The "S" word, Part I

Social impacts in sustainability

At the end of our last posting … were starting to make a connection between resiliency and some of the societal dimensions of sustainability. As we start looking into some of the less technical aspects, like consumer response/acceptance, we get into these more esoteric aspects of green and sustainable manufacturing. Our next topic - societal dimensions of sustainable design and manufacturing - the other "S" word.

To the extent that larger civil systems are involved in manufacturing supply chains or labor responsiveness, enhancing manufacturing resilience to disruptions and disasters is not a purely technical problem, but involves societal dimensions.

In perusing my latest copy of Fortune magazine I noticed an article under a discussion on "What will the Global 500 look like in 2021" (the Global 500 are the top 500 companies internationally.) The article stated that "scarcity will be the new normal" and claimed that "three billion new people will join the global middle class in the next two decades. The resulting consumption boom will drive natural-resource prices higher, opening space for companies that learn to use resources more efficiently." You can find this online at the CNNMoney site.  Their angle is, of course, that this will offer opportunities for companies in businesses like "reducing food waste, deploying efficient irrigation systems, and improving the energy efficiency of buildings."

And green manufacturing and supply chains?  In addition to food and shelter this global middle class is going to be clamoring for all the usual ornaments of that new status - refrigerators, automobiles, televisions, etc. etc.

Recall the IPAT equation I've been bandying about in several blogs and first introduced back in the September 2009 posting? The basic impact equation (or IPAT, in terms of environmental damage, consumption, etc.) which is simply:

  Impact = Population x (GDP/person) x (Impact/GDP)

(and hence the acronym IPAT: I = P x A x T or  Impact = Population x Affluence x Technology)

I commented then that population grows with time and most countries strive to improve GDP/capita since that drives living standards, etc. The rate of consumption or environmental impact per unit of GDP is the "rate of damage" done as a result of the technology driving the growth in GDP and is really the only "knob" we can adjust to reduce impact.

I noted that engineers are most effective at changing technology that affects Impact/GDP. To the extent we can reduce that impact we are, effectively, greening the process. And that means we are reducing the impact.

The "business opportunities" in the Fortune article cited above are addressing this also … but for food production and shelter effectiveness per unit of energy consumed.

I also noted in a later blog that not everyone agrees that affluence is a good measure of well-being nor that the GDP/capita is a good measure of either! However, I assume the folks at Fortune would be comfortable with this definition.

In a later posting we started to wade into motivated by a short discussion about "energy of labor." In that post, the "triple bottom line" of sustainability - economic, societal and environmental was elaborated on.

You'll recall that the "triple bottom line" term was apparently originally mentioned by John Elkington in 1994 (he called it the 3-P's: profit, people and planet) and the "people" part referred to "a measure in some shape or form of how socially responsible an organization has been throughout its operations." Because what you measure is what you are likely to pay attention to, one needs to think carefully about what metrics actually capture the social impact. And, it is only when companies actually and correctly measure their social and environmental impact will we see more socially and environmentally responsible organizations.

The accurately measure part is the tricky bit - specially for social impact.

There is the concept of Gross National Happiness (also discussed in the energy of labor posting) that was comprised by a number of logical components as:

1. Economic Wellness: economic metrics such as consumer debt, average income to consumer price index ratio and income distribution
2. Environmental Wellness: environmental metrics such as pollution, noise and traffic
3. Physical Wellness:physical health metrics
4. Mental Wellness: mental health metrics such as usage of antidepressants and rise or decline of psychotherapy patients
5. Workplace Wellness: labor metrics such as jobless claims, job change, workplace complaints, etc.
6. Social Wellness: discrimination, safety, divorce rates, complaints of domestic conflicts and family lawsuits, public lawsuits, crime rates
7. Political Wellness: political metrics such as the quality of local democracy, individual freedom, and foreign conflicts

How to characterize these in a practicable way is always the challenge. And, then, how to connect them to some aspect of the design, production, distribution, operation and end of life of our products or systems is even a bit more challenging.

Companies are trying already to become more socially and environmentally responsible organizations. Take Walmart for example. An excellent article on GreenBiz back by Marc Gunther in April of this year titled "How much of a difference can Walmart really make?" goes into some detail on the activities, and impact of those, on sustainability of one of the world's largest companies (it just got bounced out of the top position, based on sales, by two oil companies!) Based on a careful read and analysis of their recent sustainability report some of Walmart's highlights listed are:

- Reduced waste by 80 percent
- Expanded locally grown produce (up by 97 percent)
- Pledged to source $20 billion from women-owned businesses in the U.S.
- Saved customers $1 billion on fresh fruits and vegetables
- Announced a “Great for you” icon that will help shoppers identify healthier food items.

The article goes on to say that with respect to waste "Walmart’s doing very well, largely because eliminating waste makes business sense. As the new report explains: In 2011, Walmart U.S. prevented 80.9 percent of the waste generated by its stores, clubs and distribution centers nationwide from going to the landfill. This has the potential to prevent more than 11.8 million metric tons of CO2 emissions annually, the equivalent of taking more than 2 million cars off the road. The zero landfill waste program returned more than $231 million to the business last year through a combination of increased recycling revenue and decreased expenses."

Henry Ford would have been pleased. Recall that he said that waste costs you twice … once when you buy the original product or material and the second time when you pay to get rid of the left overs.

According to the "happiness" list above some of these clearly have social impacts.

So, the question remains - what are the best (or at least most practicable) social measures and how do we link them to manufacturing (either process improvements for reduction of impact or leveraging for product improvement) and product design?

And, what about trying to influence consumer behavior? Where does that fit in? It doesn't necessarily mean less profitability! (I recall this as I sip my $3.50 latte from Peet's Coffee!)

We intend to explore all this in the future postings in this series.

Axes of Resiliency

Response, recovery, regeneration

We continue here our discussion on "resiliency" and how it relates to green and sustainable manufacturing. Recall that we started with a standard dictionary definition of resiliency as the capability of a body under strain to recover its original size and shape after some external disturbance or deformation. It also listed the ability to recover from or "adjust to misfortune or change."

Engineers think of the first definition in terms of a "rubber band" which can be stretched and then, when released, returns to its original shape. This is certainly a recovery from change as well. I also believe this includes "inoculation" to disruption and risk - the rubber band is designed to recover.

In the last posting we ventured into the muddy waters of "equilibrium state" of a manufacturing process or system.  The idea was that resilience refers to the ability of an engineering system to return to equilibrium. But, I don't want to confuse equilibrium in the sense of mechanical equilibrium we learned in our early physics course. There we said that equilibrium was the state in which the sum of the forces, and torque, on each particle or element of the system is zero or thermal equilibrium wherein there is no exchange of energy between an object and the surrounds - meaning everything is at the same temperature.

I inferred that, here, equilibrium was essentially a stable operable state that the system returns to following a disruption that would tend to move the system into another state of operation - presumably less stable, or less profitable, or less environmentally benign.

So, what are the various dimensions (or axes) of resiliency?

We can think about measures of responsiveness, recovery and regeneration for starters. Returning to the information from NIST on resilience (specifically National Institute of Standards and Technology (NIST), 2008, “Strategic Plan for the National Earthquake Hazards Reduction Program: Fiscal Years 2009-2013”) one might argue that resilience entails three interrelated dimensions: reduced failure probabilities; reduced negative consequences when failure does occur; and reduced time required to recover.

So, how do these relate to green or sustainable manufacturing? To what extent can elements of manufacturing, as practiced, be implemented to reduce the likelihood of failure, minimize negative consequences when some disruption or failure occurs and, finally, minimize the time to recover (that is, get back to "equilibrium")?

These are normally topics covered in more conventional manufacturing business practices and system management - mean time to failure and mean time to repair, redundancy, etc.

One might start out with the three elements of sustainable manufacturing - materials, energy and technology. We've described in earlier postings the basics of green at a process level (see for example the diving deeper discussions)  but we can also think about the interplay of these three "elements".

The figure below, from a presentation in our lab in 2009 by Professor Chris Yingchun Yuan of UW-Milwaukee (he was a student back in in LMAS then and this was part of the research going into his

PhD thesis) illustrates this interplay well. It shows how the reduction of consumption of either materials or energy or the improvement of efficiency of converting or using materials, or cleaner energy sources or alternative materials or processing technology all work towards greening manufacturing (any one of these trajectories would be a worthy "technology wedge" as we've used it here.) Better use of lower impact materials with no deleterious side effects converted with optimal yield into a product with minimal energy use and that from renewable sources all done in a cost-effective manner - that's the ticket!

Ok, that's not so simple - but that is not our point here. The point is to add on the aspect of resilience to this picture.

If we look at the drivers for resilience, for example:

  - risk and risk reduction
  - time and schedules/availability
  - cost
  - responsiveness
  - competitiveness
  - consumer reaction/acceptance
  - responsiveness to markets and suppliers
  - regulatory compliance
  - etc.

it is an impressive list. In fact, it includes most of what we listed when the blog was started in the posting on "Why Green Manufacturing?" Missing in that list (except for a maintaining competitiveness angle) was the time factor. Resilience includes time.

So, take these three elements from the triangle and ask - "how do the drivers listed above affect these?"

We are not going to go through all the combinations but a few obvious ones come to mind. For example, cost. Maintaining the ability to control costs in the face of uncertainty is a fundamental tenet of manufacturing. It can be accomplished by being able to boost productivity (output per unit of labor) so that wild swings in exchange rates don't drive you out of the market because of prices. Consider Japanese manufacturers who were, at one time, manufacturing products with the Yen at 120 to the Dollar. Now it is closer to 80 Yen to the Dollar. That means my costs (in dollars) for the same product are increased by 50% with no appreciable change in the product. That means I have to be able to be that much more productive just to stay even. The Japanese have excelled at creating production systems that can increase productivity to accommodate swings in exchange rate. That's resilience!

Now think about energy and the "cost" in terms of energy needed to produce a product. You can track energy pries like exchange rates. This gives you the required improvement in "energy productivity" required for making a product to keep ahead of that variation. That's another form of resilience.

Risk is a bit trickier but follows the same general thread of logic. Reducing risk (and hence enhancing resilience) can be done by using less (and hence reducing demand) by redesign of process or improvement of yield  in material conversion, finding alternatives (materials or technology) or, less effective, redundant supplies.

This strategy certainly leads to reduced failure probabilities; reduced negative consequences when failure does occur; and reduced time required to recover. Mostly by "inoculating" the system against failures.

Once we start looking into less technical aspects like consumer response/acceptance we get into the more esoteric aspects of green and sustainable. This is a great segue (note: that's "seg-way" … but not the two wheeled scooter!) into our next topic - societal dimensions of sustainable design and manufacturing.

To the extent that larger civil systems are involved in manufacturing supply chains or labor responsiveness, enhancing manufacturing resilience to disruptions and disasters is not a purely technical problem, but involves societal dimensions.

We'll pick that up next time.

Green Manufacturing and Resiliency

What's resilience?

This week the discussion is on "resiliency". And, how it relates to manufacturing and, in particular, green and sustainable manufacturing.

But first, a final comment on leveraging (the subject of the last three posts). In a discussion about leveraging with some of my researchers last week it was suggested that, in fact, leveraging works in both directions - from manufacturing towards the product and from manufacturing back to material selection. We'd been discussing the "forward" direction with respect to changes in the manufacturing process that may require some investment of resources (or energy, materials, etc.) but which will yield a substantially larger reduction in life cycle impact of the product in use and, hence, a good 'return on the investment.'

The "backward" look is equally sensible but I don't have an immediate example in mind but, when I do, it will be the subject of another posting. Here, we can make decisions in the product design or manufacturing that influences material selection. For example, we can choose to use a production technology that is, perhaps, more energy intensive but allows us to choose from a wider range of materials including some that are less energy intensive to produce (lower embedded energy), less hazardous or better for operation of the product to reduce impact.

That is, we can mirror leveraging in both directions about the manufacturing process. And, interestingly, this could make our systems more reliable and resistant to disruption due to, say, materials shortages or other disruptions due to impacts.

This is a great lead in to our discussion here - resiliency.

The dictionary (Merriam-Webster on-line) defines resiliency as "1: the capability of a strained body to recover its size and shape after deformation caused especially by compressive stress or 2: an ability to recover from or adjust easily to misfortune or change" (and they give the example of "emotional resiliency"). The second definition is probably closest to what interests us here - recovering from unexpected or unwanted change or misfortune. Think supply chain disruption due to, for example, floods in Thailand or earthquakes in Japan.

Actually, we can characterize these disruptions in terms of our ability to foresee or predict the disruption or plan for it. Things like earthquakes are unpredictable. You can choose not to build your factory in an earthquake zone (but some choose not to worry about that if you can build the structure "resiliently"). You can't always predict or anticipate other system stressors like labor disruptions, mineral or material shortages, equipment malfunction, etc. But you can try to take steps to reduce the impact (or inoculate your system from their effects). Planning, redundancy, alternate sources, careful choice of components/suppliers/sources, etc. all can help.

If you "Google" the term 'manufacturing resiliency' you will get a number of postings and articles dealing with reducing downtime due to disasters and other unanticipated events that result in reduced employee productivity, revenue loss, damaged corporate reputation and missed service levels. These "unanticipated events" can be caused by power outages, natural disasters, or other disruptions to a manufacturers’ supply chains and critical material or part suppliers.

Of course, many suggest that IT is the solution … more information faster means fewer surprises. Maybe.

Others suggest that a cause of concern is the volatility of prices in the materials/metals markets. A recent article by consultants KMPG titled "Global Metals Outlook: Manufacturing Resilience" discusses this in some detail. These are not manufacturers - but metals processors and suppliers - the folks that provide materials to manufacturers. Logically, their strategies include cost optimization, trying to gain more control over raw materials and, interestingly, locating assets closer to customers or suppliers. The report states "More than one-half (53 percent) of respondents from metals companies say their organizations are considering localizing or customizing operations to improve the efficiency of their supply
chain, compared with 43 percent of manufacturing companies more widely. Given the size and bulk of their products, shipping costs are a major concern."

Interestingly, the report did not mention anything about helping their customers make better use (increased yield) from materials or lengthening the product life cycle to better control demand. Honestly, most of the experts interviewed in this report were not the operating engineers but from the financial and management side. So that is not a big surprise. But, that would work!

Back to resilience. An excellent review of "resilience thinking" is in Ecology and Society in a 2010 paper reviewing resilience as part of adaptability and transformability - all key aspects of the dynamics and development of complex social-ecological systems. We're going to dive into social metrics and manufacturing at some time in the future but, for now, keep it close to engineering. From the paper cited above, we see that "Resilience was originally introduced by Holling (1973) as a concept to help understand the capacity of ecosystems with alternative attractors to persist in the original state subject to perturbations… In some fields the term resilience has been technically used in a narrow sense to refer to the return rate to equilibrium upon a perturbation (called engineering resilience by Holling in 1996)."

Hollings wrote a foundational paper on resiliency (the full cite is Holling, CS (1973) Resilience and Stability of Ecological Systems, AnnualReview of Ecology and Systematics, 4:1–23.) In this paper Hollings discussed the difference between engineering resilience and ecological resilience. He considered that the engineering system has one equilibrium state only, while the ecological system has more than one equilibrium state.

So, simply put, resiliency is the ability of a system (say a supply chain or production system) to return to a stable operable state in the presence of "attractors" (or in engineering terms, disruptions) that would tend to move the system into another state of operation - presumably less stable, or less profitable, or less environmentally benign.

It is not too hard to see where risk comes into this and, if the risk is induced by unexpected events (like floods) the resilience of the system will be the ability of the system to return to normalcy with the least disruption. And, with respect to "equilibrium states" it is clear that manufacturing systems may have many (since they have many different components) and it might be preferable to move to a new equilibrium state if it can be shown that it is more green or sustainable!

So, let's draw the conversation back to manufacturing. Equilibrium is a very well understood engineering term and refers to a state of rest or a natural condition that a system will revert to when left alone. In the case of manufacturing, say a production system, equilibrium might be when the system is operating as designed with the requisite result or output. A complex supply chain might be said to be at equilibrium not when it is stopped or doing nothing (as in the engineering definition "state of rest") but when it is functioning smoothly. I realize this is not a precise definition but it will suffice for our discussion of resilience here.

I recently was exposed to the use of resilience with respect to green manufacturing and sustainability in the context of the National Institute of Standards (NIST) use of the term as part of a description of their sustainable manufacturing program. The site explains that "the sustainable manufacturing program will enable advanced manufacturing processes that include new manufacturing methodologies, manufacturing information systems, and effective industry standards. The Program results will advance U.S. leadership in sustainable manufacturing, resulting in technologies that support the application of Key Performance Indicators (KPI’s) to access and decide on production networks which require much less energy and materials, reduced waste and optimal logistics. By using these technologies industries are ideally positioned to optimize their processes and maximize their efficiency and resilience."

Lot's there - methodologies/technologies, information systems, key performance indicators (KPI's), standards - all with the purpose of helping to make decisions on production processes and networks that use less energy and materials, reduced waste and optimal logistics. And, hence, make the processes and networks more resilient!

Let's continue with how that might work in practice next time.

Leveraging Manufacturing, Part 3

The big finish!

That's a pun - gear finishing, leveraging, get it?! OK - blogger's license.

We will finish up our example of leveraging with this post. Although there was a long dead space in postings, recall that the example was from a recent paper from our research group and focussed on  the gear train as used in transportation. The premise was that the surface finish of gears contribute substantially to the efficiency of power transmission. Better surface finish yields better efficiency.

It was described that the gear manufacturing process chain is relatively complex with several options available to the manufacturer at each fabrication stage. In this example it is assumed here that the main process chain would be unchanged and that only gear finishing would need to be altered to produce gears with higher surface finish. For reference, the full citation to the paper on which this series is based is “Evaluating the relationship between use phase environmental impacts and manufacturing process precision,” CIRP Annals, 60, 1, 2011, pp. 49-52. I'll send you a copy if you want one.

The "leveraging" comes in with the expected fuel savings due to the better efficiency of the gear operation due to the better surface finish. We need to determine if the increased consumption of energy in finishing is paid back in the improvement in the operation of the gear train and accompanying reduction in fuel use. And a result of reduced consumption of fuel in the auto use phase we see reduced global warming potential (both from the reduced fuel used and the avoided impact of producing the fuel.)

Using the basic approach outlined in the last post, it was first necessary to determine the 'cost' of manufacturing improvements relative to surface creation. We do this by looking at the specific energy consumption requirements of the grinding process used in this part of the manufacturing process chain. From published data, for example from Professor Tim Gutowski at MIT, we know that the specific energy (meaning the amount of energy to remove a volume of material) for a grinding process assumed to be reflective of standard automotive gear finishing applications is about 200,000 Joules/cm3 for a process with a removal rate of about .01 cm3/sec. So, in English, if you want to remove a cm3 of material at this rate it will "cost" you 200KJ.

Using this approximation and the relationship between surface roughness and removal rate from earlier researchers we are able to estimate the increased specific energy required to decrease the surface roughness of the final gear drive reduction relative to the representative gear finishing process. This  estimate provides an upper bound to the manufacturing energy usage - meaning it should not exceed that since it is a convective estimate. Primary energy (energy needed for either the manufacturing process or moving the automobile) demand for the process and GWP emissions were then determined assuming a Michigan electricity mix (7015.2Btu/kWh and 0.7131kg CO2-eq/kWh, respectively. We assumed we were manufacturing the auto in Michigan.

The figure below shows the increase in PE demand and GWP emissions from electricity usage
in the manufacturing phase due to decreased surface roughness. This means, as we put more

energy into the grinding process to improve the surface roughness (recall, smaller is better in surface roughness) there will be a corresponding increase in global warming potential (GWP). Lower primary energy consumption is better for a given set of process conditions. In the figure we see two curves, one for the least sensitive relationship between process removal rate (x = 0.60) and the other for the most sensitive (x = 0.15). This shows the change (improvement) in surface roughness one can achieve by "spending" process energy - reducing surface roughness from the nominal by 50%, for example, will cost us 1.25MMBTU. (Read the graph as the x-axis at 100% is the typical surface roughness and moving towards 0 indicates reduced roughness or better surface.

Now to the automobile's primary energy consumption based on gear train efficiency. The fuel consumption of a vehicle is dependent on the power that the powertrain must deliver to meet the commanded acceleration while powering any accessories (e.g. air conditioning) and overcoming losses in the drivetrain and engine. Because this analysis considered only changes to the drivetrain efficiency, the power required for any accessories and frictional losses in the engine were neglected since neither would be affected.

The U.S. EPA Federal Test Procedure 75 (or FTP-75) emissions driving cycle was used to represent a standard driving scenario for this analysis. The decrease in fuel requirements was calculated for each surface roughness, Rq, of the gear pair in the final drive reduction. The resulting decrease in energy that must be provided by the fuel was then determined by integrating the decrease in fuel power. All deceleration events were removed from this calculation since a deceleration event does not require power from the engine. Modern engines are operated to fully combust fuel, and so the PE demand and GWP emissions were determined assuming that the fuel source was regular, unleaded gasoline (1184.8Btu/MJ used fuel and 0.0948kg CO2-eq/MJ used fuel, respectively.

The figure below details the relationship between the surface finish (stated in Rq, microns) of the gears in the final automotive drive reduction and the reduction in automotive primary energy demand (gas!) and the comparable reduction in global warming potential.

This figure shows that decreasing surface roughness (Rq) lowers PE demand relative to a standard finished final drive reduction from 2-5MMBtu depending on the operating temperature, To. The earlier figure showed that a 20-60% reduction in roughness increases PE demand in the manufacturing phase by less than 0.5MMBtu. Comparing these analyses indicates that improving the manufacturing precision of the final drive reduction can provide a substantial reduction in the life cycle impacts of an automobile. Since the final drive reduction is one of several gear pairs in a vehicle, the impact of manufacturing precision on the entire vehicle drivetrain could be much greater.

This analysis showed that a relationship exists between the manufactured precision of a product and its environmental impacts over its entire life cycle. In the case of automotive drivetrain components, this relationship was found to be positive. However, it may not be true for every product and is largely dependent on the intended function of the product. Ultimately, if a manufacturer is concerned with environmental impact when considering a process or system design, then he should improve the manufacturing precision if the resources required for the improvement are less than the potential benefit of the improvement in the use phase of the manufactured product.

This is summarized in the figure below. The figure plots surface roughness (to the right is rougher) and the comparable primary energy demand difference between the use (auto operation) and manufacture (creating the surface by grinding).

You can see that, for the standard gear finishing operation at the right we set the difference (cost minus savings) at 0. Then, according to our analysis improved surface roughness, even though it costs something in the manufacturing phase, yields a good return (savings greater than cost - so negative delta) over a wide range of surface roughness (and corresponding process conditions). If we push it too far and try to get too fine a surface finish (going far to the left in the plot), the manufacturing energy needed outweighs the benefits in improved performance - it costs too much to do. The trick is, first, finding the relationship the allows us to define this curve and, second, determining when the lower limit is reached and it is no longer "environmentally profitable" to improve the process further.

Clearly there are things, some more important than others, that we are leaving out of this analysis. But, it is a pretty good, and accurate, example of leveraging. For example, we should measure other aspects of gear finishing processes so we can include other environmental impacts such as water, industrial fluid, and raw material usage. We might also consider other manufacturing and product effects such as increased or altered process consumables for the manufacturing process, will this more aggressive finishing process result in decreased process yield (that is, more rejects) and what impact does this change have on the service life of product. These could be additional benefits as well as offer some disadvantages.

I encourage you to read the paper if you want the full details. There is tremendous potential in this approach.

Finally, we just hosted at Berkeley the 19th CIRP Life Cycle Engineering Conference. We had almost 180 participants from all over the world and it was a great series of presentations and discussions on many aspect of life cycle engineering as it applies to manufacturing. The "theme" of the conference was "Leveraging Technology for a Sustainable World." You can read a short overview of the conference in a blog posting on the BERC blog space prepared by one of our lab members, Katie McKinstry. Katie's posting is titled: GLOBAL ENGINEERING CONFERENCE SHOWCASES SUSTAINABLE MANUFACTURING INITIATIVES 28 May 2012 | BERC News. Enjoy!

Leveraging Manufacturing, Part 2

Some details on processing

The last posting began to dig into the leveraging discussion and started to elaborate on this topic using an example. The example was  from a recent paper from our research group at Berkeley and focussed on an important aspect of vehicles and transportation - the gear train.

The efficiency of gear systems was described as deriving from a variety of factors including the surface roughness of the mating surfaces.  Other studies have shown an even greater dependence on the surface roughness of the mating surfaces for hypoid gear pairs, which are found in automotive differentials. And because the vast majority of environmental impacts of an automobile occur during the use phase  the impact of increased manufacturing precision through better surface finish on the final drive reduction of an automotive manual transmission drivetrain makes this an ideal example of leveraging.

The gear manufacturing process chain is relatively complex with several options available to the manufacturer at each fabrication stage. It is assumed here that the main process chain would be unchanged and that only gear finishing would need to be altered to produce gears with higher surface finish.

It might be helpful to digress a bit (I enjoy digressing!) to talk about this important manufacturing process that underlies the efficient operation of most machines and transportation. Gear finishing, essentially abrasive machining, is one of those seemingly small and innocuous steps in manufacturing that "gets no respect" (to quote Rodney Dangerfield). In the world of machining with hard tooling (meaning not using lasers or some other type flow process) cutting processes are categorized by the geometry of the tool used and according to whether or not the tool is stationary relative to the workpiece or if the tool rotates. There is a logical division of these processes that, at the highest level, distinguishes between cutting tools that have a “defined geometry” (meaning specific dimensions that determine the shape of the tool) and cutting tools that are not defined (meaning the shape is more random)—having an “undefined geometry.”

Grinding uses undefined geometries - that is, the "tool" or abrasive doing the cutting does not have defined edges at all. Abrasive processes (grinding, sanding, polishing, etc.) use abrasive particles that are natural materials like sand, aluminum oxide, and so on, or materials made to appear as natural shapes. In typical grinding operations several abrasive grains (usually referred to as “grits”) are held together by a bonding material, as they would be in a grinding wheel or for abrasive (sand) paper. The shape of the grains is not defined but “random” depending on how the grain of abrasive was crushed to get the desirable size. If you've every sanded wood or other material or used an emory board on your finger nails you've been abrasively machining.

Importantly, as you observed when standing and noted that small grits gave you a better surface finish (meaning smoother or lower roughness), we control the desired process output by controlling the grain size and the way it moves through the material surface during grinding. Chip formation with an abrasive grain is illustrated in the figure below and shows the grit displacing/removing

workpiece material. The "v" is the velocity of the grit over the work and the arrow shows the relative movement between the grit and the work - the speed and direction. In grinding there would be hundreds of thousands of grits coming into contact with the work and each grit removing a small chip of material.

Thanks to many decades of research on grinding by engineers and academics (like Professor Steve Malkin of University of Massachusetts-Amherst) the relationship between grinding process parameters and material removal and, finally, surface finish is well characterized. We can summarize the gist of this rather simply as follows. We can describe a general empirical relationship that links the achieved average height surface roughness of a grinding process to the process specific volumetric
removal rate and the grinding wheel speed, the v in the above diagram, by assuming a direct correlation between the surface roughness and undeformed chip thickness.

Let me explain.

The undeformed chip thickness is the depth of cut of the grain into the workpiece. Seen in the figure above it would be the difference between the bottom of the grit in the work and the top of the work surface. Specific volumetric removal rate is the volume per unit time of material removed by the process - here dependent on the number of grains moving over the surface at the undeformed depth.  Each grain removes a small volume and the grains pass the surface at a specific rate. Surface roughness is a measure of the variation of the surface of a workpiece at a very fine scale, usually microinches or micrometers. The smaller the variation the smoother the surface. So small is good in the world of surface roughness!

OK … still with me?!

Then we can move on to the leveraging part. The figure below shows the link we are trying to quantify. This figure illustrates the discussion above about removal and surface effects but also shows where

the impact comes in. This figure is motivated by the work of another researcher at the University of Kentucky - Professor I. Jawahir. He studies the connection between process parameters, surface integrity and part function. The trade off between volumetric removal rate and surface roughness must be done understanding that if we adjust the grinding (or finishing) process to create a better surface finish it will cost us something. Here the cost is likely to be time (as finer finishing processes often take longer - remember how much time you need to sand with fine paper as apposed to rough sand paper to achieve a certain surface?). It will also cost us energy - we'll see why next time.

The "leveraging" comes in with the expected fuel savings due to the better efficiency of the gear operation due to the better surface finish. We need to determine if the increased consumption of energy in finishing is paid back in the improvement in the operation of the gear train and accompanying reduction in fuel use.

We'll "do the numbers" next time.

Leveraging Manufacturing, Part 1

First, some background

Where did March go?!

We finished a long set of postings on the power of the digital age in the form of software to connect the designer to the process with an eye to achieving all the normal requirements of a product but, in addition, incorporating measures to drive sustainable product design and green manufacturing. There is certainly more that can be said about that.

But, not now.

I'd like to get back to a subject that was mentioned first about one and a half years ago in an earlier posting - leveraging.

This was a follow on to a discussion centering on the "buy to fly" ratio concept used in the aerospace industry and discussed in another posting in November 2010 covering the impact of manufacturing on product performance.

That posting cited some results from VW on the role of manufacturing in the life cycle impacts of a particular VW automobile. It turned out that for a VW Golf example the data showed that there was a 20% manufacturing phase  versus 80% use phase contribution to the life cycle impact of the vehicle. I then did some simple calculations about the effect of some significant savings in one phase of manufacturing due to some "greening" efforts (like using lower energy machine tools, for example) and it turned out that, when this ripples through the production and use phases of the vehicle, we get, at most, single digit improvements in the lifecycle impact.

So, the question was, why bother?!

We rationalized that if you are paying the electricity bill for the factory and this small technology wedge improvement is added to a lot of others in machine operation it can add up to real savings. But, maybe still not impressive compared to the full life cycle of the auto.

But, I reasoned, if we follow that logic we are leaving a lot of potential impact reduction from manufacturing "on the table."

Then I gave the example of something discussed in another prior posting on precision manufacturing about a major German auto manufacturer (but not VW in this case) who has been working to improve the "power density" of some of its diesel engines over the past years and has seen an improvement of almost a factor of 3 in power per unit of displacement. That means, for the same engine size (displacement) they have managed to squeeze three times as much power out. Coupled with advanced fuel injector systems operating at very high pressures (once thought absurd) they see enhanced performance in a small engine - increased fuel economy, improved acceleration (due to reduced mass), and reduced emissions. And this was due to advanced manufacturing.

When we ripple that effect through the life cycle of the vehicle, the impact is enormous. Since most of the life cycle impact (think CO2, for example) is due to operation of the vehicle and the production of the fuel to consume in it, savings due to engine efficiency are highly leveraged.

And, the savings are attributable in large part to manufacturing.

There had been a similar example with respect to improved machining tolerances for airframe structural components in aircraft. Tighter tolerances due to improved machine tool control lead to less weight for the structural components (since we can still meet size/strength/performance requirements without "overbuilding" the component) and that means either more cargo per flight or lower fuel consumption due to decreased aircraft weight. Either one improves the performance of the aircraft. Another example of leveraging.

So, we need to look at this in more detail!

The example I'd like to use to illustrate the fundamentals of leveraging is from a recent paper from our research group at Berkeley. The full citation is “Evaluating the relationship between use phase environmental impacts and manufacturing process precision,” CIRP Annals, 60, 1, 2011, pp. 49-52 and I encourage you to look this up (or contact me and I'll send a copy) for all the details. It is co-authored by two of my research students Moneer Helu and Athulan Vijayaraghavan (now with System Insights).

The example focusses on another aspect of vehicles and transportation - the gear train.

We saw the example for the German auto maker how manufacturing precision can have a strong effect on the operational efficiency of an automotive engine. The operational efficiency of an automobile can be generally measured based on its fuel economy. The fuel economy is strongly influenced by the construction of the powertrain, where tight tolerances and high quality surfaces in the camshaft and crankshaft bearings are required to ensure relatively low losses. Tight tolerances are also required between the piston, piston ring, and cylinder surfaces to enable the use of lower viscosity oils that reduce frictional losses in the engine. In addition to the powertrain, the drivetrain is another component of automobiles that is vital to fuel economy.

Recent work has shown that the efficiency of gear systems is due to a variety of factors including the surface roughness of the mating surfaces, assembly errors (e.g. shaft misalignments), and other manufacturing errors (e.g. form errors). Because the vast majority of environmental impacts of an automobile occur during the use phase as we saw illustrated in the VW example, the impact of increased manufacturing precision through better surface finish on the final drive reduction of an automotive manual transmission drivetrain presents the ideal case study for this investigation.

For a good review of the terms powertrain and drivetrain I suggest any book in automotive engineering or our friend Wikipedia! The term powertrain usually includes the engine, transmission, drive shafts, differentials, and the final drive (drive wheels) but it sometimes refers only to the engine and transmission. Wikipedia sums up the case well-

"Competitiveness drives companies to engineer and produce powertrain systems that over time are more economical to manufacture, higher in product quality and reliability, higher in performance, more fuel efficient, less polluting, and longer in life expectancy. In turn these requirements have led to designs involving higher internal pressures, greater instantaneous forces, and increased complexity of design and mechanical operation. The resulting designs in turn impose significantly more severe requirements on parts shape and dimension; and material surface flatness, waviness, roughness, and porosity."

It's this last bit - about imposing stricter requirements on, among other things, surface features including waviness and roughness - that we are going to focus on here.

But, we'll start that in the next posting. Soon!

Tools of the trade, Part 6

Last Comments on Software

In the last posting I posed the question "suppose you want to take some action - either at the design end or the manufacturing end. What tools can you rely on after you've done the background work and now want to move on to execution?"

The simple answer I gave was "This is where software tools come in."

I then proceeded to go into details about some interesting software tools that aid the designer, or manufacturer, in decision making about green and sustainable actions to take.

Shortly after that posting, I was invited to participate in a live (and simultaneously web-broadcast) Sustainability Summit at Autodesk in San Francisco.

Not surprisingly, the event was well organized and attended by an interesting mix of media, industry and students (including a sizable audience "attending" via a YouTube live link). It was interesting to hear a large corporation with a number of software tools for designers and engineers in this field, like the Autodesk® Inventor® 3D CAD software, discuss where they think the market is going and what software tools will have to allow the designer, and manufacturer, to do.

The program was comprised of a series of discussions and panel discussion starting with company CEO Carl Bass in conversation with Marc Gunther, a Fortune and GreenBiz contributor, discussing the importance of sustainability in the future of design.  Although this conversation was design centric, it had a lot of leads into green and sustainable manufacturing.

Other participants included Clean Tech Partner Burt Hamner of Hydrovolts; myself representing our lab at UC Berkeley on green manufacturing; Daniel Talancon and Vince Romanin, UC Berkeley graduate students on their Eco-Fridge design using Inventor; and Ken Sanders of Gensler on their Shanghai Tower design and other sustainable building projects.

Rather than rattling on about the meeting here, I've decided to take the easy way out and post links to the recorded YouTube presentations and discussions. That is more effective and, to me, better to hear the participants speaking about their views in their own words.

As a set up to the recording of my comments as part of the panel discussion lead by Autodesk's Sarah Krasley, I was asked to describe our "spatial vs temporal" matrix of manufacturing activities. This was first presented in the January 21, 2010 posting as part of a "low hanging fruit" series.

As a quick refresher, in case you did not see this (or since it was over two years ago!), the matrix is designed to illustrate different levels of control and flexibility in manufacturing from a temporal view (ie what comes first, second, third, and so on) and spatial view (where in the enterprise - broadly viewed - can actions be taken). I had detailed the temporal configuration as including four levels, from product design at level 1 through process design and planning (manufacturing plan) to parameter selection and process optimization to post manufacturing operations (finishing, etc.) at level 4. It was noted that the flexibility to make decisions decreases as we move away from design towards manufacturing.

This makes sense. On the factory floor we are no longer able to change the product or component design, material or other features. We may not, at level 3, be able to do much about the suite of machines we intend to use to produce the part. We most likely can adjust some of the operating parameters or, at level 4, do some finishing or alteration to overcome a problem.

The spatial domains are defined along the same lines except they will move outward from production specifics in the plant to facility design, enterprise design, logistics (or inter-enterprise) and supply chain and distribution.

The figure below was included as a graphical representation of the matrix and is worth repeating here.

The temporal axis is horizontal and the spatial axis is vertical. As one moves up and to the right in the figure one can suffer a loss of decision making capability as all earlier decisions in the product design cycle, or lower in the supply chain, effect the ability to make decisions at higher levels.  How you affect what is happening at any location within this matrix depends on what information you have about the process or system represented there, what your understanding is of what this information says about what's going on, what ability you have respond to this understanding, if needed (or leave it alone if it is performing correctly), what "levers and buttons" you have at your disposal to make a response and, finally, what means you have to determine if your response had any impact and, if so, how much.

That is, with respect to our tools discussion here, how well the software you are using to integrate across these different levels includes all the critical information, reasoning, behavioral models, visualization, etc. to support your work and decisionmaking.

So, with that set up on the summit in general and the background on my particular contribution, the links to the different presentations are listed below. They are only 3-5 minutes in length so are easily digestible (with the exception of the interview with the CEO - which is much longer.)

Warning - this was a a commercial event so it is, not surprisingly, very professionally done and has a commercial message. But, the contributors are genuine in their enthusiasm are their messages are on target and worth listening to!

The links are:

- Overview of the program and Sara Krasley interviewing the panel

- UC Berkeley students on their Eco-fridge design

- Ken Sanders of Gensler speaking about green building design

- Dave Dornfeld speaking about the temporal - spatial matrix discussed above

- Carl Bass, Autodesk CEO, being interviewed (Careful- this is a long one! 34 Minutes)

Or you can see the complete "playlist" on line.

Enjoy!!

For sure, there is other software on the market that addresses many of these same issues. You should check that out on your own.

Finally, next time we'll revisit the leveraging discussion.