Machining strategy development in 5-axis milling operations using process models

Increased productivity and part quality can be achieved by selecting machining strategies and conditions properly. At one extreme very high speed and feed rate with small depth of cut can be used for high productivity whereas deep cuts accompanied with slow speeds and feeds may also provide increased material removal rates in some cases. In this study, it is shown that process models are useful tools to simulate and compare alternative strategies for machining of a part. 5-axis milling of turbine engine compressors made out of titanium alloys is used as the case study where strategies such as flank milling (deep cuts), point milling (light cuts) and stripe milling (medium depths) are compared in terms of process time by considering chatter stability, surface finish and tool deflections

Modeling dynamics of parallel milling processes in time-domain

The use of parallel milling processes is increasing in various industries due to several advantages of these machine tools. Parallel milling processes are the processes where more than one milling tool simultaneously cut a workpiece. Due to the increased number of cutting tools, they have the potential for considerable increase in productivity as a result of higher material removal rate (MRR). However, dynamic interactions between milling tools may reduce stability limits. Generally, direct dynamic coupling between two milling tools on such a machine is weak since they are located on different spindles. However, there can be a strong dynamic coupling in case of milling a flexible workpiece. In this case, the vibrations caused by one of the tools may have regenerative effects on the other one. In order to address this problem, a stability model that works in time domain has been developed. The model is capable of simulating cases where two flexible milling tools are cutting a flexible workpiece. Several example cases are simulated with the model and results are presented

Absolute electrical impedance tomography (aEIT) guided ventilation therapy in critical care patients: simulations and future trends

Thoracic electrical impedance tomography (EIT) is a noninvasive, radiation-free monitoring technique whose aim is to reconstruct a cross-sectional image of the internal spatial distribution of conductivity from electrical measurements made by injecting small alternating currents via an electrode array placed on the surface of the thorax. The purpose of this paper is to discuss the fundamentals of EIT and demonstrate the principles of mechanical ventilation, lung recruitment, and EIT imaging on a comprehensive physiological model, which combines a model of respiratory mechanics, a model of the human lung absolute resistivity as a function of air content, and a 2-D finite-element mesh of the thorax to simulate EIT image reconstruction during mechanical ventilation. The overall model gives a good understanding of respiratory physiology and EIT monitoring techniques in mechanically ventilated patients. The model proposed here was able to reproduce consistent images of ventilation distribution in simulated acutely injured and collapsed lung conditions. A new advisory system architecture integrating a previously developed data-driven physiological model for continuous and noninvasive predictions of blood gas parameters with the regional lung function data/information generated from absolute EIT (aEIT) is proposed for monitoring and ventilator therapy management of critical care patients

A DYNAMIC MODEL OF VERTICAL INTEGRATION FOR THE AMERICAN PULP AND PAPER INDUSTRY

The focus of this research is to learn about the factors that influence the decision of a manufacturing firm to vertically integrate into the production of its input. The American paper industry has a feature that makes it particularly suitable for this purpose: over the years paper mills of apparently similar characteristics have made different decisions with regards to their integration status. This work draws on the insight that there must be some unobserved mill characteristic that drives the decision process for a mill. Mills´ choices of whether to exit the industry, and with regards to their integration status when they choose to stay in operation, depend on their productivity. This generates selection and simultaneity biases in a reduced form estimation. In order to deal with these issues, I propose a dynamic model in the spirit of Olley and Pakes (1996). This approach not only takes care of the estimation biases, but also allows me to learn about the unobserved characteristics of the firms in my data, and to use them to determine which firms vertically integrate and which firms do not. In addition, the model I propose allows me to learn about how vertical integration affects productivity and mill´s entry and exit decisions.Vertical Integration

Models Needed to Assist in the Development of a National Fiber Supply Strategy for the 21st Century: Report of a Workshop

This discussion paper reports on a Workshop on Wood Fiber Supply Modeling held October 3-4, 1996 in Washington, DC. The purpose of this discussion paper is to provide an overview of some of the modeling work being done related to timber supply modeling and some of the issues related to the more useful application of wood fiber supply and projections models. This paper includes brief presentations of three commonly used long-term timber projections and forecasting models: the Timber Assessment Market Model (TAMM) of the Forest Service; the Cintrafor Global Trade Model (CGTM) of the University of Washington; and the Timber Supply Model (TSM) of Resources for the Future. Also, issues related to the useful of the models are addressed as well as a discussion of some applications of other timber or fiber projection models. The usefulness of the models are addressed from both a technical perspective and also from the perspective of their usefulness to various model users.

ABSTRACTS, PAPERS PRESENTED, ANNUAL MEETING, SAEA, TULSA, OKLAHOMA, FEBRUARY 1993

Teaching/Communication/Extension/Profession,

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain