Exploring Engineering Chapter 15 Manufacturing Engineering

We Cover Three Shop Topics Basic Metal Cutting Operations Speeds and Feeds Shop Safety

Applications of statistical methods  A statistical method of analyzing defective manufactured products will be introduced.  Six Sigma methods will be touched upon.

To Be Safe - Think!

Basic Safety Rules Wear eye protection at all times No loose-fitting clothing or jewelry Do not work alone

Ask Someone If You Are Have Questions = ?

Traditional Machining Operations Turning Drilling Milling

Typical Drill Press

On Lathes, Part Rotates

On Milling Machines The Cutter Rotates

Two Types Of Milling Conventional Milling Climb Milling Part Cutter

Climb Milling Both the cutter and the lead screw move the table in the same direction Climb Milling Part Cutter

Speeds And Feeds Are Like Biting And Chewing Speed: how fast the cutting tool (or part) spins Feed Rate: how hast the part is advanced into the part

Manufacturers Have Recommended Cutting Speeds

Calculating The Cutting RPM Where RPM = revolutions per minute for the cutting tool (mill and drill) or work piece (lathe) CS = cutting speed in surface feet per minute Dia = diameter in inches for the cutting tool (mill and drill) or work piece (lathe) Same equation for drill press, mill, and lathe

The Feed Rate For Milling Chip load values are found in tables (check Machineries Handbook) We will use IPT for high speed steel cutting steel

The Feed Rate For Turning (Lathe)

Variability & Six Sigma  No manufacturedl part is exactly like another.  If everyone in this class measured the length of new a pencil with a suitable ruler, the derived lengths would randomly vary by small amounts.

Variability & Six Sigma  Extend to many points and in the limit of large measurements the data become continuous:  Plot ordinate as “frequency” (fraction of total measurements) vs. Z

Variability & Six Sigma  Plot with abscissa Z and ordinate

Variability & Six Sigma  When plotted this way the area under the curve from -  to +  is 1.00 (i.e., 100% of the samples)  The area from -1  Z  1 contains 68% of the data  The area from -2  Z  2 contains 95% of the data  The area from -3  Z  3 contains 99.7% of the data

Example 1:  You make 1,000 rods of mean length 10.0 cm, standard deviation 0.1 cm.  How many are within a specified range of between 9.8 and 10.2 cm?  Z = ±0.2/0.1 = ±2. Thus 95% of the rods, or 950 are within spec and 50 are not serviceable since out of spec.

Example 2:  You make 1,000 rods of mean length 10.0 cm, standard deviation 0.1 cm.  How many are within a specified range of between 9.85 and cm?  Z = ±0.15/0.1 = ±1.5. In the general case get the area under the normal curve using Normsdist(Z) in Excel.  Normsdist(1.5) yields and  67 rods will fail  Less failures than in Example 1 since the window of acceptance is wider.

Variability & Six Sigma  Variability has deep consequences  For example: Are these two noisy means equivalent? Hint: Add ±boundaries for 2  - if they overlap Then the data cannot be statistically distinguished with 95% confidence

Six Sigma  A quality control program introduced into the US by Motorola to reduce the number of rejects and thus improve the quality of their products.  Z = 6 is the stated target; with some slight of hand it translates to 3.4 defects per million samples)

Summary  Manufacturing engineering is covered in Chapter 15  Machining, cutting, welding, extrusion, pultrusion are all ways of manufacturing different products  Derivation of formulae relating to cutting rates for drilling, milling, and lathe work, are derived.  Statistical analysis leads to better process control and lest rejected widgets being out of specifications.