Table 1 Percentage of emissions generated during each stage of the vehicle life cycle for a steel unibody and an average aluminum design Emissions produced, % Emission type Mining/refining Production Use Post-use Steel unibody CO 2 2.32 0.05 97.63 0 HC 0.10 0 99.90 0 NO x 2.21 0.04 97.74 0 CO 0.05 0 99.95 0 Particulate 23.30 0.50 76.20 0 SO x 97.78 1.97 0 0.25 Aluminum body CO 2 8.65 0.09 91.26 0 HC 0.40 0 99.60 0 NO x 8.27 0.08 91.64 0 CO 0.21 0 99.79 0 Particulate 84.10 0.05 15.85 0 SO x 98.98 0.99 0 0.04 Results show that the aluminum designs perform better in the pollutant categories that dominate during the use phase (CO 2 , hydrocarbons, NO x , and CO). However, for particulates and sulfur oxides, the steel design is more competitive. Emissions associated with the aluminum design are greater for the mining and refining stage (sulfur oxides and particulates). For the categories that arise predominantly during vehicle use (CO 2 , hydrocarbons, NO x , and CO), the lightweighting achieved in the aluminum designs pays off. Analysis of the inventory data does not lead to an unambiguous result. On a cost basis, even with a life cycle approach, the steel unibody is most competitive. However, if the goal is to reduce greenhouse gases and smog precursors, one of the aluminum designs may be preferred (Fig. 13). Emission Type Change when using aluminum, % CO 2 -1.24 HC -7.71 NO x -1.50 CO -7.50 Particulate +438 SO x +264 Fig. 13 Emissions throughout the entire product life cycle for steel and aluminum automobile bodies Impact and Evaluation. Most efforts to develop the LCA technique have focused on constructing a complete set of procedures for the collection and organization of the information that must be developed in the course of a LCA. However, determining what to do with this information, once it is collected, has so far been only imperfectly addressed. Although the reason for employing LCA is to develop activities that reduce environmental impact, establishing how this mass of data informs specific problems has proven to be extremely difficult for all but the simplest of situations. In particular, the most problematic aspect of LCA has been the final, "improvement analysis" component. Improvement analysis implicitly assumes that it is possible to choose (and implement) a "best" action from the set of possible actions, thus yielding improvement. Aside from simple cases where it is possible to find an action that leads to reductions in all impacts on the environment, this choice depends upon the relative importance placed upon each of the possible consequences that are indicated by the analysis. This relative rating of importance is a reflection of the strategic objectives of the user objectives that are not necessarily shared by all interested stakeholders. Example: Method for Estimating the "Environmental Load" of Materials. To illustrate the potential and limitations of LCA method, the Swedish Environmental Priority Strategies (EPS), under development by the Swedish Environmental Research Institute, Chalmers Institute of Technology, and the Federation of Swedish Industries, are discussed (Ref 29). EPS translates emissions into a single monetary metric that allows the direct costs of manufacturing, use, and recycling/disposal to be compared with the social costs generated by emissions. EPS is specifically constructed to associate an "environmental load" with individual activities or processes on a per unit of material consumed or processed basis. For example, EPS might associate X units of environmental load (ELUs) per kilogram of steel produced and Y units of environmental load per kilogram of steel components stamped. Thus, the environmental load of stamping a 5 kg automobile component, requiring 5.3 kg of steel, would be (5.3 X + 5 Y). This load could then be compared to the load associated with a different process stream or with using a different material. The interesting questions are: how are these environmental loads established and what do they mean. Based on the environmental objectives of the Swedish Parliament, EPS relates all of the physical consequences of the processes under consideration to their impact on five environmental "safeguard subjects": biodiversity, production (i.e., reproduction of biological organisms), human health, resources, and aesthetic values. Because the impacts on any one safeguard subject by a process may take several forms, EPS allows for individual consideration of each of these consequences, called "unit effects." Two criteria are applied when establishing which impacts will become unit effects: the importance of the impact on the sustainability of the environment and the existence of an ability to establish a quantitative value for that impact within traditional economic grounds. Examples of unit effects for human health include: mortality due to increased frequency of cancer; mortality due to increased maximum temperatures; food production decreases (and, hence, increased incidence of starvation) due to global warming. Once the individual unit effects are established, their value must be determined. This valuation is accomplished by expressing each unit effect in terms of its economic worth and associated risk factors. Formally, the value of each unit effect is set equal to the product of five factors, F1 through F5. F1 is a monetary measure of the total cost of avoiding the unit effect. The extent of affected area (F2), the frequency of unit effect in the affected area (F3), and the duration of the unit effect (F4), represent "risk factors" similar to those employed in toxicological risk evaluations. F5 is a normalizing factor, constructed so that the product F1 × F5 is equal to the cost of avoiding the unit effect that would arise through the use or production of one kilogram of material. The product of all five factors yields the contribution of a particular unit effect to environmental load. Summing the value of each unit effect yields the "environmental load index" (ELI) in units of environmental load per unit of material consumed or processed (ELU/kg). Since these unit effects were specified according to their relevance to the five safeguard subjects, the ELI represents the total environmental load (or impact) of the process. While this formulation of valuation raises important questions of scientific feasibility (insofar as the ability to characterize fully the unit effects of every process or activity that might be developed is debatable), the crucial valuation questions arise from two other aspects of this scheme: (a) the nature of the economic measures used in calculating the cost of avoiding a unit effect, and (b) the assumption that the value of the total environmental impact of an action (the "environmental load") is equal to the sum of each individual environmental load weighted by the size of each unit effect. The first of these valuation questions relates to the distinction between "cost" and "worth." While the theory of competitive markets argues that prices are the worth of an object, the theory rests upon assumptions that are difficult to support in the case of environment. In the first place, perfect markets assume the availability of perfect information to all participants, which clearly is not the case, or there would be no need to develop life cycle analysis in the first place. Furthermore, the theory of markets routinely discusses "consumer surplus," which can roughly be defined as the difference in the prevailing market price and the higher price that some consumers would have been willing to pay (recall that demand curves slope downward). Finally, there is the critical question how to establish these costs/prices when markets do not exist. While litigators are prepared to place a value on wrongful death or pain and suffering during a civil suit, there are no markets for pain, clean air, or future well-being. Generally, most environmental attributes are "external" to markets; many of the classical examples of market externalities are based on environmental issues. Where markets exist, EPS uses market prices to establish the costs of avoidance. Where market prices do not exist, EPS relies upon two alternatives. If there are governmental funds allocated to resolve specific problems (e.g., funds to protect a particular species), these funds are normalized and extrapolated to obtain a cost figure (e.g., the value of maintaining biodiversity is established by normalizing the annual budget of the Swedish government for species protection). If relevant financial allocations do not exist, then the method of contingent valuation is employed. This method (or set of methods) is based on direct inquiries of representative populations to determine their willingness to pay to avoid specific effects. As might be expected, this last approach to establishing the appropriate costs of avoidance is somewhat controversial, since it is hard (both conceptually and practically) to design questions that demonstrably extract the "correct" measure of value. The second of these valuation questions is a reflection of the fact that the mathematical structure of the value function is a consequence of critical assumptions about the nature of the subject's preferences. The valuation employed in the EPS system is an example of a linear, additive preference structure. Each unit effect is reduced to a monetary value, normalized for risk/exposure and for material quantity. Thereafter, the net impact of each increment in unit effect is the same, regardless of how large the effect is, and regardless of the size of any other unit effect. While such value functions are simple to represent and employ (linear combinations of linear functions), it is difficult to argue that they are an accurate, general purpose formulation of value functions for environmental impact. Although the appropriate form of the value function may be linear, EPS does not explicitly make this assumption. Rather, the linearity of EPS valuation is based on the assumption that, because monetization reduces all effects to a common metric, the resulting metrics should be additive. In fact, most individuals do not even exhibit linear preferences for money, much less for more subjective attributes. (For example, most individuals would consider paying $0.50 to play a game offering a 50:50 chance of winning $1.00, while rejecting out of hand paying $5,000 to get a 50:50 chance of winning $10,000). In practice, preferences usually reflect nonlinearities in both individual effects and in substitution between effects. The first two issues (money as a measure of value and linear additive preferences) are not necessarily crippling assumptions when considering the development of value functions for the environment. While difficult, it may be possible for someone to establish the dollar value that exactly offsets a particular unit effect. Similarly, linear additive preferences may be able to model the behavior of an individual over a restricted range. However, it is impossible to state that the same dollar value, or the same linearization of preferences, will be agreeable to every individual in the affected population in the case of environmental considerations. And, if individuals cannot agree on the value or the structure of their preferences, then no single value function can be constructed to represent their wants. A recent methodology developed at MIT (Ref 30) is similar to EPS, but provides a set of broad ranges of value, in dollars per kilogram of each emission, based on estimates of willingness to pay to avoid the environmental impacts of each pollutant. These ranges reflect scientific uncertainty, variation in context or location, and large variations of possible values for parameters that have a subjective component. The dollars per kilogram ranges can be applied to the life cycle inventories of products to compare material or process alternatives. The methodology was used to analyze the life cycle costs of three material alternatives for automotive fenders produced at low volumes (60,000/year). The three materials under consideration were steel, aluminum, and Noryl (Noryl is a trademark of General Electric Company for a polyphenylene oxide blend thermoplastic). The results of the base case, employing "best guess" for scientific data and economic valuation, are shown in Fig. 14. Fig. 14 Estimated life cycle costs by life phase for competing materials for an automobile fender application In this scenario, the private costs of manufacturing and use (with German gasoline prices) are significantly greater than the social costs from emissions to the environment. Figure 15 shows the implications of allowing the scientific and economic assumptions to take on the highest and lowest values possible, based on a review of published estimates. Fig. 15 Total costs relative to steel of competing materials for an automobile fender application The externalities are the environmental costs of emissions from the extraction to the manufacturing and use stages. The private costs include manufacturing, use, and disposal. The specific assumptions employed in this case study lead to a lower total cost for Noryl, although no clear winner arises under this set of assumptions. Even when no clear choice emerges, the environmental cost drivers can be identified. For instance, the fender case study shows that only 4 or 5 emissions categories account for more than 95% of the total environmental cost for each material. The EPS system is a commendable attempt at simplifying the enormous detail of inventory data to a representative environmental load. The developers of EPS have pointed out that this system is based on their subjective value judgments, which are not necessarily supportable in all situations worldwide. The ultimate goals for improvement analysis based on life cycle inventories are laudable, but can only be realized by some kind of consensus on the values for avoiding environmental degradation. This suggests that achieving the ultimate stage of LCA will require the development of a basis for devising (and revising) this consensus. In the absence of a common strategic objective, it will be impossible to use LCA to designate ways to achieve environmental improvement beyond straightforward pollution prevention/precautionary principle strategies, because a strategic consensus is required to trade off competing environmental, economic, and engineering goals. Uses of LCA. In summary, life cycle analysis is a technique that has already shown great promise for improving our understanding of the wider implications and relationships that must be taken into consideration when incorporating environmental concerns into technical decision making. As these concepts diffuse into industrial and technical decision making, LCA will enable industry and government to find ways to be both more efficient and less harmful to the environment. However, practitioners and proponents must guard against using LCA to determine "best" modes of action when the consequences of the alternatives expose conflicting objectives and values within the group of decision makers. In these cases, no amount of analysis will directly resolve the conflict. Rather, the role of LCA should be to articulate clearly the consequences of each alternative and to provide a framework for the necessary negotiations. Additional information about LCA is provided in the articles "Life-Cycle Engineering and Design" and "Environmental Aspects of Design" in this Volume. References cited in this section 28. F.R. Field, J.A. Isaacs, and J.P. Clark, Life Cycle Analysis and Its Role in Product and Process Development, J. Environmentally Conscious Manufacturing, 1996 29. B. Steen and S O. Ryding, The EPS Enviro-Accoun ting Method: An Application of Environmental Accounting Principles for Evaluation and Valuation of Environmental Impact in Production Design, Swedish Environmental Institute, Dec 1992 30. J. Clark, S. Newell, and F. Field, Life Cycle Analysis Methodology Incorporating Private and Social Costs, in Life Cycle Engineering of Passenger Cars, VDI Verlag GmbH, 1996, p 1-19 Techno-Economic Issues in Materials Selection Joel P. Clark, Richard Roth, and Frank R. Field III, Massachusetts Institute of Technology Conclusions There is an ever growing need for consistent methodologies for analyzing the use of new materials, designs, and technologies in many applications. Advances in materials science and in the development of new processing technologies have presented product designers with a wide array of choices previously unavailable to them. This has made the selection of a material for a given application a far more challenging task. The difficulty confronting designers is compounded by the increasing number of objectives that product designers must satisfy. In the past, the designer simply had to meet a set of performance criteria, at or below a specified cost, from a very limited set of design alternatives. The current situation is much more complicated. In addition to the increasing number of design choices, there are potentially conflicting performance, cost, and environmental characteristics. Central to all product evaluations is a consideration of the economic consequences of design and materials choice. Cost is one of the key strategic elements of product competitiveness, and an early appreciation of the relationship between major design choices and the cost of the resulting product is a vital element of effective product development. However, cost remains an elusive element of design evaluation. The tools are largely outside the control of the design engineers. The results suggest only a limited number of ways in which cost can be changed, and the costs tend to focus only upon the cost consequences to the firm itself. Unfortunately, designers require a far more comprehensive appreciation of cost, particularly as the number and complexity of design objectives have increased. The combined technical cost modeling and life cycle analysis methodology offers the product designer a much needed systematic approach for analyzing the trade-offs associated with various choices of materials and technologies. Technical cost modeling enables designers to estimate the manufacturing costs of alternative designs. Its main advantages lie in the fact that it is predictive and allows one to investigate the sensitivity of the outcome to changes in the input parameters. Because it is predictive, it can be used with new processes for which there is no past experience upon which to base cost estimates. The ability to do sensitivity analysis enables the product designer to look at the effects of unknown or uncertain model parameters, capturing the scope and consequences of important processing and market assumptions. The advantages of the life cycle approach are also two-fold. First, life cycle analysis enables one to look at cost over the entire life of the product, not just the manufacturing phase. For many products, cost can be quite substantial during other parts of the product life, especially the use phase. Second, life cycle analysis is useful for looking at issues relevant to environmental concerns, such as tracking selected emissions throughout the product life. While valuation techniques are rather imperfect, they provide a means for translating these diverse parameters into a common metric, as well as a context for analyzing the implications of distinctions in the strategic objectives of all parties affected by the product and design choice. The integrated approach provided by technical cost modeling and life cycle analysis is particularly important in industries such as the automotive sector, where both consumer and regulatory pressures are causing the producers to continuously innovate. The combined life cycle cost and emissions methodology offers a systematic and predictive method for addressing some of the fundamental considerations involved in selecting materials and designs for specific products. Techno-Economic Issues in Materials Selection Joel P. Clark, Richard Roth, and Frank R. Field III, Massachusetts Institute of Technology References 1. R. Roth, F. Field, and J. Clark, Materials Selection and Multi-Attribute Utility Analysis, J. Computer- Aided Mater. Des., Vol 1 (No. 3), ESCOM Science Publishers, Oct 1994 2. J.V. Busch and F.R. Field III, Technical Cost Modeling, Blow Molding Handbook, Donald Rosato and Dominick Rosato, Ed., Hanser Publishers, 1988, Ch 24 3. M.F. Ashby, Materials Selection in Mechanical Design, Pergamon Press, 1992 4. R. Cooper and P. Kaplan, Measure Costs Right: Make the Right Decisions, Harvard Business Review, Sept-Oct 1988 5. "Implementing ABC in the Automobile Industry: Learning from Information Technology Experiences," MIT International Motor Vehicle Program working paper 6. J.F. Elliot, J.J. T ribendis, and J.P. Clark, "Mathematical Modeling of Raw Material and Energy Needs of the Iron and Steel Industry in the USA.," Final Report to the U.S. Bureau of Mines, NTIS PB 295- 207 (AS), 1978 7. F.E. Katrak, T.B. King, and J.P. Clark, Analysis of the Supply of and Demand for Stainless Steel in the United States, Mater. Soc., Vol 4, 1980 8. P.T. Foley and J.P. Clark, U.S. Copper Supply An Engineering/Economic Analysis of Cost- Supply Relationships, Resour. Policy, Vol 7 (No. 3), 1981 9. J.P. Clark and G.B. Kenney, The Dynamics of International Competition in the Automotive Industry, Mater. Soc., Vol 5 (No. 2), 1981 10. J.P. Clark and M.C. Flemings, Advanced Materials and the Economy, Sci. American, Oct 1986 11. Lee Hong Ng and Frank R. Field III, Mat erials for Printed Circuit Boards: Past Usage and Future Prospects, Mater. Soc., Vol 13 (No. 3), 1989 12. S. Arnold, N. Hendrichs, F.R. Field III, and J.P. Clark, Competition between Polymeric Materials and Steel in Car Body Applications, Mater. Soc., Vol 13 (No. 3), 1989 13. V. Nallicheri, J.P. Clark, and F.R. Field, A Technical & Economic Analysis of Alternative Manufacturing Processes for the Connecting Rod, Proceedings, International Conference on Powder Metallurgy (Pittsburgh, PA), Metal Powder Industries Federation, May 1990 14. C. Mangin, J. Neely, and J. Clark, The Potential for Advanced Ceramics in Automotive Engines, J. Met., Vol 45 (No. 6), 1993 15. F.R. Field and J.P. Clark, Automotive Body Materials, Encyclopedia of Advanced Materials, R.W. Cahn et al., Ed., Pergamon Press, 1994 16. H. Han and J. Clark, Life Cycle Costing of the Body-in-White: Steel vs. Aluminum, J. Met., May 1995 17. G. Potsch and W. Michaeli, Injection Molding: An Introduction, Hanser Publishers, 1995 18. P. Kennedy, Flow Analysis Reference Manual, Moldflow Pty. Ltd., Australia, 1993 19. J.V. Busch, "Technical Cost Modeling of Plastics Fabrication Processes," MIT Ph.D. thesis, June 1987 20. G.H. Geiger and D.R. Poirier, Transport Phenomena in Metallurgy, Addison-Wesley Publishing Company, 1973 21. D. Politis, "An Economic and Environmental Evaluation of Aluminum Designs for Automotive Structures," MIT S.M. thesis, May 1995 22. M.A. DeLuchi, "Emissions of Greenhouse Gases from the Use of Transportation Fuels and Electri city," Vol 2, U.S. Department of Energy, 1993 23. OECD, Automobile Fuel Consumption in Actual Traffic Conditions, Organization for Economic Co- Operation and Development, Dec 1981 24. SRI International, Potential for Improved Fuel Economy in Passenger Car s and Light Trucks, Prepared for the Motor Vehicle Manufacturers Association, Menlo Park, CA, 1991 25. F.R. Field and J.P. Clark, Recycling Dilemma for Advanced Materials Use: Automotive Materials Substitution, Mater. Soc., Vol 15 (No. 2), 1991 26. A.C. Chen, "A Product Lifecycle Framework for Environmental Management and Policy Analysis: Case Study of Automobile Recycling," MIT Ph.D. thesis, June 1995 27. A.C. Chen, H.N. Han, J.P. Clark, and F.R. Field, A Strategic Framework for Analyzing the Cost Effectiveness of Automobile Recycling, Proceedings, International Body Engineering Conference (Detroit), M.N. Uddin, Ed., Society of Automotive Engineers, 1993, p 13-19 28. F.R. Field, J.A. Isaacs, and J.P. Clark, Life Cycle Analysis and Its Role in Product an d Process Development, J. Environmentally Conscious Manufacturing, 1996 29. B. Steen and S O. Ryding, The EPS Enviro- Accounting Method: An Application of Environmental Accounting Principles for Evaluation and Valuation of Environmental Impact in Production Design, Swedish Environmental Institute, Dec 1992 30. J. Clark, S. Newell, and F. Field, Life Cycle Analysis Methodology Incorporating Private and Social Costs, in Life Cycle Engineering of Passenger Cars, VDI Verlag GmbH, 1996, p 1-19 Material Property Charts M.F. Ashby, Engineering Design Centre, Cambridge University Introduction MATERIAL PROPERTIES limit performance. However, it is seldom that the performance of a component depends on just one property. Almost always it is a combination (or several combinations) of properties that matter: one thinks, for instance, of the strength-to-weight ratio, f / , or the stiffness-to-weight ratio, E/ , which are important in design of lightweight products. This suggests the idea of plotting one property against another, mapping out the fields in property- space occupied by each material class, and the subfields occupied by individual materials. The resulting charts are helpful in several ways. They condense a large body of information into a compact but accessible form, they reveal correlations between material properties that aid in checking and estimating data, and they lend themselves to a performance-optimizing technique (developed in the article "Performance Indices" following in this Section of the Handbook), which becomes the basis of the selection procedure. The idea of a materials-selection chart is developed below. Further information about the charts and their uses can be found in Ref 1, 2, 3 and in the article "Performance Indices." Acknowledgements The charts reproduced as Fig. 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, and 13 first appeared in Ref 1, where more details about their use can be found. The author wishes to thank Dr. David Cebon for helpful discussions. The support of the Royal Society, the EPSRC through the Engineering Design Centre at Cambridge, and the Advance Research Project Agency through the University Research Initiative under Office of Naval Research Contract No. N-00014092-J-1808 are gratefully acknowledged. References 1. M.F. Ashby, Material Selection in Mechanical Design, Pergamon Press, 1992 2. M.F. Ashby and D. Cebon, Case Studies in Material Selection, Granta Design, 1996 3. CMS Software and Handbooks, Granta Design, 1995 Material Property Charts M.F. Ashby, Engineering Design Centre, Cambridge University Displaying Material Properties Each property of an engineering material has a characteristic range of values. The values are conveniently displayed on materials selection charts, illustrated by Fig. 1. One property (the modulus, E, in this case) is plotted against another (the density, ) on logarithmic scales. The range of the axes is chosen to include all materials, from the lightest foams to the heaviest metals. It is then found that data for a given class of materials (polymers for example) cluster together on the chart; the subrange associated with one material class is, in all cases, much smaller than the full range of that property. Data for one class can be enclosed in a property-envelope, as shown in Fig. 1. The envelope encloses all members of the class. Fig. 1 The idea of a Materials Property Chart: Young's modulus, E, is plotted against the density, , on log scales. Each class of material occupies a characteristic part of the chart. The log scales allow the longitudinal elastic wave velocity v = (E/ ) 1/2 to be plotted as a set of parallel contours. All this is simple enough just a helpful way of plotting data. However, by choosing the axes and scales appropriately, more can be added. The speed of sound in a solid depends on the modulus, E, and the density, ; the longitudinal wave speed v, for instance, is or (taking logs) log E = log + 2 log v For a fixed value of v, this equation plots as a straight line of slope 1 on Fig. 1. This allows the addition of contours of constant wave velocity to the chart: They are the family of parallel diagonal lines linking materials in which longitudinal waves travel with the same speed. All the charts allow additional fundamental relationships of this sort to be displayed. A number of mechanical and thermal properties characterize a material and determine its use in engineering design; they include density, modulus, strength, toughness, damping coefficient, thermal conductivity, diffusivity, and expansion. The charts display data for these properties for the nine classes of materials listed in Table 1. Within each class, data are plotted for a representative set of materials, chosen both to span the full range of behavior for the class and to include the most common and most widely used members of it. In this way the envelope for a class encloses data not only for the materials listed in Table 1, but for virtually all other members of the class as well. [...]... 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, and 40 References cited in this section 4 American Institute of Physics Handbook, 3rd ed., McGraw-Hill, 1972 5 Metals Handbook, 9th ed., and ASM Handbook, ASM International 6 Handbook of Chemistry and Physics, 52 nd ed., The Chemical Rubber Co., Cleveland, OH, 1971 7 Landolt-Bornstein Tables,... Institute of Physics Handbook, 3rd ed., McGraw-Hill, 1972 Metals Handbook, 9th ed., and ASM Handbook, ASM International Handbook of Chemistry and Physics, 52 nd ed., The Chemical Rubber Co., Cleveland, OH, 1971 Landolt-Bornstein Tables, Springer, 1966 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 Materials Selector, Materials Engineering, Penton Publishing,... Ed., ASM Engineered Materials Reference Book, 2nd ed., ASM International, 1994 18 Materials Selector and Design Guide, Design Engineering, Morgan-Grampian Ltd, London, 1974 19 Handbook of Industrial Materials (1992), 2nd ed., Elsevier, 1992 20 G.S Grady and H.R Clauser, Ed., Materials Handbook, 12th ed., McGraw-Hill, 1986 21 A Goldsmith, T.E Waterman, and J.J.Hirschhorn, Ed., Handbook of Thermophysical... 13 N.A Waterman and M.F Ashby, Ed., The Elsevier Materials Selector, Elsevier and CRC Press, 1991 14 R Morrell, Handbook of Properties of Technical and Engineering Ceramics, Parts I and II, National Physical Laboratory, London, U.K., 19 85 and 1987 15 J.M Dinwoodie, Timber, Its Nature and Behaviour, Van Nostrand-Reinhold, 1981 16 L.J Gibson and M.F Ashby, Cellular Solids, Structure and Properties, 2nd... Material Selection in Mechanical Design, Pergamon Press, 1992 Material Property Charts M.F Ashby, Engineering Design Centre, Cambridge University References 1 2 3 4 5 6 7 M.F Ashby, Material Selection in Mechanical Design, Pergamon Press, 1992 M.F Ashby and D Cebon, Case Studies in Material Selection, Granta Design, 1996 CMS Software and Handbooks, Granta Design, 19 95 American Institute of Physics Handbook,... this article) An important use of the strength-density chart is in materials selection in lightweight plastic design The guide lines performance indices (Table 5b in the article "Performance Indices," which follows in this Section of the Handbook) for materials selection in the minimum-weight design of ties, columns, beams, and plates, and for yield-limited design of moving components in which inertial... Ceramics, Parts I and II, National Physical Laboratory, London, U.K., 19 85 and 1987 J.M Dinwoodie, Timber, Its Nature and Behaviour, Van Nostrand-Reinhold, 1981 L.J Gibson and M.F Ashby, Cellular Solids, Structure and Properties, 2nd ed., Cambridge University Press, 1996 M.L Bauccio, Ed., ASM Engineered Materials Reference Book, 2nd ed., ASM International, 1994 Materials Selector and Design Guide, Design. .. 34 R Morrell, Handbook of Properties of Technical and Engineering Ceramics, Parts 1 and 2, National Physical Laboratory, Teddington, U.K., 19 85 35 W.E.C Creyke, I.E.J Sainsbury, and R Morrell, Design with Non Ductile Materials, Applied Science, London, 1982 36 N.P Bansal and R.H Doremus, Ed., Handbook of Glass Properties, Academic Press, 1966 37 D.S Oliver, Engineering Design Guide 05: The Use of Glass... absolute temperature T, and the vibrational specific heat is Cp Cv = 3Nk where k is Boltzmann's constant The volume of N atoms is (N ) m3, where is the volume per atom; for almost all solids lies within a factor of two of 1.4 × 1 0-2 9 m3 (8 .5 × 1 0-2 5 in.3) The volume specific heat is then (as Fig 10 shows): (Eq 13) Some materials deviate from this rule: they have lower-than-average volumetric specific heat... Harper, Ed., Handbook of Plastics and Elastomers, McGraw-Hill, 19 75 A.K Bhowmick and H.L Stephens, Handbook of Elastomers, Marcel Dekker, 1986 S.P Clarke, Jr., Ed., Handbook of Physical Constants, Memoir 97, The Geological Society of America, New York, 1966 N.A Waterman and M.F Ashby, Ed., The Elsevier Materials Selector, Elsevier and CRC Press, 1991 R Morrell, Handbook of Properties of Technical and Engineering . Physics Handbook, 3rd ed., McGraw-Hill, 1972 5. Metals Handbook, 9th ed., and ASM Handbook, ASM International 6. Handbook of Chemistry and Physics, 52 nd ed., The Chemical Rubber Co., Cleveland,. gases and smog precursors, one of the aluminum designs may be preferred (Fig. 13). Emission Type Change when using aluminum, % CO 2 -1 .24 HC -7 .71 NO x -1 .50 CO -7 .50 Particulate. which follows in this Section of the Handbook) for materials selection in the minimum-weight design of ties, columns, beams, and plates, and for yield-limited design of moving components in which