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This paper details the lightweighting efforts of the Ford Research & Advanced Transmission team as part of the Multi Material Lightweight Vehicle Project. The Multi Material Lightweight Vehicle (MMLV) developed by Magna International and Ford Motor Company is a result of a US Department of Energy project DE-EE0005574. The project demonstrates the lightweighting potential of a five passenger sedan, while maintaining vehicle performance and occupant safety. Prototype vehicles were manufactured and limited full vehicle testing was conducted. The Mach-I vehicle design, comprised of commercially available materials and production processes, achieved a 364kg (23.5%) full vehicle mass reduction, enabling the application of a 1.0-liter three cylinder engine resulting in a significant environmental benefits and fuel consumption reduction.

A major task of road and airfield maintenance for transportation departments in the Northern United States and in cold regions globally is snow removal. In addition, there is a service industry built on snowplow equipped light trucks to remove snow from vehicle serviceways and parking lots. Thus, a source of stresses on a truck frame are the forces applied by the plow. Unfortunately, very little research has been performed to provide design models that will predict these forces. In this paper, both theoretical and experimental work on developing expressions for snowplow forces will be discussed.

As the pool of existing non-renewable natural resources continues to shrink, it will be necessary for government and industrial leaders to achieve a workable strategy for the intelligent allocation of scarce resources. In this paper, a method of quantifying the environmental and resource impacts of product redesign is proposed. This new method utilizes Input Output Analysis coupled with the Markovian decision making into a single matrix-based tool. The benefit of a fully developed tool would be the ability to make informed pre-production decisions leading to optimum product and process designs with minimal environmental impact. This paper illustrates this technique with an example based upon real industry data and extrapolated effects.