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A D V A N C E D M A T E R I A L S & P R O C E S S E S | J A N U A R Y 2 0 1 8 2 6 real world is envisioned. This technol- ogy trend is characterized by: (a) phys- ical and virtual connectivity across the product development cycle; (b) mul- tifunctional synergies including engi- neering, manufacturing, quality, and customer support; and (c) higher-level intelligence from connected databases, products, and process models, which would lead to more informed and au- tonomous decisions with self-learn- ing intelligence developed within the organization [10] . Examples within ma- terials engineering include analytics across the product cycle, proactive maintenance of equipment and assets, control of specific quality issues, auto- mation of recipe selection, and process optimization [10] . Further, with the available tech- nology, specific components can be tracked across the production chain, which provides a lever to fine-tune pro- cess parameters. This enables signifi- cant quality control, such as controlling distortion in a specific gear by track- ing and fine-tuning process set-points across the production chain. It is ex- pected that a higher level of efficiency and quality will be achieved through customized solutions at the compo- nent or machine level, better leverage of networked production across the supply chain, and proactive equipment maintenance. Autonomous and electric vehicles. Autonomous vehicles will have limited implications for materials engineers, except from the passenger and pedes- trian safety viewpoint. Maturity in au- tonomous vehicles would significantly drive design changes, with indirect im- plications for materials engineering. On the other hand, electric vehicles have far-reaching implications for ma- terials engineering. First, the materi- als palette of an electric vehicle would be significantly different than for a conventional gasoline engine vehicle. Engine and drivetrain materials and the associated technologies (e.g., gear ma- terials, gear processing, piston and cyl- inder, heat treatment, machining, and surface engineering), which are signif- icant contributors to current gasoline vehicles, will become redundant. In comparison, electricmotors and batteries form the core of electric vehi- cles. Issues in the supply chain of stra- tegic materials such as lithium, cobalt, rare-earth elements, and copper would be key drivers of design and technology decisions. Currently, 45% of the world’s cobalt production is used in lithium ion batteries, which has resulted in signif- icant price hikes in cobalt during the past year. Research efforts to develop successful alternatives are ongoing. SUMMARY Although technology trends are discipline agnostic, most of today’s trends having implications for materi- als engineering (Table 1). It is import- ant for engineers to understand these trends and leverage them appropriate- ly to drive priorities. An in-depth un- derstanding of trends not only keeps materials engineers relevant, but also puts them in the driver’s seat [11] when it comes to formulating strategies, de- fining use cases, developing solution roadmaps, executing projects, and tran- sitioning technology-led projects for business value. ~AM&P For more information: Satyam S. Sa- hay is a John Deere Fellow, materials engineering, John Deere Technology Center India, Tower 14, Cybercity, Mag- arpatta City, Pune, India 411013. satyamsahay@yahoo.com. References 1. S.S. Sahay and C.P. Malhotra, Cost Model for Gas Carburizing, Heat Treating Progress, Vol 2, No. 2, p 29-32, 2002. 2. S.S. Sahay and K. Mitra, Cost Model Based Optimization of Carburizing Op- eration, Surface Engineering, Vol 20 (5), p 379-384, 2004. 3. U. Tewary, G. Mohapatra, and S.S. Sahay, Distortion Mechanisms during Carburizing and Quenching in a Transmission Shaft, J. Mater. Eng. Perform., Vol 26, No. 10, p 4890-4901, 2017. 4. S.S. Sahay, V. Deshmukh, and M. El-Zein, A Probabilistic Approach to Examine the Effect of Chemistry Vari- ations on Distortion During Industrial Gas Carburizing, J. Mater. Eng. Perform., Vol 22, Issue 7, p 1855-1860, 2013. 5. S.S. Sahay, et al., Analytics, Model- ing and Optimization of Industrial Heat Treating Processes, Adv. Mater. & Proc., p 44-47, March 2014. 6. S.S. Sahay, et al., Model-based Optimization of a Highly Automated Industrial Batch Annealing Operation, Ironmak. Steelmak., Vol 33, No.4, p 306-314 , 2006. 7. S.S. Sahay, et al., BAF tinplate Pro- cess Analytics, Modeling, and Optimi- zation of an Industrial Batch Annealing Operation, Mater. and Manuf. Proc., Vol 24, p. 1459-1466, 2009. 8. D. Pal, A. Datta, and S.S. Sahay, An Efficient Model for Batch Annealing Using a Neural Network, Mater. Manuf. Process., Vol 21, No. 5, p 556-561, 2006. 9. R. Mehta, et al., Neural Network Models for Industrial Batch Anneal- ing Operation, Mater. Manuf. Process., Vol 23, p 204-209, 2008. 10. S.S. Sahay, Industry 4.0 Meets Heat Treating, Adv. Mater. & Proc., p 59-61, Nov/Dec 2016. 11. S.S. Sahay, Careers in Materials Engineering: Leadership Roles for Ma-— terials Engineers Steadily Evolving: Are You Ready? Adv. Mater. & Proc., p 22-26, April 2017.