Chris Hardee’s picture

By: Chris Hardee

As moviegoers, we have all seen a wide range of animation—from early Disney features, such as “Snow White,” to Japanese anime, and Pixar’s “Toy Story,” to an assortment of recent blockbusters that seamlessly integrate animation with real actors. With each release, the movie magic gets more amazing as animated characters such as the Incredible Hulk or Gollum in “The Lord of the Rings” take on lifelike qualities and realistic human facial expressions. How in the world do filmmakers do it?

As animators know all too well, the human face is one of the most difficult objects to realistically model. A flexible layer of skin covers a complex array of muscles and bones, producing a seemingly endless number of subtle facial expressions that are an important component of our communication system. Technology that allows blending live-action with special effects has pushed the animation field into realms hardly imagined just a few years ago, as animators use computer-based physics in much the same way that design engineers use realistic simulation.

Fluke Corp.’s picture

By: Fluke Corp.

I

n process manufacturing, temperature uniformity is essential. Technicians rely on monitoring of all kinds, from fixed mount sensors to hand-held thermal imagers to track the condition of product and critical equipment. That’s because temperature measurement and control is one of the single most significant variables for uniformity across process industries.

Temperature monitoring can detect overheating delivery system components, help solve irregularities in electrical power supplies, predict operational machinery failure, detect blockages in supply pipes, and identify product inconsistencies.

Given the number of process industries and associated equipment variations, the possibilities for thermal monitoring are endless. One approach is to provide the most monitoring to critical assets, followed by equipment in harsh environments. For example, the sludge, solvents, and particulates found in many processes put extra stress on motors and affect bearings, windings, and insulation.

That stress shows up as heat detectable by a thermal imager.

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By: Lonnie Wilson

Five things to lean my company? In two days? That's pretty quick. Why so quickly?  

I can think of two reasons. First, people expect things to be done better and faster each day; and it appears we’re getting decent at doing just that. Lean has made huge strides toward contributing to that “better, faster” concept. Second, I always emphasize making large early gains. It is interesting that this concept of “early gains” isn’t typically pushed by many lean practitioners. Most are quite content to sell lean as “a journey” and leave it at that. A journey it is; no doubt about that. However, it is awfully convenient, and quite frankly suspicious, that they leave out the early gains issue, because those gains are there. So why exclude this low hanging fruit? Because to include it would put more pressure on them to produce today, and without that pressure, consulting is just a little easier—not better, just easier. Since because people want it, and because “early gains” is one of my mantra, I decided I should be responsive to the request. Here goes. 

FARO’s picture

By: FARO

Rocky Mountain Hydro Electric Plant is a pumped storage facility located in the Appalachian mountains of Northwest Georgia, approximately 62 miles from Chattanooga, Tennessee. The facility is co-owned by Oglethorpe Power Corporation (75%) and Georgia Power (25%), with all operations and maintenance controlled by Oglethorpe. Oglethorpe is the nation’s largest power supply cooperative and provides electricity to 4.1 million Georgia citizens.

Mark Graban’s picture

By: Mark Graban

A couple of Sundays ago, I read this New York Times article about Apple's "App Store" for the iPhone and iPod Touch (I've been a pretty happy iPhone user for the past three months after switching over from BlackBerry).

I'm going to try to use this example to teach about two concepts that can be used in virtually any process—takt time and cycle time—including some questions for health care.

Some details of Apple operations came out through filings made in response to the controversy over Apple not carrying the "Google Voice" app and a Federal Communications Commission investigation. The article unveils:

Neil McLeod’s default image

By: Neil McLeod

If ever there was an industry in which time compression is the name of the game, it’s Formula 1 Grand Prix motor racing.

Scuderia Toro Rosso, owned by the Red Bull Co., is among the Formula 1 teams looking for new and better ways to compress development and production times, while increasing the reliability of its racing cars.

One advantage Scuderia Toro Rosso has over the competition is Geomagic Qualify 3-D inspection software, used at the company’s headquarters in Faenza, Italy. Geomagic Qualify has reduced the time required to inspect new parts by an average of 30 percent. It has also given Scuderia Toro Rosso the ability to inspect parts that previously could not adequately be inspected within the demanding time frames of Formula 1 racing.

The software enables fast, easy-to-understand graphical comparisons between 3-D CAD models and as-built parts, or between parts from different production runs. It saves time and increases accuracy for first-article and in-process inspection and enables trend analysis, 2-D and 3-D dimensioning, geometric dimensioning and tolerancing (GD&T), and automated reporting in a variety of formats, including Microsoft Word, Microsoft Excel, PDF, and VRML/HTML.

Quality Digest’s picture

By: Quality Digest

Developing and refining the advanced processes for its manufacturing and assembly operation is no small task for Irvine, California-based Coast Composites Inc. The company makes large, precise tooling for the aerospace industry. The scale and accuracy requirements of their product creates quality control issues that were addressed using a combination of FARO Laser Tracker and Verisurf software.

“Some of the high-precision tooling we produce for customers is really huge, up to 100 feet long," says Steve Anthony, Coast Composites IT manager. Size creates its own set of challenges and opportunities, and has also led Coast to its recent plant expansion from 85,000 square feet to more than 200,000 square feet. “When you build huge tooling you need lots of room,” Anthony explains.

Even though the molds it produces are extremely large (typically 2- to 60-feet long), Coast still must meet precise tolerance requirements to satisfy its customers’ specifications. “We’re talking fine finishes and tolerances of four to five thousandths over a 50- to 60-foot length,” Anthony explains. “And, like any other QC requirement, we have to be able to prove to our customers that we have met or exceeded their specs. To do that has taken us into some interesting new technologies.”

Thomas J. Duesterberg Ph.D.’s default image

By: Thomas J. Duesterberg Ph.D.

In the worst economic climate since the 1930s, and at a time of intensified political change, manufacturers are experiencing difficulties in articulating a clear and strong message about the health of their sector and how policy change might affect it. What follows are 10 summary points intended to convey an accurate picture regarding the current state of U.S. manufacturing and some of its key issues. These talking points barely scratch the surface; suggestions are provided for further reading on some crucial points1.

1. Despite perceptions that U.S. manufacturing is disappearing, the quantity of manufactured goods produced in the United States has kept pace with overall economic growth for the last 90 years. Since 1947, value added in manufacturing has grown sevenfold, the same as gross domestic product (GDP). While employment has steadily declined in the sector, one in six private sector jobs are still in or directly tied to manufacturing.

Minitab LLC’s picture

By: Minitab LLC

Story update 8/27/2009: An error was spotted and corrected by author in paragraph starting with "The population mean for a six-sided die..."


Mark Twain famously quipped that there were three ways to avoid telling the truth: lies, damned lies, and statistics. The joke works because statistics frequently seem like a black box—it can be difficult to understand how statistical theorems make it possible to draw conclusions from data that, on their face, defy easy analysis.

But because data analysis plays a critical role in everything from jet engine reliability to determining the shows we see on television, it’s important to acquire at least a basic understanding of statistics. One of the most important concepts to understand is the central limit theorem.

In this article, we will explain the central limit theorem and show how to demonstrate it using common examples, including the roll of a die and the birthdays of Major League Baseball players.

Defining the central limit theorem

A typical textbook definition of the central limit theorem goes something like this:

Multiple Authors
By: Michelle Paret, Eston Martz

Story update 8/27/2009: An error was spotted and corrected by author in paragraph starting with "The population mean for a six-sided die..."

Mark Twain famously quipped that there were three ways to avoid telling the truth: lies, damned lies, and statistics. The joke works because statistics frequently seem like a black box—it can be difficult to understand how statistical theorems make it possible to draw conclusions from data that, on their face, defy easy analysis.

But because data analysis plays a critical role in everything from jet engine reliability to determining the shows we see on television, it’s important to acquire at least a basic understanding of statistics. One of the most important concepts to understand is the central limit theorem.

In this article, we will explain the central limit theorem and show how to demonstrate it using common examples, including the roll of a die and the birthdays of Major League Baseball players.

Defining the central limit theorem

A typical textbook definition of the central limit theorem goes something like this:

As the sample size increases, the sampling distribution of the mean, X-bar, can be approximated by a normal distribution with mean µ and standard deviation σ/√n where:

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