Toyota Research Research Articles

Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries

With the proliferation of electric vehicles (EVs) and the consequential increase in EV battery circulation, the need for accurate assessments of battery health and remaining useful life (RUL) is paramount, driven by environmentally friendly and sustainable goals. This study addresses this pressing concern by employing data-driven methods, specifically harnessing deep learning techniques to enhance RUL estimation for lithium-ion batteries (LIB). Leveraging the Toyota Research Institute Dataset, consisting of 124 lithium-ion batteries cycled to failure and encompassing key metrics such as capacity, temperature, resistance, and discharge time, our analysis substantially improves RUL prediction accuracy. Notably, the convolutional long short-term memory deep neural network (CLDNN) model and the transformer LSTM (temporal transformer) model have emerged as standout remaining useful life (RUL) predictors. The CLDNN model, in particular, achieved a remarkable mean absolute error (MAE) of 84.012 and a mean absolute percentage error (MAPE) of 25.676. Similarly, the temporal transformer model exhibited a notable performance, with an MAE of 85.134 and a MAPE of 28.7932. These impressive results were achieved by applying Bayesian hyperparameter optimization, further enhancing the accuracy of predictive methods. These models were bench-marked against existing approaches, demonstrating superior results with an improvement in MAPE ranging from 4.01% to 7.12%.

Jellyfish optimized recurrent neural network for state of health estimation of lithium-ion batteries

The state of health (SOH) of lithium-ion batteries is considered an important health indicator to evaluate different features such as efficiency, robustness, and accuracy. The SOH of the lithium-ion battery is investigated to determine the advent of failures to mitigate the battery risk, avoid uneven fault occurrences and ensure safe battery operation. Nonetheless, the accurate assessment of SOH is difficult due to the occurrence of capacity degradation and performance deviation with temperature and aging impacts. Therefore, this work presents an improved hybrid recurrent neural network (RNN) and jellyfish optimization (JFO) to estimate the SOH of lithium-ion batteries. A total of 124 lithium-ion battery cells classified in three batches, namely ‘2017-05-12’, ‘2017-06-30’, and ‘2018-04-12’ from the MIT Stanford- Toyota Research Institute battery database, are used for the proposed work. The systematic sampling method is utilized for developing a 31-dimensional data format. The 31-dimensional data format is developed considering various critical battery parameters such as capacity, voltage, current, and temperature. The JFO is applied to achieve the best suitable RNN model hyperparameters for accurate estimation outcomes. The validation of the hybrid RNN-JFO method is conducted with other JFO-optimized neural network (NN) models, such as feedforward neural network (FFNN) and backpropagation neural network (BPNN). Compared with FFNN-JFO and BPNN-JFO models, the experiment conducted with the RNN-JFO method delivered accurate outcomes. For the batches ‘2017-05-12’, ‘2017-06-30’, and ‘2018-04-12’, the average root mean square error (RMSE) error achieved with the RNN-JFO model was 0.0529, 0.2674, and 0.1638. The SOH estimation outcomes confirm the effectiveness of the RNN-JFO method for SOH estimation on different lithium-ion batteries.

(Invited) All-Solid Battery Interfaces and Research in the Toyota-ECS Young Investigator Fellowship

Advances in hybrid and electric vehicle technologies combined with a demand for green initiatives have motivated necessary diversification in energy storage research. To achieve customer expectations for hybrid and electric vehicles, new battery systems with higher energy densities, power densities and cycle life than the current state-of-art are needed. Post Li-ion battery systems, such as solid-state batteries, are at the forefront of research and development for the future of mobility.ECS and the Toyota Research Institute of North America (TRINA), have partnered to create the ECS Toyota Young Investigator Fellowship since 2014. The fellowship supports young faculty members conducting electrochemical research in green energy technology. The purpose of this fellowship is to encourage young professors and scholars to pursue innovative research in sustainability including next generation solid-state batteries.Here, we will show the progress of fundamental research of all-solid-state battery (ASSB) interfaces at TRINA. In addition, we will take a look at the history and relationship of TRINA and the ECS in the Toyota-ECS Young Investigator Fellowship. Throughout the years, the knowledge and relationships built through the Fellowship immensely supports the progress of post lithium-ion batteries. Finally, we show deep appreciation and will highlight key technical achievements of Toyota-ECS Young Investigator Fellowship winners in battery technology. Figure 1

Battery Aging Models Based on High-Current Incremental Capacity in Fast Charging

This paper presents battery aging models based on high-current incremental capacity features in the presence of battery cycling profiles characterized by fast charging conditions. In particular, the main peak area under the incremental capacity graph is proposed as a capacity indicator. A dataset from the Toyota Research Institute is analyzed. Batteries’ cycling data are characterized by various single- or double-step fast charges in constant current to reach 80% of the battery state of charge; the remaining charge process is performed by a 1C charge. Depending on the battery, a linear or logarithmic model was identified as the best suitable for representing the capacity–peak area relationship. The generalization capabilities of the proposed models are evaluated by performing an inference analysis of the fitting results over groups of batteries. Finally, we evaluated the prediction performance of the models by adopting a cross-validation approach.

Fault-Tolerant Framework for State-of-Health Prediction of Lithium Batteries

Accurately predicting the state of health (SoH) of lithium-ion batteries remains one of the top challenges, especially in uncertain environments where feature availability is not guaranteed. The main contribution of this article is to propose a highly available feature fault-tolerant prediction framework capable of predicting the SoH of batteries with a very high accuracy under sensor data unavailability. Previously published studies mitigated the feature incompleteness issue using models that deeply depend on a specific set of features; if one of the features becomes unavailable, the prediction model fails. Unlike the existing methods, the proposed feature fault-tolerant prediction framework proposes a novel agile approach so that when one of the features is missing, the model considers the next available feature to make the prediction. Our fault-tolerant prediction framework is highly available for SoH prediction and always predicts at a very high accuracy level. A hybrid deep learning model combining long short-term memory and deep neural networks along with a meticulous feature engineering design is used to train the NASA dataset and predict the SoH of batteries with a high precision. In order to prove the efficiency of the model, the feature fault-tolerant prediction framework configuration has been validated against two other datasets: Toyota Research Institute in partnership with MIT and Stanford dataset and NASA dynamic profile datasets. The performance is evaluated using the error indicators: mean absolute error, root-mean-square error, and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R$</tex-math></inline-formula> square.

How Robots Can Help US Act and Feel Younger: Toyota's Gill Pratt on Enhancing Independence in Old Age

The number of people over the age of 80 will triple, approaching half a billion. Supporting an aging population is a worldwide concern, but this demographic shift is especially pronounced in Japan, where more than a third of Japanese will be 65 or older by midcentury.• Toyota Research Institute (TRI), which was established by Toyota Motor Corp. in 2015 to explore autonomous cars, robotics, and “human amplification technologies,” has also been focusing a significant portion of its research on ways to help older people maintain their health, happiness, and independence as long as possible. While it's an important goal in itself, improving self-sufficiency for the elderly also reduces the amount of support they need from society more broadly. And without technological help, sustaining this population in an effective and dignified manner will grow increasingly difficult-first in Japan, but globally soon after.• Gill Pratt, Toyota's chief scientist and the CEO of TRI, believes that robots have a significant role to play in assisting older people by solving physical problems as well as providing mental and emotional support. With a background in robotics research and five years as a program manager at the Defense Advanced Research Projects Agency, during which time he oversaw the DARPA Robotics Challenge in 2015, Pratt understands how difficult it can be to bring robots into the real world in a useful, responsible, and respectful way. In an interview earlier this year in Washington, D.C., with IEEE Spectrum's Evan Ackerman, he said that the best approach to this problem is a human-centric one: “It's not about the robot, it's about people.”

Toward autonomous materials research: Recent progress and future challenges

The modus operandi in materials research and development is combining existing data with an understanding of the underlying physics to create and test new hypotheses via experiments or simulations. This process is traditionally driven by subject expertise and the creativity of individual researchers, who “close the loop” by updating their hypotheses and models in light of new data or knowledge acquired from the community. Since the early 2000s, there has been notable progress in the automation of each step of the scientific process. With recent advances in using machine learning for hypothesis generation and artificial intelligence for decision-making, the opportunity to automate the entire closed-loop process has emerged as an exciting research frontier. The future of fully autonomous research systems for materials science no longer feels far-fetched. Autonomous systems are poised to make the search for new materials, properties, or parameters more efficient under budget and time constraints, and in effect accelerate materials innovation. This paper provides a brief overview of closed-loop research systems of today, and our related work at the Toyota Research Institute applied across different materials challenges and identifies both limitations and future opportunities.

Remaining Useful Life Estimation for LFP Cells in Second-Life Applications

The increasing deployment of battery storage applications in both grid storage and electric vehicle fields is generating a vast used battery market. These batteries are typically recycled but could be reused in second-life applications. One of the challenges is to obtain an accurate remaining useful life (RUL) estimation algorithm, which determines whether a battery is suitable for reuse and estimates the number of second-life cycles the battery will last. In this article, the RUL estimation problem is considered. We propose several health indicators (HIs), some of which have not been explored before, along with simple yet effective estimation and classification algorithms. These algorithms include classification techniques such as regularized logistic regression (RLR), and regression techniques such as multivariable linear regression (MLR) and multilayer perceptron (MLP). As a more advanced solution, a multiple expert system combining said techniques is proposed. The performance of the algorithms and features is evaluated on a recent lithium iron phosphate (LFP) data set from Toyota Research Institute. We obtain satisfactory results in the estimation of RUL cycles with errors down to 49 root mean square error (RMSE) cycles for cells that live up to 1200 cycles, and 0.24% mean relative error (MRE) for the prediction of the evolution of capacity.

Empirical Study of Toyota Motor Corporation Relative to the Good to Great Framework

Best-selling business bookGood to Great was published in 2001 as the result of an effort to understand what characteristics, if any, companies who experience an extended run of greatness have in common compared to companies who do not. The resulting seven-component framework of Good to Great has brought the book both wild acclaim in management circles and heavy scrutiny in the research arena. While the book originally studied only American companies, this research will examine Good to Great’s research methodology and definition of “great” in order to compare the framework to Toyota Motor Corporation. A consistent tenant in Fortune’s Global 500 top 10, Toyota is arguably one of the most successful companies in the world, showing a growth that has been remarkably steady for almost 80 years. This paper examines empirical data and evidence from Toyota research and analyses the degree of fit relative to the Good to Great framework. The outcome of the paper offers evidence to support Good to Great framework by putting it on trial against a large international organization.

The Pransky interview: Dr James Kuffner, CEO at Toyota Research Institute Advanced Development, Coinventor of the rapidly, exploring random tree algorithm

PurposeThe following article is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD-turned entrepreneur regarding his pioneering efforts of bringing technological inventions to market. The paper aims to discuss these issues.Design/methodology/approachThe interviewee is Dr James Kuffner, CEO at Toyota Research Institute Advanced Development (TRI-AD). Kuffner is a proven entrepreneur and inventor in robot and motion planning and cloud robotics. In this interview, Kuffner shares his personal and professional journey from conceptualization to commercial realization.FindingsDr Kuffner received BS, MS and PhD degrees from the Stanford University’s Department of Computer Science Robotics Laboratory. He was a Japan Society for the Promotion of Science (JSPS) Postdoctoral Research Fellow at the University of Tokyo where he worked on software and planning algorithms for humanoid robots. He joined the faculty at Carnegie Mellon University’s Robotics Institute in 2002 where he served until March 2018. Kuffner was a Research Scientist and Engineering Director at Google from 2009 to 2016. In January 2016, he joined TRI where he was appointed the Chief Technology Officer and Area Lead, Cloud Intelligence and is presently an Executive Advisor. He has been CEO of TRI-AD since April of 2018.Originality/valueDr Kuffner is perhaps best known as the co-inventor of the rapidly exploring random tree (RRT) algorithm, which has become a key standard benchmark for robot motion planning. He is also known for introducing the term “Cloud Robotics” in 2010 to describe how network-connected robots could take advantage of distributed computation and data stored in the cloud. Kuffner was part of the initial engineering team that built Google’s self-driving car. He was appointed Head of Google’s Robotics Division in 2014, which he co-founded with Andy Rubin to help realize the original Cloud Robotics concept. Kuffner also co-founded Motion Factory, where he was the Senior Software Engineer and a member of the engineering team to develop C++ based authoring tools for high-level graphic animation and interactive multimedia content. Motion Factory was acquired by SoftImage in 2000. In May 2007, Kuffner founded, and became the Director of Robot Autonomy where he coordinated research and software consulting for industrial and consumer robotics applications. In 2008, he assisted in the iOS development of Jibbigo, the first on-phone, real-time speech recognition, translation and speech synthesis application for the iPhone. Jibbigo was acquired by Facebook in 2013. Kuffner is one of the most highly cited authors in the field of robotics and motion planning, with over 15,000 citations. He has published over 125 technical papers and was issued more than 50 patents related to robotics and computer vision technology.

Relationship between mechanical strain and chemical composition in LiFePO4 via 4D-scanning transmission electron microscopy and scanning transmission X-ray microscopy

Journal Article Relationship between mechanical strain and chemical composition in LiFePO4 via 4D-scanning transmission electron microscopy and scanning transmission X-ray microscopy Get access LA Hughes, LA Hughes National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA 94708, USA Corresponding author, laurenhughes@lbl.gov Search for other works by this author on: Oxford Academic Google Scholar Benjamin H Savitzky, Benjamin H Savitzky National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA 94708, USA Search for other works by this author on: Oxford Academic Google Scholar Haitao D Deng, Haitao D Deng Depart. of Mat. Sci. & Eng., Stanford University, Stanford, CA 94305, USA Search for other works by this author on: Oxford Academic Google Scholar Norman L Jin, Norman L Jin Depart. of Mat. Sci. & Eng., Stanford University, Stanford, CA 94305, USA Search for other works by this author on: Oxford Academic Google Scholar Eder G Lomeli, Eder G Lomeli Depart. of Mat. Sci. & Eng., Stanford University, Stanford, CA 94305, USA Search for other works by this author on: Oxford Academic Google Scholar William C Chueh, William C Chueh Depart. of Mat. Sci. & Eng., Stanford University, Stanford, CA 94305, USA Search for other works by this author on: Oxford Academic Google Scholar Patrick Herring, Patrick Herring Toyota Research Institute, Los Altos, CA 94022, USA Search for other works by this author on: Oxford Academic Google Scholar Colin Ophus, Colin Ophus National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA 94708, USA Search for other works by this author on: Oxford Academic Google Scholar Andrew M Minor Andrew M Minor National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA 94708, USADepart. of Mat. Sci. & Eng., University of California, Berkeley, CA 94720, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 25, Issue S2, 1 August 2019, Pages 2068–2069, https://doi.org/10.1017/S1431927619011073 Published: 01 August 2019

Toyota hunts for novel battery materials

Toyota’s research arm, Toyota Research Institute, will invest about $35 million over the next four years to develop advanced battery materials and fuel-cell catalysts. Toyota has been a leader in developing hybrid electric cars such as the Prius, but in the full-electric car market, its battery technology trails that of Tesla. Toyota hopes to close the gap with research that merges computational materials modeling, new sources of experimental data, machine learning, and artificial intelligence. Its goal is to reduce the time it takes to develop new materials. Toyota’s move to use computer technology for developing novel chemistries follows BASF’s recent decision to buy a supercomputer to accelerate research in fields such as catalysis. Toyota will collaborate with Stanford University, Massachusetts Institute of Technology, the University of Michigan, the University at Buffalo, the University of Connecticut, and the U.K. materials science company Ilika. Ilika has been working with Toyota on solid-state

Toyota's Ken Laberteaux Talks about the Road Ahead

In this interview, Ken Laberteaux, senior principal scientist for the Toyota Research Institute-North America, discusses topics ranging from his early work on wireless communications and Vanet, to finding the right balance between convenience and privacy, to addressing the challenges of automated driving. This department is part of a special issue on smart vehicle spaces.

Quest for nonaqueous multivalent secondary batteries: magnesium and beyond.

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTQuest for Nonaqueous Multivalent Secondary Batteries: Magnesium and BeyondJohn Muldoon*†, Claudiu B. Bucur†, and Thomas Gregory‡View Author Information† Toyota Research Institute of North America, 1555 Woodridge Avenue, Ann Arbor, Michigan 48105, United States‡ Borealis Technology Solutions LLC, Midland, Michigan 48642, United States*Phone: 734-995-4403. E-mail: [email protected]Cite this: Chem. Rev. 2014, 114, 23, 11683–11720Publication Date (Web):October 24, 2014Publication History Received28 January 2014Published online24 October 2014Published inissue 10 December 2014https://doi.org/10.1021/cr500049yCopyright © 2014 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views23818Altmetric-Citations903LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit Read OnlinePDF (13 MB) Get e-AlertsSUBJECTS:Batteries,Electrodes,Electrolytes,Magnesium,Materials Get e-Alerts

Percolation-Driven Multiscale Roughening for Superhydrophobic Polymer Nanocomposite Coatings

ADVERTISEMENT RETURN TO ISSUEPREVCommunication to the...Communication to the EditorNEXTPercolation-Driven Multiscale Roughening for Superhydrophobic Polymer Nanocomposite CoatingsShane E. Harton*†, Cynthia G. Templeman‡, and Brenda Vyletel§View Author Information† Materials Research Department, Toyota Research Institute of North America, Toyota Technical Center, Ann Arbor, Michigan 48105‡ Materials Engineering Division, Toyota Technical Center, Ann Arbor, Michigan 48105§ Electron Microbeam Analysis Laboratory, University of Michigan, Ann Arbor, Michigan 48109*To whom correspondence should be addressed. E-mail: [email protected]Cite this: Macromolecules 2010, 43, 7, 3173–3176Publication Date (Web):March 10, 2010Publication History Received7 January 2010Revised5 March 2010Published online10 March 2010Published inissue 13 April 2010https://doi.org/10.1021/ma100044qCopyright © 2010 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views1260Altmetric-Citations18LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit Read OnlinePDF (3 MB) Get e-AlertsSupporting Info (1)»Supporting Information Supporting Information SUBJECTS:Coating materials,Hydrophobicity,Nanocomposites,Nanoparticles,Surface roughness Get e-Alerts

Experimental analysis on tensile properties of FRP with nano clay

The field of fiber-reinforced plastic (FRP) nanocomposites is stimulating both fundamental and applied research because these nanoscale materials often exhibit mechanical, physical, and chemical properties that are dramatically different from conventional microcomposites. A large number of nanoparticles, layered fibers, and polymeric whiskers are being used for the preparation of nanocomposites. Since the Toyota research group’s pioneering work on nylon 6/layered silicate nanocomposites, polymer nanocomposites containing layered silicates have attracted much attention. These nanocomposites can exhibit increased modulus, decreased thermal expansion coefficient, increased solvent resistance, and enhanced ionic conductivity as compared to the polymer alone. Here, we have prepared FRP with nanocomposites in the combination of polyester resin, E-glass fiber, and nano-montmorillonite (nano clay). The mechanical properties of the nanocomposites were studied. It results in increase of tensile strength, percent elongation and yield strength, moderate increase in Poisson ratio, and reduced area.

A short review on rubber/clay nanocomposites with emphasis on mechanical properties

AbstractThe invention of Nylon‐6/clay nanocomposites by the Toyota Research Group of Japan heralded a new chapter in the field of polymer composites. This article highlights the work done in the field of rubber/clay nanocomposites. The preparations of rubber/clay nanocomposites by solution blending, latex compounding, and melt intercalation are covered and a thorough discussion of the mechanical properties of the various rubber/clay nanocomposite systems is presented. Other properties such as barrier, dynamic mechanical behavior, and thermal properties are also discussed. Finally, the future trends in the rubber/clay nanocomposites are mentioned. POLYM. ENG. SCI., 47:1956–1974, 2007. © 2007 Society of Plastics Engineers

Effects of production parameters on the structure of resol type phenolic resin/layered silicate nanocomposites

Polymer/layered silicate nanocomposites belong to one of the most promising group of materials of the past few decades and most probably for the near future. Following the pioneering works of Toyota Research Group in the 1980s, the interest on these materials increased rapidly and research is now being carried out world wide, using all kinds of polymers as base material. In this present study, the aim was to investigate the effects of several different production parameters; on the morphology of resol type phenol formaldehyde based layered silicate nanocomposites produced by mixing and casting. For this purpose; two different liquid resol type phenolic resins (PF76 and PF76TD), two different curing methods (heat cure route and acid cure route), two different montmorillonite clays (unmodified Cloisite Na+ and modified Rheospan), two different clay sources (Wyoming-USA and Tokat-Turkey), and five different clay amounts (0.5%, 1%, 1.5%, 3%, 10%) were used. XRD, SEM, TEM analyses and mechanical tests indicated that resol type phenolic resins lead to better structures when they were modified with ethylene glycol and cured by the use of an acidic curing agent. It was also observed that use of modified clay with no more than 1.5 wt% in the phenolic matrix lead to certain degree of exfoliation consequently better structure and higher mechanical performance.

New heat treatment methods for glass removal from silicon nitride and sialon ceramics

Sialon ceramics were discovered simultaneously (but independently) in late 1971 at Newcastle University and also at the Toyota Research Laboratories in Japan. During the 30 years since their original discovery, the Newcastle laboratory has made a significant contribution to current understanding of the science and technology of these materials. Sialons are of interest as engineering materials for high temperature (>1000°C) applications because they can be pressureless-sintered to high density and be designed to retain good mechanical properties even up to ≈1350°C, whereas competing metallic materials are weaker and prone to corrosion. A characteristic disadvantage of all nitrogen ceramics is that an oxide additive is always included in the starting mix to promote densification, and this remains in the final product as a glassy phase distributed throughout the grain boundaries of the final microstructure. Since the glass melts at ≈1000°C, the high temperature properties of the final ceramic are in fact determined by the properties of the grain-boundary glass. The most common method of improving high-temperature performance is to heat-treat the material at temperatures of 1100–1350°C in order to devitrify the glass into a mixture of crystalline phases. More specifically it is desirable to convert the glass into a sialon phase plus only one other crystalline phase, the latter having a high melting point and also displaying a high eutectic temperature (max ≈1400°C) in contact with the matrix sialon phase. Previous studies have shown that there are a limited number of possible metal-silicon-aluminium-oxygen-nitrogen compounds which satisfy these requirements. The present paper gives an overall review of this subject area and then summarises recent work at Newcastle aimed at total removal of residual grain boundary glass. This has been achieved by: (1) a post-preparative vacuum heat treatment process to remove the grain boundary glass from silicon nitride based ceramics in gaseous form, (2) above-eutectic heat-treatment (AET) of sialon-based ceramics to crystallize grain-boundary liquid into five-component crystalline sialon phases.

The titanium image: Ascending to new heights

Image is what it is all about; this applies equally to the latest and greatest soap flakes as well as golf clubs. As Marc Antony said, “so let it be with Caesar” (or, with apologies to Shakespeare, titanium). In the past few years, we have seen the titanium image ascend to new heights. Here are a few examples: • An expensive wine, 100 Château Margaux 1995 ($200–290 per bottle) is described as “absolutely stunning, with masses of raspberry and violet aromas and flavors and a titanium girdle of tannis.” • ARTnews describes the Guggenheim Museum in Bilbao as the “Art of the Century,” a masterpiece by Frank Gehry consisting of titanium and stone. • Tactical Knives describes the new titanium mission knives as “taking the legendary combat-proven multipurpose knife to new levels of excellence.” • To some, the ultimate accolade— the name of a men’s club in Honolulu, Hawaii (dutifully visited last summer), was changed from Totally Gold to Totally Titanium. So, titanium is no longer an exotic aerospace material to which the general public cannot relate. Even cartoons in the newspaper use titanium as something of a standard. For example, in the Sally Forth cartoon, Sally refer to the ultimate in husbands as a “titanium husband” (while he is working below the sink to get the garbage disposal working—happy memories!). Forget steel, aluminum, and magnesium—the titan metal is up there with the gods of gold, silver, and platinum (the name of a gentlemen’s club in San Diego). While titanium has expanded into many new markets, the traditional aerospace market has attained new levels in the commercial segment, with a recent hiccup of cutbacks in Boeing orders due to the economic problems in Asia. We have even touched the “holy grail” of getting titanium into the family automobile; albeit outside the United States. Due to the excellent work of Takashi Saito of Toyota Research and DevelopThe Titanium Image: Ascending to New Heights