Category Archives: Dr. Ron

Materials expert Dr. Ron Lasky is a professor of engineering and senior lecturer at Dartmouth, and senior technologist at Indium Corp. He has a Ph.D. in materials science from Cornell University, and is a prolific author and lecturer, having published more than 40 papers. He received the SMTA Founders Award in 2003.

Wet Gold II: Measuring Gold Content in an Ore With Only a Scale

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Folks,

In my last post we saw how you could measure density with only a scale.  In this post, we will expand on that technique and learn how to measure metal content in gold/quartz ore.  In principle, this technique could be used for other ore, but the ores can only be two part (e.g. gold and quartz) systems. Gold is a “natural” for this analysis as it is typically pure gold with quartz.

Gold is often found “veined” in quartz. I was certain that this was the origin of the “Golden Fleece,” the fleece being the white quartz with the gold on top. However, a little research did not clarify this belief.

Anyway, let’s assume you take a few weeks off from work. Leaving the world of solder paste, TIMS, ITO, wave solder flux and solder preforms behind, you set out for the west in search of some large gold nuggets.  Fate was with you in that, in a short time, you find a gold/quartz specimen as shown below.   The images, and the new “wet gold” weighing technique I will discuss, are from Bill and Linda Prospecting.

You are so excited you are shaking.  The only tools you brought are a scale, some string and a beaker.  To determine that gold content, you need to measure the weight of the gold in air and under water.  But you only have the scale as shown below.  What can you do?

After measuring the weight of the ore in air, fill the beaker part way with water, place it on the scale and zero the weight.  Then insert the ore on a string as shown below.  The scale will now read the weight of the volume of water that the ore displaces.  Let’s call this weight of the water displaced WD .  The wet weight of gold (weight of gold under water) will be the weight in air minus WD.  So we now have the weight in air and the weight in water.

 

The derivation of the equation that tells us how much gold is in the ore is at the end of this post.  The final equation we need is WAu = 3.07WW – 1.91WAir.  For our ore sample WAir = 25.1 pennyweight (pw). A pennyweight is 1/20th of a troy oz.  WD as shown in the photo above is 8.3 pw.  So WW = WAir – WD = 25.1-8.3 = 16.8 pw.  So WAu = 3.05*16.8 – 1.91*25.1 = 3.635 pw.  Subsequent analysis showed that the gold content was actually 3.9 pw and error less than 7%.  Not too bad for a simple field measurement.  At $1600/oz our ore sample contained. a little over $300 dollars of gold.

This technique could be used to measure the density of an alloy as in the last post.

Cheers,

Dr.Ron

The Derivation of the Equation

 

RoHS, Six Years After

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Folks,

I was at IPC Apex Expo the other week.  San Diego is a great venue for the show, but I always forget how cold it can be (55°-65°F) this time of year.

While at the show, I was interviewed on lead-free reliability and its cost for consumer electronics. These are topics I think about often, so let’s discuss them a bit. First, let’s consider reliability.  RoHS was enacted on July 1, 2006, more than 6 ½ years ago. Each year more than $1 trillion worth of electronics are made, therefore, in this period of time, something over $3 trillion worth of consumer electronics have been manufactured. There have been no “the sky is falling”-type of reliability issues in this time. How can I say this? Well, my office at the Thayer School of Engineering at Dartmouth is across the hall from the IT (information Technology) Dept. They purchase all the millions of dollars worth of PCs, printers, displays etc. that Thayer uses. Several years ago (say early 2011) I stopped by when most of the department was in and cheerfully asked if the reliability of the equipment they purchase has gone down since lead-free assembly was enacted. They asked me in unison, “What’s lead-free assembly.” After I explained what lead-free assembly was, they confirmed that they have noticed no changes in reliability. Since RoHS, my family has purchase about 100+ electronic devices, a few have had reliability problems, about as many as in the past. Most were attributed to hard drive fails. Of the scores of friends and colleagues I have, no one has ever commented that they have noticed an increase in electronics fails. So, my conclusion is that consumer product reliability is not “practically” worse if my family and  these many  other folks haven’t noticed it.

I have made an informal study of reliability data of lead-free vis-a-vis tin-lead solders published in papers. A statement from Rockwell Collins’ JCAA/JGF-PP No Lead solder Project: -55C-125C Thermal Cycle Testing Final Report  sums up my overview conclusion nicely: “Test vehicles assembled with lead-free materials (notably tin-silver-copper) exhibited lower reliability under some test conditions.”  Naysayers might be quick to suggest that this statement says that lead-free is no good. However, the statement could be reworded to say: “In considerably more than half of the test conditions, test vehicles assembled with lead-free materials had higher reliability.” Counting the comparisons in the Rockwell-Collins paper shows lead-free better in 51 cases, tin-lead better in 31 cases, and one draw. However, it is disturbing that a small percentage of lead-free assembled test vehicles had much much worse reliability than tin-lead test vehicles. This later information makes me believe that lead-free is not yet ready for mission-critical, high-reliability, long-life products. These small numbers of much poorer reliability assemblies must be understood and corrected before lead-free is ready for mission-critical prime time. The much shorter lifecycle of today’s consumer electronics may also mask this concern.

What about cost? I don’t at all want to minimize the expense that many went through to go lead-free and RoHS compliant. In about 2007, one of our colleagues estimated that it cost the electronics industry $20 billion to become RoHS compliant. I think this number is low, but, from a consumer’s perspective, there has been no cost hardship. The price of a PC continued to go down during and after RoHS implementation, as shown in the figure below. While performing my non-scientific survey of co-workers, family, and friends on reliability, I also asked about cost. All agreed, electronics are cheaper than ever.

 

Challenges still exist, even in consumer electronics with the Head-in-Pillow, Graping, non wet opens, and other defects.  However, we can all purchase lead-free, RoHS compliant products at a reasonable cost and reliability.

 

Cheers,

Dr. Ron

The source for the image is :http://thomaslah.wordpress.com/2010/02/03/apple-and-intel-defying-gravity/

 

Best Wishes,

Dr. Ron

Measuring Alloy Density

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Folks,

In the category of interesting requests, Ron, a gold worker, from Guyana, sent me the following note:

Dr. Ron,

My colleagues use a “wet” gold technique to measure gold alloy density.  Is this valid?  Where does the formula come from?

Sincerely,

Ron

Well, to tell the truth, I had never heard of it and was skeptical. How can you measure density (mass/volume) by only measuring weight? So, I investigated. The technique claims that one can measure density with only a scale, by measuring the alloy’s weight in air and in water.

I could find no derivation, so I thought about it and derived it on my own. As far as measurements go, as stated, you only have to measure the weight in air and water. If you don’t have a scale that can handle being immersed in water, you can use a hanging scale (think weighing a fish). So, after weighing the alloy in air, you immerse it in water. It will weigh the amount of water it displaces less.  The derivation is below:

As an example, let’s say you have a gold alloy ingot that weighs 1,000 grams (OK, I know grams is mass, but we are all sloppy and use it as weight, too) in air.  You weigh it in water and it weighs 930 grams. From the formula below, the alloys density is:

r = 1000/(1000-930) = 14.29g/cc

Since the density of gold is 19.3g/cc, the alloy is not pure gold.  If you knew the alloying element, say copper, you could use a Solder Alloy Density Calculator to determine that the alloy was 69.8% gold, 30.2% copper. If there are multiple alloying elements, since most of the common elements have a density of about 9 g/cc, you can even estimate the fineness of the gold.

Could this technique be used to measure the alloy density of say a handful of solder preforms? Sure, you could put them in a woven bag of non-hygroscopic material and weigh them in air and water. Admittedly, measuring the density of solder paste, with this technique, would be a challenge.

Next posting, I will show how this technique is used to measure the quantity of gold in gold/quartz ore.

Cheers,

Dr. Ron

Weibull Part III

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Folks,

Our discussion of Weibull Analysis continues…. Let’s say you have worked hard and assembled some SMT lead-free PCBs for thermal cycle testing. You used the best lead-free solder paste and some lead-free solder preforms as you assembled several through-hole components with the pin-in paste process.  You were a little concerned with the assembly process as the board was thermally and physically massive and the reflow process needed to be a bit above the recommended temperature and time.

The results of the thermal cycle testing are shown in Figure 1 below. You dutifully report the characteristic life (or scale) as 2,387 cycles and the first fail at 300 cycles. You were quite disappointed, as in the past similar, but slightly smaller boards, had a slightly higher scale, but more importantly, the first fail was about 1,000 cycles. Anyway, you write up your report and file it away.

Figure 1. A Weibull plot of the thermal cycle data.

Hold on! The data are screaming at you the something is going on. Look at the same data in Figure 2. Note two distinct lines shown in green. These two separate lines suggest very strongly that there are multiple failure modes. The line furthest to the right is likely the typical failure mode observed in the past. The line to the left is a new early failure mode. It could be due to something like oxidized pads or some other phenomena not seen when testing similar but smaller boards. Root cause failure analysis should be performed to try and understand to new failure mode.

Figure 2. A Weibull plot of the thermal cycle data with multiple failure modes noted.

Now for a human interest note: One of the rewarding aspects of being a professor at Dartmouth is the outstanding nature of many of the students. They are not just good academically, but often are talented artistically, athletically, etc. This point was brought home to me recently.  In a class I teach, ENGS 1: The Technology of Everyday Things, we were recently discussing the conservation of angular momentum (CoAM). One of the most striking ways to demonstrate CoAM is an ice skater’s spin. I went on the internet and could not find a good video of a spin. I then remembered that one of my former students, Julia Zaskorski, was on Dartmouth’s figure skating team. I asked her if she had a video she could share. It appears here. She is a materials science and physics major. Who knows, maybe we will see her at APEX or SMTAI in a few years.

Here is a little bio in her own words:

My name is Julia Zaskorski, and I’m a junior from Wellesley College taking part in the 12 College Exchange Program at Dartmouth. At Wellesley I am majoring in physics with the intent to pursue mechanical engineering. Despite Wellesley’s relationship with nearby MIT, Wellesley does not have its own engineering program, so I sought out the more self-contained curriculum and atmosphere at the Thayer School of Engineering.  In addition to the draw of the Thayer School, the Dartmouth Figure Skating team was also a hugely motivating factor for my exchange, as Wellesley does not have a team, let alone a rink.  I have known the coach of the Dartmouth team for several years now, and to finally see my name on the roster for the team is a dream come true.  The engineers, as well as the winter activities here in Hanover, pulled my heart to Dartmouth long before I’d ever set foot on campus. 

Cheers,

Dr .Ron

 

Weibull Analysis II: The Curse of the Early First Failure

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Folks,

In continuing our discussion on Weibull Analysis, let’s assume we assembled some SMT and through-hole PCBs with lead-free solder paste. On this board are also some bottom-side terminated (BTC) components (often called QFNs), that are also assembled with solder preforms.  A stress test is performed to test the BTCs. In such a test, the first fail in Weibull analysis is the most important data point. No matter the results of remainder of the data, these later fails cannot undo the effect of a very early first fail.

To understand this concept, let’s look at the Weibull chart below. In many high reliability applications, there may be a requirement that some small percentage of the components under test have at least some minimum reliability.

 

Figure 1.  Weibull Analysis with an Early Fail.

As an example, let’s say that 1% of the components cannot have less than 500 cycles of life.  By looking at Figure 1, we see that 1% have less than 150 cycles of life (see arrow.)  This one early outlier dramatically affects the Weibull Analysis.

However, if that outlier was removed, as seen in Figure 2, the data suggest that 1% of the components will have a life of 900 cycles. We can see the dramatic effect the first fail has on this result. Note that the first fail does not affect the “scale” or characteristic life much (2647 vs 2682). Hence, the characteristic life, is not a robust metric to use in a high reliability environment. However, the shape or slope is dramatically affected by the early fail as it changes from 2.22 to 4.23 when the early fail is “censored.”

Figure 2. Weibull analysis with the early fail removed (censored).

Why might an outlier like this exist? Almost certainly there is something unusual about the early fail. It might be something like an oxidized pad preventing good wetting of the solder. Perhaps something like this failure mode might be discovered in root cause failure analysis. However, I am typically opposed to censoring data, even with supportive failure analysis. I think the test should be done over. It is often too easy to talk yourself into accepting inconclusive failure analysis.

What is your opinion?

Cheers,

Dr. Ron

Interpreting Weibull Plots: I

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Folks,

A while ago I discussed the Weibull Distribution and its importance in electronics reliability analysis. This distribution has been used to evaluate the life of solder joints whether formed in SMT, wave, or even using solder preforms. In the next few posts, I would like to discuss how to interpret Weibull plots.

Let’s consider two Weibull plots from thermal cycle testing of lead-free solder joints as seen in Figure 1.

Figure 1. A Weibull plot of thermal cycle data for Alloy 2 and Alloy 4.

Both alloys have almost exactly the same scale, or characteristic life. You will remember that characteristic life is the number of cycles at which 63% of the test subjects fail. For Alloy 2 it is 2,593 cycles and for Alloy 4 it is slightly better at 2,629 cycles. However, these two alloys performed dramatically differently. The most striking difference is in their “spread.” We see this much greater spread for Alloy 4, when we plot a fit to the data as a normal distribution, as in Figure 2 below.

Figure 2. The best fit normal distribution plot for Alloy 2 and Alloy 4.

In the Weibull plot, the data for Alloy 2 has a very steep slope or shape factor, this indicates a tight distribution. A tight distribution is desirable as it facilitates more accurate prediction of thermal cycle life. Alloy 2 is clearly superior. So, in a Weibull distribution, not only is a large scale factor or characteristic life desired, but so is a steep slope or larger shape factor.

Next time we will talk about outliers.

Cheers,
Dr. Ron

Pondering the Past, Present and Future

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Folks,

Let’s step away from electronics assembly challenges, and deep considerations of solder paste, solder preforms and wave soldering, to ponder where electronics have gone in the past decade or so.

The mobile phone of the early 2000s was just that, a phone. Today it is a phone, music player, PalmPilot-type organizer, camera (still and video), video player, gamer, TV remote, GPS system, web portal, etc. There is almost nothing electronic that it can’t do. The USB memory stick of 2002 with 0.5Gb of memory cost $500, today $5 will get you a 4Gb one, a cost reduction of 800:1 the equivalent of halving in price about every year.

There is no reason to expect any less dramatic advancements in the future. But, predicting the future of electronics is never easy. In the January 2013 edition of Scientific American Ed Regis wrote an article titled, “The Bold and Foolish Effort to Predict the Future of Computing.” In this article, Regis interviewed eight computer luminaries, including Stephen Wolfram and Nathan Myhrvold, to ascertain their perspectives on where computing will be in 150 years. The conclusion was that no one can predict the future of computing. As interviewee George Dyson said, “All I can guarantee is that any prediction will be wrong.”

One person less humbled by the difficulties of computing predictions is Ray Kurzweil. His prediction success level of more than 80% would seem to support such confidence. Kurzweil also just got a new job at Google. I am finishing his new book How to Create a Mind: The Secret of Human Thought Revealed, and, while I finding it fascinating, I think he goes too far. He believes the mind is a sophisticated computer and that, when computers get to a certain point equaling and surpassing the human mind’s computational ability, they will be considered human.

Supporting this point, he hopes to, someday, resurrect his father, as Bloomberg states:
“Among the stranger things Ray Kurzweil will say to your face is that he intends to bring his father back to life. The famed inventor has a storage locker full of memorabilia—family photographs, letters, even utility bills—tied to his father, Fredric, who died in 1970. Someday, Kurzweil hopes to feed these data trove into a computer that will reconstruct a virtual rendering of dear old Dad.”

Call me a religious fanatic, but I think there is something more to each of us than our memories and our brains’ computing ability.

Kurzweil endorses the IBM Watson computer system’s victory in Jeopardy in February of 2011 as a major step in the direction of computers as humans. IBM provided commercial support for these Jeopardy episodes. In the commercials they strongly reminded us that Watson was not thinking, but only doing what it (not “he”) was programmed to do. Someone summed it up nicely, Watson won, but did he know he won?

I think there are a few major things that people like Kurzweil minimize when they propose that computers will be recognized as human. These points are:

1. Humans are sentient (they would know whether or not they won or lost Jeopardy; we have emotions and feelings). I know of no progress in sentience development for machines.

2. Humans have a will. We get up in the morning, we decide what we will do that day and do it. There is no progress (thankfully?) in giving computers a will.

3. Humans have a biological body. We smell the newly cut grass, feel a refreshing breeze, get tired, enjoy a meal, enjoy sports etc. It is easy for some to minimize the importance of the body in being human. Again no progress in this area.

However, I don’t want to minimize much of what Kurzweil predicts. In her groundbreaking book, Alone Together, Sherry Turkle tells us that, in addition to the fact that the average teenager in the US sends 200 text messages a day, electronic companions already exist. As time goes by they will become more realistic and will be capable of interesting and stimulating speech and interaction. Having all of the world’s knowledge at their fingertips (pun intended), these companions will likely be more stimulating than people, they will easily pass the Turing Test, and, for good or ill, will make us more “alone together” than ever. But our companion will not love, fear, hate, or know that it is a companion.

As has been pointed out, this brave new world is coming whether we like it or not.
Btw, on another topic, the History Channel has produced a terrific video series, Men Who Built America. It is a the spellbinding story of Vanderbilt, Rockefeller, Carnegie, J. P. Morgan, Edison, and Henry Ford. If you missed it, it is coming out in DVD in January.

Cheers and best for the New Year,
Dr. Ron

Pete Rides the Wave

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Let’s see how Pete is handling the wave solder crisis.

Pete had to admit that he was surprised by the positive outcome of his meeting with Fred Castle. He had sent Patty a text the day before, after he took the operators to lunch, before meeting Fred. The text was a little negative. So he was eager to send her the good news about the surprises in his two meetings with Fred since then. He was frustrated that he kept on getting her voice mail. Finally she answered.

“Advanced Processes,” Patty speaking.

“Hey, kiddo, it’s your favorite process genius!” Pete responded cheerfully.

“Oh, this must be Oscar Patterson!” Patty joked, and they both laughed. Patterson was an annoying chap they had to deal with a few years ago. He topped their list of most annoying people. Pete had almost come to fisticuffs with him.
“How is it going there?” Patty asked.

“Shockingly well. My meetings with Fred Castle were very productive,” Pete answered.

“Well, that is shockingly positive news. But I thought he said, ‘I’ve forgotten more about wave soldering than you’ll ever know,’” Patty responded.

“That’s the first thing he said to me when we shook hands, but he was clearly teasing. He slapped me on the back at the same time and chuckled. He went on to say that he had worked in wave soldering for over 30 years, typically at companies that had processes that were out of control. It was clear that he understood a lot about wave. We talked for 30 minutes about what makes a good wave process. As far as I could tell he was right on in everything he said. I think the operators didn’t pick up on his teasing, by the way,” Pete elaborated.

“What about special cause vs. common cause?” Patty queried.

“He didn’t have a clue,” Pete replied.

Patty was bracing herself. She was concerned that Pete might have insulted Castle.

“And you didn’t tell him he was an idiot?’ Patty teased.

“Patricia! I’m shocked you could even think such a thought,” Pete replied.

Pete went on, “We bonded, and he admitted that he was frustrated with the yield loss increasing. He was studying the situation and spending a lot of time trying to figure out the issues. He said he was having trouble sleeping. He mentioned that, in his last job, he was responsible for the wave processes at 10 locations. He was constantly fighting fires and got good at it. He had never worked at company that performed DoEs and developed optimized processes.”

“I’m dying to know how this situation worked out,” she interrupted.

“Patience, patience,” Pete admonished jokingly. He continued, ”It was clear that Fred likes to learn, so I mentioned that, recently, The Professor had mentioned the importance of understanding the differences between common cause and special cause variation when trouble shooting a process. I suggested that maybe studying these topics might help. So I gave him a few links to The Professor’s posts on common cause and special cause.” (Au:  If you are not familiar with common cause and special cause fails, it will be helpful understanding this story to read The Professor’s post.)

“What happened then?” Patty asked, the impatience in her voice apparent.

“Remember, this is now the end of my first day. I watched the process in the morning, took Molly and Chuck to lunch, and then met with Fred. On the second day I had a morning meeting with the quality director, Pam. Then Castle and I went to lunch,” Pete elaborated.

“And?” Patty asked impatiently.

“Castle was all excited. After studying common cause and special cause all night, he realized that he was seeing common cause fails in his detailed scrutiny of the wave line. By adjusting the process parameters slightly when he found a common cause fail, he was moving away from the optimized process settings that were determined by a DoE, so the failure rate got worse. In his previous job, he was mostly seeing special cause fails, as the processes were not optimized, so he was used to intervening,” Pete explained.

“It seems like he won’t have enough to do now,” Patty commented.

“I suggested he help quality. They are stretched thin and he is a detailed-oriented fellow. He keeps meticulous Pareto charts of the fails,” Pete said.

So, where are things now?’ Patty asked.

“Yesterday and today, first pass yields are at 96%. Fred also started helping quality today. It felt good to help and not offend,” Pete finished.

Patty thanked Pete for the great job he did and complimented him strongly for being successful and making friends at the same time. As she hung up the phone, she saw an email from Pam Olinski in her in box. It was a kind note thanking her and Pete for his help. It recounted much of what Pete had said.

She wistfully looked out her window. She was happy and grateful for all of her success, but, to be truthful, she missed the action of being out on the shop floor solving these types for problems.

She was jolted from her chair when she suddenly remembered it was her turn to take her twin sons to karate lessons. So she packed up quickly to pick them up at her mother-in-law’s, to get them to the gym by 5PM.

Patty and the Professor: The Twiddler

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Folks,

It’s been a while. Let’s look in on Patty…

Patty stared, bleary eyed, at her laptop screen. It was the day after the election. She and Rob were following the election closely as a “statistical thinking” exercise. They had met at a conference with The Professor in late October and agreed that following the election would test their statistical thinking skills. They established beforehand that they would not discuss who they favored, just the data.

All agreed that Mitt Romney had a greater challenge than President Obama.

As Rob said, “Of the six most populated states, even the Republicans agree that Obama will win California (1), New York (3), Illinois (5), and Pennsylvania (6). Romney is only a shoe-in for Texas (2). Only Florida (4) is a toss up.”

“I thought some analysts were saying that Pennsylvania is in play,” The Professor commented.

“They’re dreaming,” Patty said with conviction. “Pennsylvania has too many big cities; typical Democrat strong holds,” she continued.

“Many pollsters have 255 electoral votes in Obama’s column and only a little over 200 for Romney. It’s hard to see a Romney path to victory. It is statistically unlikely he could win all of the swing states” Rob added.

The Professor beamed as he listened to his protégés intelligently analyze and argue the situation. They all agreed that it was hard to understand why many were referring to it as a close race, although voter turnout could change everything.

As election night went on, Patty felt she could call the election at 8PM EST. However, she was sympathetic that the networks needed a high level of certainty. The major networks were finally calling it at 10PM. When they did, Romney was ahead in the popular vote by about 1 million. Patty chuckled to herself, when a renowned TV anchor commented that it might be a governing challenge to Obama to win the electoral college and not the popular vote. Clearly he had not factored in the fact that, although California was “called” for Obama around 10PM EST, it was called with only a few percent of the votes in. The networks were using exit polls and statistical analysis to make a projection. By the time all of the west coast votes were counted, Obama will comfortably win the popular vote – because of California’s large population. Patty thought this should be obvious to the pundits.

Patty had stayed up until about 11PM to watch the results. It was comforting that her analysis was spot on. However, she was so “wound up” that she couldn’t fall asleep and she was now paying the price.

As her attention shifted back to the email she was writing. Suddenly, she was jarred by a loud, cheerful voice.

“Hey kiddo, pack your bags, looks like we’re on the road again,” Pete said loudly.

As usual Patty thought. “How does Pete always know these things before I do? I’m the boss!”

“What’s the scoop?” Patty asked.

“Remember our facility in Ohio? They are having wave soldering yield and throughput problems,” Pete answered.

“What!” Patty shouted. “We spent a lot of time there six months ago optimizing their wave soldering operation and teaching them the appropriate use of solder preforms. What happened?”

“Not sure,” Pete responded. “I thought we worked really well with their team and developed a good process. It seemed to me it was one of the more productive projects I was involved in in quite awhile.”

“And you didn’t even offend any of the senior managers,” Patty teased.

Pete chuckled but his cheeks did turn a little red. Pete was a terrific process engineer, but he had a little bit of a short fuse, although he was usually right.

“In talking to some of my buddies there, they told me that senior management hired a very senior fellow who is considered an expert in wave. Strangely, things fell apart right after he joined,” Pete explained.

“Well, you are on your own for this one. I’ve got a number of family commitments over the next two weeks,” Patty said with a little sadness in her voice. Patty enjoyed these types of challenges. “As soon as I get the official request, you’ll be on your way,” Patty said. “Oh, and don’t offend anyone,” she teasingly finished.

As Pete left her office, she checked her emails. Sure enough, there was a note from Mike Madigan asking her to intervene in this wave soldering problem.

Two days later Pete was in ACME’s Ohio facility sitting in the office of Pam Olinski, the site’s quality manager.

“Pete, I’m so glad you could come. Three months ago our wave soldering first-pass yield was 95% and our production was about 2,000 boards per day. Yield is now 90% and production is off 15%. Help!” Pam said.

“Tell me about the new guy,” Pete inquired.

“Fred Castle; he has very impressive credentials, but he has been running the wave process like a dictator. He stops the process a lot to adjust the wave machine. I think he will be offended that you are here to audit the process,” Pam finished.

Because of this concern, they agreed that it might be best to have Pete initially view the process from afar. They decided that Pete would be given an operator’s smock and walk around the shop floor for half a day or so.

As Pete arrived on the shop floor, almost immediately he saw Fred stop the wave machine and make some adjustments. While making the adjustments, Fred held a board in his hand — and he looked at occasionally. After the wave machine was running again,

Pete saw that Fred looked carefully at every board. Pete saw one of the wave operators was going on a break. Pete remembered Molly Stark from his visit to optimize the wave process six months ago, so he stopped her and ask if she could join in for lunch.

The morning passed quickly, and Pete was off to lunch with Molly. As Pete had suggested, Molly brought another operator, Chuck Petrus to lunch. Pete insisted on treating, so Molly and Chuck left their brown bags behind.

In total, Fred stopped the line four times during the almost 4 hours of Pete’s observations. Each time he made adjustments on the wave machine.

After exchanging pleasantries Pete asked, “Why was that fellow stopping the wave line so often?”

Molly got quite animated and answered, “That’s Fred Castle, the supposed wave genius. He stops the line every time there is a defect and adjusts the wave machine parameters. A number of us complained to him that he shouldn’t make adjustments on the machine that with just one fail. That’s what you taught us.”

“What did he say?” Pete asked.

“ ‘I’ve forgotten more about wave soldering than you will ever know.’ No one has said a word since,” Chuck responded.

“You and Patty taught us about special cause and common cause variation. I don’t think Fred understands that,” Molly commented.

“He’s also a knob twiddler,” Chuck added.

Does Fred know the difference between common and special cause variation? Is that the root of the yield and throughput problems? What is a knob twiddler? Stay tuned to find out.

Can Your Mortality be Modeled with Weibull Distribution?

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Folks,

In the last posting we saw how Weibull analysis helped us to determine that SACM lead-free solder (SAC 105 with about 0.1% manganese) has comparable (actually better) thermal cycle performance versus SAC 305 solder.  Software like Minitab will give us even more detailed information about the performance of the solder joints in stress testing as we see in Figure 1.

In addition to the Weibull plot, we also have the Probability Density Function (PDF), the Survival Function and the Hazard Function. The PDF tells us when it is most likely that a test board will fail in a test population, as shown by the inserted red line. We see that it is a little less than 2,000 cycles. The Survival Function shows the percent of surviving test boards. We observe that the expected life (the 50% point) is quite close to the maximum of the PDF. The Hazard Function tells us the rate at which the test boards are dropping out.  It increases with time, but there are few boars left so the PDF drops down at the end of the test, even though the fallout rate is the highest.

It is interesting (and perhaps appropriate in the wake of Halloween) to consider if human mortality follows a Weibull distribution. I used some data for the Centers for Disease Control that are a little over 10 years old for males in the US.  So, the mean life expectancy is a little low at 72 years. (I was a little lazy: the old data were a little easier to work with than new data, some conversions are needed to make it work.) The data appear in Figure 2.

As you can see, just like a solder joint, your life expectancy can be modeled quite well by the Weibull distribution.

Cheers,

Dr. Ron