The Science of Forming



Things That Bother MePart Two

By: Stuart Keeler

Monday, December 01, 2008
Over many years in this field, I have kept a log of Things That Bother Me. The log contains statements about metalforming made by seminar speakers and classroom attendees, sentences in technical papers, and comments made in magazine articles or ads that make no sense, are completely wrong, or show a lack of understanding by their creator. Last month’s column contained a number of items from the log. This month’s column continues with more items.

Identifying the Material

• “We know all about the coil properties. The mill sent us the certs for our coil.”

• “We have the heat number of the coil that does not work. The mill should be able to tell us what is wrong with our coil.”

Many steel mills produce heats ranging from 100 to 350 tons of steel per heat depending on whether the mill uses electric-furnace or basic-oxygen-process technology. Controlling the chemistry of the heat is of key concern to the steel mills. Some of the specific chemical values of the heat—usually carbon, manganese, phosphorus and sulfur—make up the certs that are available to customers. Chemistry is one of the inputs used to create a specific set of mechanical properties in a coil, but knowing only the chemistry is insufficient to deduce the final mechanical properties.

The heat number is helpful in troubleshooting sheetmetal problems. However, 100- and 350-ton heats will generate approximately 20 and 70 coils weighing 10,000 lb. each, respectively. Any of these coils may have different final properties because each coil could receive different processing. One must keep track of the coil number all the way to the part.

Placing Blame

• “This coil is bad. It does not make good parts.”

The user complains about bad material. The supplier replies that the same coil can make thousands of good parts having a different design. Maybe the die is not good.

User responds: “My die makes perfectly good parts using steel from other suppliers.” The finger-pointing continues with much wasted time and no problem resolution. The fundamental problem is not good or bad material but a mismatch between the material supplied (numerically defined mill-property capability) and the needs of the part design/die/press combination (numerically defined engineering window). A supplier and user team focused on matching mill capability and the engineering window is much more effective and in the end results in buy-in by both sides.

Defining Lubrication

• “Some companies form cold-rolled steel dry without any lubrication.”

• “I do not use a lubricant. My steel only has a rust-preventive liquid put on by the mill.”

Unless your steel has a gritty red surface called rust, its surface has a rust-preventive liquid or some other process to isolate the iron from oxygen. The rust preventive solution, often called mill oil, also acts as a lubricant. Using a laboratory-standard tool and sheetmetal, the mill oil has a measureable coefficient of friction. Many mills can replace the mill oil with a lower-coefficient-of-friction lubricant to obtain better results for more difficult forming while maintaining rust prevention.

Making Decisions Without Data

• “The steel is no good.”

• “I (chose one—feel, think, assume, know) the problem is...”

• “It’s probably…”

• “I am comfortable with...”

• “Go fix the problem.”

We hear these phrases too many times. Troubleshooting or problem solving requires at least two numerically defined progress points before one can even begin the solution portion of the task. The first is a numerical definition of the problem. This definition is the starting point for progress tracking. Problem-solving experts state that creating the numerical definition of the problem is more than half of the problem solution.

The second numerically defined progress point must be the target or final goal. If you do not know when you have solved the problem, how will you ever know when you have been successful? In addition, one now can numerically track progress from the starting problem definition to the final problem target. The five statements above represent defeat before even crossing the starting line—if anyone can even find the undefined starting line. Everyone should prepare a large sign and hang it in their workplace stating, Decision-making without data is another form of guessing.

Using Improper Data

• “The bad sample had a hardness of RB 44, while the good sample had a hardness of RB 42. We set the specification at RB 42 maximum.”

• “The bad sample had a hardness of RB 42, while the good sample had a hardness of RB 44. Now, do we set the specification at RB 44 minimum?”

• “What is the small shiny spot on the backside of the hardness test sample?”

The first two conditions could occur in the same group of samples. Two samples from a population of samples do not make a valid statistical conclusion. There is a range of hardness values for both the good and bad (here we go again with finger-pointing terms) population of samples. Picking only two is not valid. A shiny spot on the backside of the sample in line with the indenter means the hardness test also is measuring the hardness of the test-machine anvil upon which the sample rests. Want a different hardness reading? Change the hardness of the anvil.

Bypassing Normal Logic

• “I use only hot-rolled steel because cold-rolled steel is too hard and the formability is gone after all that reduction in thickness.”

• “I put the one-side-galvanized surface on the outside because the part splits if I put the galvanized surface on the inside as the part print specifies.”

• “I did not know you could get cold-rolled steel without stretcher strains. I have been ordering dead soft steel and polishing them off of all of my parts.”

Misconceptions and not searching for acceptable solutions can be expensive.

After reviewing the list of Things That Bother Me, one must begin to wonder where to focus attention as we move into a more competitive world environment. Will new and faster presses, computers, CAD, electronic measurement systems, and other expensive equipment and programs by themselves solve all our problems? On the other hand, do we need to step back and establish a solid foundation of simple understanding of the how and why things work?

We all remember GIGO: garbage in, garbage out. Unfortunately, today, with so many computers and electronic devices, GIGO has become garbage in, gospel out. We now must understand what we feed into our computers, dies and everything else associated with metalforming. MF


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