R. Moore endurance limit at this location is estimated to be 164.3/2, or 82 kpsi. Steels in large sections or with less alloying ingredients (smaller ideal critical diameters) exhibit larger transverse strength changes. For sections in tension, significant strength is at the center. When testing, machined specimens from the center of a round say little about the strength at the surface. Heat treating a specimen proves little about strengths in the actual part. Some variations in strength attributed to size result from differences in cooling rates. When the number of cycles is less than 107, the endurance strength must be estimated. Reference [29.8] gives a useful curve fit for steels: Sf = Sum m exp (m) mNfcm exp ( f N fc) f Su f N c m f f N c > m f (29.24)

7:

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TABLE 29.9 Surface Ultimate Strength as a Function of Jominy Distance for 4140 Steel Oil Quenched (H = 0.35) and Tempered 2 Hours at 1000 F, Grain Size 71 2

Figure 7-6.

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The measure phase is to identify correct measures, establish a baseline, and eliminate trivial variables (Gupta et al., 2007). The following tools are important to understand:

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TABLE 29.10 Variation of Surface Strength with Diameter of 4140 Steel Round Quenched in Still Oil (H = 0.35) and Tempered for 2 Hours at 1000 F, Grain Size 71 2

Nested Stored Procedures Two tricks can help you debug a set of nested stored procedures (that is, when a stored procedure calls another stored procedure). It is a useful practice to display the name of the stored procedure at the beginning and end of the stored procedure.

strain-strengthening exponent true strain at fracture an exponent commonly in the neighborhood of 1 2 the number of cycles to failure ultimate tensile strength

Basic statistics Statistical thinking Cost of quality Measurement system analysis Critical parameters Critical to quality

Example 3. Estimate the finite-life engineering fatigue strength of an annealed 4340 steel with the following properties: Su = 103 kpsi RA = 0.56 Solution. The endurance limit is estimated as Su/2 = 103/2 = 51.5 kpsi at 107 cycles. Because no strain-hardening information is supplied, it is necessary to estimate m from m Su = Sy (offset) exp 1 or 103 m 65.6 0.002(2.718)

declare @chvProcedure sysname set @chvProcedure = 'prGetInventoryProperties_2' if @debug <> 0 select '**** '+ @chvProcedure + 'START ****' ... if @debug <> 0 select '**** '+ @chvProcedure + 'END ****' return 0

from which m = 0.14. The true strain at fracture can be assessed from the reduction in area: f = ln 1 1 = ln = 0.821 1 RA 1 0.56

When you call a nested stored procedure, you need to pass the value of the @debug parameter to it as well. In this way, you will be able to see its debugging information.

There are two types of statistics, descriptive and inferential. The descriptive statistics summarize the historical data (Gupta et al., 2007). Basic descriptive statistical analysis consists of the mean, median, mode, range, variance, and standard deviation (Gupta et al., 2007). Also measuring the cost of quality is critical because high variations and inconsistencies can cause high cost and waste valuable resources. Inferential statistics is based on analysis of the sample to infer performance of the process (Gupta et al., 2007). It is generally concerned with the source of the data and seeks to make generalizations beyond the data at hand. Inferential statistics can include regression analysis and hypothesis testing among many others.

TABLE 29.11 Variation of Local Strength in a 1.125-in Round of 4140 Steel Quenched in Still Oil (H = 0.35) and Tempered for 2 Hours at 1000 F, Grain Size 71 2

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