Towards a generalized energy prediction model for machine tools.

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

A Classification Scheme for Smart Manufacturing Systems' Performance Metrics.

This paper proposes a classification scheme for performance metrics for smart manufacturing systems. The discussion focuses on three such metrics: agility, asset utilization, and sustainability. For each of these metrics, we discuss classification themes, which we then use to develop a generalized classification scheme. In addition to the themes, we discuss a conceptual model that may form the basis for the information necessary for performance evaluations. Finally, we present future challenges in developing robust, performance-measurement systems for real-time, data-intensive enterprises.

Life-cycle energy demand and global warming potential of computational logic.

Computational logic, in the form of semiconductor chips of the complementary metal oxide semiconductor (CMOS) transistor structure, is used in personal computers, wireless devices, IT network infrastructure, and nearly all modem electronics. This study provides a life-cycle energy analysis for CMOS chips over 7 technology generations with the purpose of comparing energy demand and global warming potential (GWP) impacts of the life-cycle stages, examining trends in these impacts over time and evaluating their sensitivity to data uncertainty and changes in production metrics such as yield. A hybrid life-cycle assessment (LCA) model is used. While life-cycle energy and GWP of emissions have increased on the basis of a wafer or die, these impacts have been reducing per unit of computational power. Sensitivity analysis of the model shows that impacts have the highest relative sensitivity to wafer yield, line yield, and die size and largest absolute sensitivity to the use-phase power demand of the chip.

A hybrid life cycle inventory of nano-scale semiconductor manufacturing.

The manufacturing of modern semiconductor devices involves a complex set of nanoscale fabrication processes that are energy and resource intensive, and generate significant waste. It is important to understand and reduce the environmental impacts of semiconductor manufacturing because these devices are ubiquitous components in electronics. Furthermore, the fabrication processes used in the semiconductor industry are finding increasing application in other products, such as microelectromechanical systems (MEMS), flat panel displays, and photovoltaics. In this work we develop a library of typical gate-to-gate materials and energy requirements, as well as emissions associated with a complete set of fabrication process models used in manufacturing a modern microprocessor. In addition, we evaluate upstream energy requirements associated with chemicals and materials using both existing process life cycle assessment (LCA) databases and an economic input-output (EIO) model. The result is a comprehensive data set and methodology that may be used to estimate and improve the environmental performance of a broad range of electronics and other emerging applications that involve nano and micro fabrication.