[PART 2] DELMIA Helps the Aerospace Industry Meet the Challenges of Composite Manufacturing

By Christian

Composite wing flap at Airbus
A composite wing flap in Filton, Bristol.
Source: Department for Business, Innovation & Skills, UK.

I’m Christian Chaplais, Senior Manager of R&D DELMIA Enterprise Intelligence Applications. Welcome to the  second blog  of a two-part series on how  Operational Intelligence is helping the Aerospace & Defense Industry.

Finding a Way Around the Complexity of Composite Manufacturing

Many composite parts manufacturers have been exposed to quality issues for years. Some have used classic approaches such as simple statistics, advanced statistics or optimization consulting services to find an answer, but came up short.

So, the question remains. How do you solve composite manufacturing issues without going through all the complexities? There is a way to discover and apply an empirical (data-based) model without the complications in just a few weeks: Operations Intelligence (O/I).

With (O/I) Process Rules Discovery, for example, quality engineers or process and product experts can discover patterns (or rules) explaining whether results have been satisfactory or not. This can be done with a limited number of observations, which keeps down costs, in a ramp-up study.

Process and product experts can also understand the model with Process Rules Discovery, change it by editing the rules and immediately see the impact on the rule KPIs (Key Performance Indicators) based on facts (data).

Here’s a sample rule discovered by Process Rules Discovery:

sample rule

The rule can be interpreted as:

When the product is in the autoclave for an extended period of time (cure cycle time is high)…
…and the binding strength of the fiber is low,
…and fibers have been aging sufficiently,
then the quality is good.

Let’s take another O/I example. With Operations Advisor, shop floor workers can assess risk and take preventive or corrective action in real-time. Operations Advisor recommends values for actionable parameters (settings) without requiring any change to the process specifications or investment in new material.

operations advisor

[Operations Advisor risk assessment and proposed settings ranges (in green)]

Adopting Operations Intelligence

The DELMIA Operations Intelligence solution for Composites has been widely adopted by the Aerospace & Defense Industry from both OEMs and tier-one suppliers.

For several years, one company has been faced with an important and repetitive nonconformance issue (delamination) on the composite leading edge of wings for an aircraft manufacturer. On this family of products, the reject rate could reach 13% and the rework rate 28%. There were delays (up to 6 months of manufacturing backlog), extra internal costs, a loss of confidence from the customer and internal frustration. Multiple quality tasks including process audits, investigating new processes, SPC analysis, inspections of raw material, etc. did not solve the problem.

They then decided to use Operations Intelligence to analyze two years of production. In less than six weeks, two influent parameters, unsuspected until now, were identified (the fluidity of the resin and the time during which the part is kept under vacuum), as well as the recommended lower and higher limits for these parameters. By applying the rules discovered, they managed to instantly reduce the scrap rate to zero and the rework rate to 1%, removing any backlog shortly after.

I’d like to hear your experiences with Composite Manufacturing? What was the outcome?

Continue the technical conversation. Join the DELMIA Enterprise Intelligence Community: https://swym.3ds.com/#community:453

 

A Sprinkling of Smart Dust

By Catherine

Written by Catherine Bolgar

6-18-2015 12-53-22 PM

The “Internet of Things” allows industrial companies to tune or monitor equipment and processes with sensors. But how can these be best positioned and remain connected? One solution is to use smart dust, deployed in oil refineries, industrial process automation, breweries, waste-water treatment and elsewhere.

Smart dust was conceived in the mid 1990s by Kristofer S.J. Pister, now professor of electrical engineering and computer sciences at the University of California, Berkeley. He wanted to  create tiny, diffuse wireless sensors that could be used for product tracking, temperature monitors, defense surveillance and other such applications.

By 2001, Dr. Pister’s team had developed an autonomous sensor the size of a grain of rice, containing five solar power supplies. “It was completely useless for anybody trying to do anything practical,” he says.

But with growing industry interest in smart dust, he founded a company, Dust Networks (acquired later by Linear Technology), to work on possible industrial applications.

When we got out of the university setting, we learned that size wasn’t particularly important, but power and reliability are,” Dr. Pister says.

The sensors had to be so efficient that their batteries would be able to last a decade, and communications technology had to function in harsh industrial conditions.

“We found that as we made these things better, the package gets smaller,” Dr. Pister says. The Dust sensors are now about the size of a sugar cube—too big to inhale, as some had feared—and are fixed in precise spots, so  “they aren’t thrown willy-nilly in the breeze,” he notes. “They aren’t going to wander.”

The sensing work itself—detection and measurement of temperature, flow rates, vibrations etc.—doesn’t require much power, but communicating it does. As a result, Dr. Pister’s team focuses on extremely low-power radios, turning them on only at the right time (down to the microsecond) either on a schedule or when the sensors have information to report.

To further save energy and improve reliability, the sensors are wirelessly connected in a mesh for efficient batch networking, communicating among one another to send data in short hops that don’t require much energy.

“The n6-18-2015 12-59-54 PMetworking aspect is similar to what goes on in the Internet,” Dr. Pister says. “It’s [about] how to build a reliable network when individual paths may be unreliable. We had to come up with different optimum solutions than what people are used to in radio.”

Typically, a sensor system has many low-power transmitters and one really good receiver, in what is called a “star-connected network.” However, in an industrial setting, the physics of a star network doesn’t work well. “The nature of radio frequency propagation and thermal noise in receivers mean there is some tradeoff,” Dr. Pister says. “If anything changes in the environment, it interferes.”

Sound travels like ripples when a stone is dropped into a pond, he says. When a ripple hits a wall, it reflects back and makes patterns. “If you’re a radio, you don’t want those patterns. You get destructive interference,” Dr. Pister explains. Industrial settings are full of such radio-reflecting walls.

That’s where the mesh really shines,” Dr. Pister says. You don’t need any one particular length between devices. Everybody can talk to everybody as long as [a] path is available. Packets [of data] keep going through, no matter what happens in the environment.”

The technology continues to improve and new applications continue to arise. “It’s not out of the question that 10 years from now there’s a whole new deployment,” he says. “The interesting thing, especially in the industrial process space, is that standards tend to last a long time.”

The first Dust sensors, deployed in 2007, are still working with their original batteries. While new generations may use less power, they operate with the same protocol, so companies don’t have to replace older models when a new one comes out. Linear Technology continues to sell products launched 30 years ago.

The quest to reduce size and energy use continues. One research focus is “energy scavenging,” whereby sensors can be powered by vibrations from the equipment they’re monitoring, or by differences in ambient temperature. “It’s not science fiction,” Dr. Pister says. “We have customers who integrated our wireless sensors that are running off solar power or temperature differences in a refinery. It’s part of the future of infinite-life products.”

Catherine Bolgar is a former managing editor of The Wall Street Journal Europe. For more from Catherine Bolgar, contributors from the Economist Intelligence Unit along with industry experts, join the Future Realities discussion.

Photos courtesy of iStock

‘Just in sequence’ takes ‘just in time’ a step further

By Catherine

Written by Catherine Bolgar

Modern car production line

When Toyota Motor Corp. introduced just-in-time (JIT) manufacturing, it transformed the automotive sector and changed how the business world viewed production and supply chains. Toyota wanted the right quantity and quality of parts to arrive at the factory floor at the right time, allowing the firm to shrink its expensive inventories.

The auto industry continues to innovate. As cars become increasingly customized, the sector is once again leading change, with “just-in-sequence” (JIS) production. Carmakers want to ensure not just that they have enough doors at the right time, but enough blue, red or any other color doors precisely when they need them. If the plant is making a blue, a white, a black and a red car in that order, it has to know that blue, white, black, and red doors will also arrive in that sequence.

Just in sequence “is a very hot topic in the automotive industry,” says Nils Boysen, professor of operations management at Friedrich-Schiller-Universität Jena, in Jena, Germany. A car rolls off the assembly line every 60 to 90 seconds, for which any one of more than a thousand varieties of door trim or seat, and hundreds of different center-consoles, might be needed. JIS allows assembly workers to get the right item quickly, without having to sort through large, cumbersome parts. Given that auto workers are generally well-paid, carmakers have an interest in improving workflow efficiency. It’s a little like Charlie Chaplin’s film ‘Modern Times,’ Dr. Boysen says. “It’s pretty fast and exhausting if you have to take a door every 90 seconds again and again.”

JIS isn’t practical for all manufacturers. Aircraft makers, which also require large parts, operate at a different pace—typically producing a plane every 2.5 days—and many parts, such as passenger seats, come in large, standardized quantities. At the other end of the spectrum, electronics factories require large quantities of small but expensive parts, which workers can easily handle multiple times, says Dr. Boysen. Even in the auto sector, not all parts are sequenced: mirrors, for example, are too small to be worth the trouble.

Just in sequence has a lot of prerequisites,” Dr. Boysen notes. “The parts and products must be assembled very often and in huge variety.”

 

iStock_000003309143_SmallWith JIS, the cost of getting parts in the correct sequence shifts from the factory worker to the supplier. With JIT, companies reduce warehousing costs by leaving inventory with suppliers, who are then expected to deliver them at the right time. Companies outside the auto sector have adopted what is effectively a less-complex version of JIS. Certain clothing retailers, for instance, ship clothes with price tags and hangers attached, so shop staff don’t have to iron, hang and tag them. The main benefit of this is not about saving the time of salespeople, whose wages are generally not high, but in displaying the clothes more quickly for loyal customers who want first pick of the new arrivals.

Companies shift costs to suppliers in other ways besides JIT and JIS. Some manufacturers and retailers deploy vendor-managed inventory, providing point-of-sale data to vendors who ensure that warehouses or shelves are efficiently stocked. Other companies are shifting research-and-development operations to their suppliers, so that those suppliers can become more innovative.

Factory manufactoring transmissions

Although JIS is mainly about cost-cutting, even the auto industry isn’t entirely convinced that the returns justify the investment. “It’s hard to measure the effects of how much does it cost and how much does it bring you,” says Dr. Boysen. The automakers “plan their sequence and order some parts, like engines, in JIS, then they receive these parts in the planned sequence, but their production processes aren’t that reliable. There are always problems with the paint shop. When a sensor detects a paint defect, the car has to be repainted. So they don’t make sequence as planned. They have to reorder or resequence.”

In a factory producing several thousand cars a day, the buffer inventory can take up a lot of space and cost tens of thousands of euros, Dr. Boysen points out. Even the world’s largest car companies struggle with “huge space problems,” he notes.

 

Catherine Bolgar is a former managing editor of The Wall Street Journal Europe. For more from Catherine Bolgar, contributors from the Economist Intelligence Unit along with industry experts, join the Future Realities discussion.

 

Photos courtesy of iStock



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