The next generation innovation will be driven by affordability and sustainability. Manufacturers are implementing energy efficiency and sustainable manufacturing practices not only from a cost reduction perspective but as a business strategy to succeed. This paper presents a bottom-up methodology for autonomous real-time energy consumption analytics at multiple level of abstraction from the lowest machining operation to enterprise level using MTConnect. The temporal aspect of data enables correlation of machining parameters and energy consumption at the most granular component feature and operation level. Decision support capability to drive part cost reduction and assessing the effect of adding or modifying a certain feature, operation or quality constraint is explained with help of a case study. Cost structure transparency and ability to justify improvement changes presents a significant opportunity for improved productivity and energy efficiency. Utilizing smart machine analysis tools, return-on-investment is quantified for production of an aerospace component on two machines.
Reference: Feature Level Energy Assessments for Discrete Part Manufacturing; Deshpande A., Snyder J., Scherrer D.; Proceedings of NAMRI/SME, Vol. 39, 2011, Corvallis, OR.
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