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The second edition of Mechanical Behavior of Materials has revised and updated material in every chapter to reflect the changes occurring in the field. In view of the increasing importance of bioengineering, a special emphasis is given to the mechanical behavior of biologi-cal materials and biomaterials throughout this second edition. A new chapter on environmental effects has been added. Professors Fine and Voorhees make a cogent case for integrating biological materials into materials science and engineering curricula. This trend is already in progress at many US and European universities. Our second edition takes due recognition of this important trend. We have resisted the temptation to make a separate chapter on biological and biomaterials. Instead, we treat these materials together with traditional materials, viz., metals, ceramics, polymers, etc. In addition, taking due cognizance of the importance of electronic materials, we have emphasized the distinctive features of these materials from a mechanical behavior point of view.
The underlying theme in the second edition is the same as in the first edition. The text connects the fundamental mechanisms to the wide range of mechanical properties of different materials under a variety of environments. This book is unique in that it presents, in a unified manner, important principles involved in the mechanical behavior of different materials: metals, polymers, ceramics, composites, electronic materials, and biomaterials. The unifying thread running throughout is that the nano/microstructure of a material controls its mechanical behavior. A wealth of micrographs and line diagrams are provided to clarify the concepts.
In this chapter, we discuss one important means of altering the mechanical response of metals and ceramics: martensitic transformation. Martensitic transformation is a highly effective means of increasing the strength of steel. An annealed medium-carbon steel (such as AISI 1040) has a strength of approximately 100 MPa. By quenching (and producing martensite), the strength may be made to reach about 1 GPa, a tenfold increase. The ductility of the steel is, alas, decreased.
A quite different effect is observed in ceramics. Martensitic transformation can be exploited to enhance the toughness of some ceramics. If a ceramic undergoes a martensitic transformation during the application of a mechanical load, the propagation of cracks is inhibited. For example, partially stabilized zirconia has a fracture toughness of approximately 7 MPa m1/2. An equivalent ceramic not undergoing martensitic transformation would have a toughness less than or equal to 3 MPa m1/2.
An additional, and very important, effect associated with martensitic transformations is the “shape-memory effect.” Alloys undergoing this effect “remember” their shape prior to deformation. The three effects just described have important technological applications.
Structures and Morphologies of Martensite
Quenching has been known for over 3,000 years and is, up to this day, the single most effective mechanism known for strengthening steel. However, it is only fairly recently that the underlying mechanism has been studied in a scientific manner and understood.
There is some confusion in the literature about the terminology pertaining to fatigue. We define fatigue as a degradation of mechanical properties leading to failure of a material or a component under cyclic loading. This definition excludes the so-called phenomenon of static fatigue, which is sometimes used to describe stress corrosion cracking in glasses and ceramics in the presence of moisture. Brittle solids (glasses and crystalline ceramics) undergo subcritical crack growth in an aggressive environment under static loads. Silica-based glasses are especially susceptible to this kind of crack growth in the presence of moisture. If a glassy phase exists at grain boundaries and interfaces, it will be susceptible to such an attack. Thus, static fatigue is more appropriately a stress corrosion phenomenon, rather than a cyclic stress-related phenomenon.
In general, fatigue is a problem that affects any structural component or part that moves. Automobiles on roads, aircraft (principally the wings) in the air, ships on the high sea constantly battered by waves, nuclear reactors and turbines under cyclic temperature conditions (i.e., cyclic thermal stresses), and many other components in motion are examples in which the fatigue behavior of a material assumes a singular importance. It is estimated that 90% of service failures of metallic components that undergo movement of one form or another can be attributed to fatigue. Often, a fatigue fracture surface will show some easily identifiable macroscopic features, such as beach markings.
These days, the term ‘sophist’ is used solely as a term of disdain, for those who hope to get away with shoddy reasoning. It was not always thus. Our term ‘sophist’ derives from a Greek term σοφιστής; and in the fifth century bc, when that term was first used, σοφισταί were men to be reckoned with.
The first σοφισταί were so called because of some expertise or σοφία. In principle, any expert might be given the name σοφιστής. We hear, for example, of those who were given the name because they were experts in poetry, statecraft or ritual (311e4n.). In practice, the main bearers of the name were men like three of the characters in the Protagoras: Protagoras of Abdera himself, Hippias of Elis and Prodicus of Ceos. Among the better documented of the others like them were Gorgias of Leontini, Thrasymachus of Chalcedon and Antiphon of Athens. These men did not all make claim to exactly the same expertise (312d9–e1n.): for example, Prodicus had a special flair for distinguishing between words of very similar meaning (337a1–c4); Hippias cultivated a special mnemonic technique that enabled him to repeat a list of fifty names after hearing it just once (Hp. Ma. 285e; cf. 318e3n.); and Protagoras won so special a reputation for his understanding of how institutions can be managed (318e4–319a6) that he was commissioned to devise the constitution for a new Panhellenic settlement at Thurii (DK 80 A 1.50).
Socrates explains to his friends that he has just come from a conversation with Protagoras, a man whose wisdom makes him so attractive that Socrates found him even more attractive than the most handsome youth in Athens. The friends invite him to tell them the full story, and he agrees. These many friends all remain anonymous, as perhaps befits the audience for a dialogue that will have, as a leading character, a composite figure called ‘the many’
The Introduction to this book contains general remarks that could not conveniently be digested into the piecemeal format of the commentary. In spite of its name ‘Introduction’, and its position before the text, there is no need to have read the Introduction before starting to read the rest of the book. If some preliminary orientation to the Protagoras is required, it will be found in the italicised paragraphs of summary that are scattered throughout the commentary.
I have incurred many intellectual debts in writing this book: to the Editors of this series; to the unfailingly efficient and helpful staff of that marvellous resource, the Thesaurus Linguae Graecae; to those who took part in the Mayweek 2004 seminars on the Protagoras; to Bernard Dod, an exact and scrupulous copy-editor; and to Adam Beresford, Lynne Broughton, Myles Burnyeat, Andrea Capra, Giovanni di Pasquale, David Konstan, Geoffrey Lloyd, Catherine Osborne, Philomen Probert, Christopher Rowe, Catherine Steel, Liba Taub, Christopher Taylor, James Warren, Roslyn Weiss, and Jo Willmott.
More important than any intellectual debt is my debt to my father, Ronald Denyer. He died while I was writing this book. I dedicate it to his memory.
Recall the major steps in inverted index construction:
Collect the documents to be indexed.
Tokenize the text.
Do linguistic preprocessing of tokens.
Index the documents that each term occurs in.
In this chapter, we first briefly mention how the basic unit of a document can be defined and how the character sequence that it comprises is determined (Section 2.1). We then examine in detail some of the substantive linguistic issues of tokenization and linguistic preprocessing, which determine the vocabulary of terms that a system uses (Section 2.2). Tokenization is the process of chopping character streams into tokens; linguistic preprocessing then deals with building equivalence classes of tokens, which are the set of terms that are indexed. Indexing itself is covered in Chapters 1 and 4. Then we return to the implementation of postings lists. In Section 2.3, we examine an extended postings list data structure that supports faster querying, and Section 2.4 covers building postings data structures suitable for handling phrase and proximity queries, of the sort that commonly appear in both extended Boolean models and on the web.
Document delineation and character sequence decoding
Obtaining the character sequence in a document
Digital documents that are the input to an indexing process are typically bytes in a file or on a web server. The first step of processing is to convert this byte sequence into a linear sequence of characters.
The meaning of the term information retrieval (IR) can be very broad. Just getting a credit card out of your wallet so that you can type in the card number is a form of information retrieval. However, as an academic field of study, information retrieval might be defined thus:
Information retrieval (IR) is finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections (usually stored on computers).
As defined in this way, information retrieval used to be an activity that only a few people engaged in: reference librarians, paralegals, and similar professional searchers. Now the world has changed, and hundreds of millions of people engage in information retrieval every day when they use a web search engine or search their email. Information retrieval is fast becoming the dominant form of information access, overtaking traditional database-style searching (the sort that is going on when a clerk says to you: “I'm sorry, I can only look up your order if you can give me your order ID”).
Information retrieval can also cover other kinds of data and information problems beyond that specified in the core definition above. The term “unstructured data” refers to data that does not have clear, semantically overt, easy-for-a-computer structure. It is the opposite of structured data, the canonical example of which is a relational database, of the sort companies usually use to maintain product inventories and personnel records.
On page 113, we introduced the notion of a term-document matrix: an M × N matrix C, each of whose rows represents a term and each of whose columns represents a document in the collection. Even for a collection of modest size, the term-document matrix C is likely to have several tens of thousands of rows and columns. In Section 18.1.1, we first develop a class of operations from linear algebra, known as matrix decomposition. In Section 18.2, we use a special form of matrix decomposition to construct a low-rank approximation to the term-document matrix. In Section 18.3 we examine the application of such low-rank approximations to indexing and retrieving documents, a technique referred to as latent semantic indexing. Although latent semantic indexing has not been established as a significant force in scoring and ranking for information retrieval (IR), it remains an intriguing approach to clustering in a number of domains including for collections of text documents (Section 16.6, page 343). Understanding its full potential remains an area of active research.
Readers who do not require a refresher on linear algebra may skip Section 18.1, although Example 18.1 is especially recommended as it highlights a property of eigenvalues that we exploit later in the chapter.
Linear algebra review
We briefly review some necessary background in linear algebra. Let C be an M × N matrix with real-valued entries; for a term–document matrix, all entries are in fact non-negative.
Thus far, this book has mainly discussed the process of ad hoc retrieval, where users have transient information needs that they try to address by posing one or more queries to a search engine. However, many users have ongoing information needs. For example, you might need to track developments in multicore computer chips. One way of doing this is to issue the query multicore and computer and chip against an index of recent newswire articles each morning. In this and the following two chapters we examine the question: How can this repetitive task be automated? To this end, many systems support standing queries. A standing query is like any other query except that it is periodically executed on a collection to which new documents are incrementally added over time.
If your standing query is just multicore and computer and chip, you will tend to miss many relevant new articles which use other terms such as multicore processors. To achieve good recall, standing queries thus have to be refined over time and can gradually become quite complex. In this example, using a Boolean search engine with stemming, you might end up with a query like (multicore or multi-core) and (chip or processor or microprocessor).
To capture the generality and scope of the problem space to which standing queries belong, we now introduce the general notion of a classification problem. Given a set of classes, we seek to determine which class(es) a given object belongs to.
In Chapters 1 and 2, we developed the ideas underlying inverted indexes for handling Boolean and proximity queries. Here, we develop techniques that are robust to typographical errors in the query, as well as alternative spellings. In Section 3.1, we develop data structures that help the search for terms in the vocabulary in an inverted index. In Section 3.2, we study the idea of a wildcard query: a query such as *a*e*i*o*u*, which seeks documents containing any term that includes all the five vowels in sequence. The * symbol indicates any (possibly empty) string of characters. Users pose such queries to a search engine when they are uncertain about how to spell a query term, or seek documents containing variants of a query term; for instance, the query automat* seeks documents containing any of the terms automatic, automation, and automated.
We then turn to other forms of imprecisely posed queries, focusing on spelling errors in Section 3.3. Users make spelling errors either by accident, or because the term they are searching for (e.g., Herman) has no unambiguous spelling in the collection. We detail a number of techniques for correcting spelling errors in queries, one term at a time as well as for an entire string of query terms. Finally, in Section 3.4 we study a method for seeking vocabulary terms that are phonetically close to the query term(s).