Weibull Analysis at Ivy U

Folks,

Let’s check in on how Patty is doing at Ivy U.

Patty was nearing the end of her teaching stint at Ivy U. Only a few more classes remained. She had to admit that she was sad to see this adventure end. Oh, well, such is life.

The syllabus allowed for the last few lectures to cover “Selected Topics,” so Patty decided that her selected topics would be Weibull Analysis. She felt passionately that all engineers should have some exposure to failure analysis and this topic fit right in to engineering statistics.

Before she knew it, she was heading north up to Ivy U for her next-to-the-last lecture. She thought she should soak in the beauty of the campus as her car approached, for soon this would be her last time at Ivy U for a while. Today she was lucky, she found a parking spot right away.

As she walked into the main engineering building, she noticed a note in her mailbox. It was from Dean Howard. She quickly opened it. He was requesting a brief meeting after her last class.

“Yikes!” thought Patty, “Dean Howard wants to see me! I wonder if it’s serious. Did I goof up, somehow?”

She would have to wait for two days to find out what the Dean wanted and she couldn’t worry about it now, as her class was starting in 10 minutes.

Patty began the class by explaining the development of Weibull’s theory and gave a few examples. She showed where the scale factor and slope came from. Patty emphasized that a steep slope indicted a tight distribution of data (a good thing for prediction from the data) and that a larger scale suggested a longer mean life. She then discussed the importance of different types of tests in electronics, such as thermal cycle testing and drop shock testing. As an example, she thought she would share some accelerated thermal cycle data for two different alloys that are used in electronics assembly.

She showed the first set of data in a PowerPoint slide (Figure 1).

“Can someone explain these results to me?” Patty asked.

After some murmuring, Karen Armstrong raised her hand.

“Yes, Karen,” Patty responded.

“It appears that Alloy 2 demonstrated superior performance, as seen in its much steeper slope and slightly better scale,” Karen answered.

“Nice job, Karen,” Patty responded.

“What about this one point?” Patty asked as she pointed to the obvious outlier for Alloy 1.

There was more murmuring, but no one raise their hand. So Patty showed a slide with the outlier removed.

“I have removed the outlier because failure analysis showed it was atypical,” Patty said.

“As you can see, now alloy 1 has a slightly better slope. This suggested a tighter distribution and hence more ability to predict performance,” she went on.

There was now very loud murmuring, finally Scott Bryzinski raised his hand.

“Yes, Scott?” Patty responded.

“Professor, it just seems like cheating, dropping a bad data point because you claim it is not representative of the other samples,” Scott explained.

There were many loud echoes of agreement.

Patty chuckled a little.

“OK, OK, you are right. It is not fair to censor a data point in most cases. This is part of the lesson of this class. Don’t censor data lightly,” Patty said.

“Let’s look at data for Alloy 3 and 4,” Patty went on.

The students looked at the data for some time and finally Diane Pompey raised her hand.

“Yes, Diane,” Patty acknowledged.

“They look about as dead even as one could expect, except that the sample sizes are different. Alloy 3 has 15 samples and Alloy 4 only 13 samples, as can be seen in column ‘F’ in the ‘Table of Statistics’,” Diane explained.

“Nice work Diane, few people would have picked up on that difference,” Patty replied.

“I will tell you that both alloy 3 and 4 had 15 samples to start with in the test. What do you think happened?” asked Patty.

Very quickly, Fred Wilkins raised his hand. Patty nodded to him.

“I’ll bet that two of the samples from alloy 4 did not fail,” Fred suggested.

“Correct!” Patty responded enthusiastically.

“I want you all to take a few minutes to discuss this situation with those seated around you. I then want you to vote anonymously whether the two samples that did not fail make alloy 4 the same, better or worse than alloy 3,” Patty instructed.

After five minutes of noisy discourse, the students voted on a website, the results of which Patty could show on her laptop and project to the class. Twelve students thought the alloys were still the same. 24 thought alloy 4 was better, and 6 thought alloy 4 was worse.

“Any comments on the results?” Patty asked.

There were no takers.

“Let’s assume that the two samples that failed were tested for a much longer time and they finally failed at some very high number of cycles, say 2,000. Let’s look at what the Weibull plot would look like,” Patty said.

She then showed Figure 4.

“Can anyone explain it?” Patty asked.

After a short time, Young Koh raided his hand.

“Dr. Coleman, the added cycles increased the scale significantly, but ruined the slope, suggesting much more scatter in the data. As you suggested earlier, reliability testing is about hoping to have the ability to predict lifetime. With the large decrease in the slope, prediction becomes much more difficult, So, sample 4 is likely worse than sample 3, even though it has a large scale.” Young expounded.

“Precisely,” Patty answered.

“It is interesting to note that many engineers in the electronics industry today just ignore the samples that don’t fail,” Patty went on.

The class looked at her with shocked faces.

“Well, that’s all until next time,” Patty said.

“Two of the female students, Jessica Han and Mary Connor, stayed after the class to talk to Patty.

“Professor, there is a rumor that you will be teaching “Manufacutring Processes” next term, is it true?” Mary asked. Then went on, “We really hope so. You are best teacher here.”

Patty was so touched she started getting a little misty eyed, “Thank you for your kind comment, but I doubt that that will be the case,” she said as her voice quavered.

Will the Dean fire Patty or will she be teaching Manufacturing Processes the next term. Stay tuned to see.

Cheers,

Dr. Ron

 

Avnet: Software Distributor?

Companies stand still at their peril. So pay close attention to today’s announcement by component distributor Avnet about its acquisition of Magirus Group.

Make that component and software distributor Avent.

For Magirus not only has an attractive footprint in Europe and the Middle East, but its product line centers on software and systems for storage, cloud computing, security,  and information life-cycle management.

So in addition to adding more than half-a-billion dollars in revenue to the top line, Avnet extends its linecard into a very hot growth area.

Companies stand still at their peril.

No Counterfeits, No Excuses

In a move that already is causing no small degree of consternation, President Obama last Saturday signed a new law that places the onus squarely on the Pentagon’s supply chain for ensuring all electronics components in all defense products are legitimate.

The bill, part of the 2012 National Defense Authorization Act, requires that the Department of Defense, the Department of Homeland Security and their contractors  “detect and avoid counterfeit parts in the military supply chain.”

Counterfeits have been a known problem for years. (CIRCUITS ASSEMBLY has been warning of the issue at least since I came aboard in 2005.) I was personally told by a QA manager at one prime contractor that no less than a fourth of all the parts in some of its systems were suspected to be faked or otherwise out of compliance. And workshop after workshop told the tale of rivers of parts being shipped as e-waste to China, primarily the Shenzhen area, where they were separated and stripped from circuit boards, cleaned (usually in polluted water), sanded and remarked, and then resold into the supply chain. In a keynote at SMTAI in 2010, Tom Sharpe of independent distributor SMT Corp. noted some 29,000 incidents of counterfeits were reported to the US Department of Commerce between 2005 and 2008.

But the turning point, according to some analysts, was a Nov. 8 Senate Armed Services Committee hearing at which Congress heard compelling testimony on the sheer volume of fakes in the US military supply chain, including the results of a Government Accounting Office sting operation targeting electronics parts counterfeiters.

The evidence spurred Committee Chairman Carl Levin (D-MI) and Sen. John McCain (R-AZ) to lead a bipartisan effort to act. The result: legislation that establishes a program of enhanced inspection of electronic parts imported from any country determined by the Secretary of Defense to be a “significant source of counterfeit parts” in the DoD supply chain. The bill further requires defense contractors to establish policies and procedures to eliminate counterfeit electronic parts from their supply chains, and for the DoD to adopt policies and procedures for detecting and avoiding counterfeit parts in its own direct purchases.

Most important, the new law states those contractors that fail to detect and avoid counterfeits, or fail to exercise adequate due diligence, can be debarred. Furthermore, contractors can no longer charge the DoD for rework or related costs to remove and replace counterfeit parts, and they are held liable for any remedies required, regardless of where the counterfeit entered the supply chain.  The law affects all contractors at all tiers and is not limited to direct acquisition of parts. In other words, an EMS firm would be responsible for the counterfeit solder mask (yes, that happens) on a PCB it sourced from a fabricator in Asia (yes, that happens too).

Counterfeiting runs the gamut from the mundane to the highly sophisticated. In some cases, the trickery is performed by crude remarking and easily caught by a diligent inspector with an eye loupe. But at the upper end, it has evolved into a wholly systemic problem; again, we have been reporting on the “fourth shift” at various semiconductor factories, where workers build parts using legitimate materials and lines, but those parts are not subject to rigorous inspection and are sold “out the back” to unscrupulous third parties. In one egregious episode, VisionTech Components administrator Stephanie McCloskey was sentenced to prison and her boss, Shannon Wren, died of a drug overdose after facing similar charges for duping the US government in a long-running scam.

There is no question the supply chain has found counterfeit detection and prevention an expensive and difficult undertaking. XRF, chemical or laser etching and DNA marking are three of the more sophisticated means, although each adds time and cost to traditional inspection methods.

But the problem is too pervasive, and the risks too great, to whine about the costs. Counterfeiting has gotten completely out of hand. For those reasons, we welcome the bill and its well-conceived structure that puts the onus not on the taxpayer (via pass-along costs) but on the supplier, where it belongs. If this means contractors will have to start relying more on known-good suppliers, well, that’s not a bad thing either. I’ve seen far too many instances of high-level buyers at OEMs and EMS companies searching for parts on LinkedIn to be confident that the auditing many claim to have in place is being taken seriously.