Abstract

Rotary swaging is an incremental cold forming process for the manufacture of cylindrical light weight components such as axles and steering spindles and has a wide spread use in the automotive industry. The advantage of rotary swaging is the optimal use of work piece material resources. This is due to material strengthening through strain hardening and the ability to manufacture hollow parts with variable wall thicknesses. However, main drawback is the need for an extensive lubrication to control tool wear, to discharge abrasive particles from the forming zone, and to provide adequate work piece surface quality. The subsequent, inevitable cleaning of the work pieces after manufacture leads to a significant increase of costs per unit. Thus, the development of rotary swaging towards a dry process layout is highly innovative both under economic and ecological aspects. The omission of lubricant provokes fundamentally changed tribological conditions in tool-work piece contact, leading to changes in process forces, increased abrasive and adhesive tool wear and reduced work piece quality. A robust, lubricant free rotary swaging, offering the same product quality as a conventional rotary swaging process with the use of lubricant, therefore implies the control of the modified tribological properties by methodologies substituting the tasks of the lubricant. This work shows an interdisciplinary approach for the development of dry rotary swaging, that comprises the FE modeling and simulation of the rotary swaging process for general process understanding and designing; the micro structuring of the rotary swaging tool surfaces for control of friction and process forces; and the development of tungsten doped a-C:H tool hard coatings for the reduction of abrasive and adhesive wear of the tools.

Highlights

  • Big data has gone beyond a conceptual construct to a viable working approach for addressing and making use of huge volumes of data

  • While the use of statistics in healthcare excels its use in other industries, the use of big data techniques has lagged in healthcare [3]

  • There are six key characteristics of big data that impact its use in healthcare

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Summary

Sergio Davalos*

Big data has gone beyond a conceptual construct to a viable working approach for addressing and making use of huge volumes of data. The expectations of big data applications and outcomes in healthcare such as: a 20% decrease in patient mortality, better information regarding patient health and symptoms, reducing readmission, better point of care decision making, integration of smart devices and sensors with data bases, everyday genome sequencing, developing a treatment approach for cancer, and assessing the risk of readmission [1,2,3]. There are six key characteristics of big data that impact its use in healthcare: volume, velocity, variety, veracity, validity, and volatility. These impact the use of big data methods. There are several types of challenges in the use of big data in biostatistics: statistical verifiability, computational load, and technical learning curve

Introduction
Biostatistics and Biometrics Open Access Journal
Full Text
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