Application of multi sensor data fusion based on Principal Component Analysis and Artificial Neural Network for machine tool thermal monitoring

Due to the various heat sources on a machine tool, there exists a complex temperature distribution across its structure. This causes an inherent thermal hysteresis which is undesirable as it affects the systematic tool –to-workpiece positioning capability. To monitor this, two physical quantities (temperature and strain) are measured at multiple locations. This article is concerned with the use of Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) to fuse this potentially large amount of data from multiple sources. PCA reduces the dimensionality of the data and thus reduces training time for the ANN which is being used for thermal modelling. This paper shows the effect of different levels of data compression and the application of rate of change of sensor values to reduce the effect of system hysteresis. This methodology has been successfully applied to the ram of a 5-axis gantry machine with 90 % correlation to the measured displacement

HOST structural analysis program overview

Hot-section components of aircraft gas turbine engines are subjected to severe thermal structural loading conditions, especially during the startup and takeoff portions of the engine cycle. The most severe and damaging stresses and strains are those induced by the steep thermal gradients induced during the startup transient. These transient stresses and strains are also the most difficult to predict, in part because the temperature gradients and distributions are not well known or readily predictable and, in part, because the cyclic elastic-viscoplastic behavior of the materials at these extremes of temperature and strain are not well known or readily predictable. A broad spectrum of structures related technology programs is underway to address these deficiencies at the basic as well as the applied level. The three key program elements in the HOST structural analysis program are computations, constitutive modeling, and experiments for each research activity. Also shown are tables summarizing each of the activities

Demonstration of capabilities of high temperature composites analyzer code HITCAN

The capabilities a high temperature composites analyzer code, HITCAN which predicts global structural and local stress-strain response of multilayered metal matrix composite structures, are demonstrated. The response can be determined both at the constituent (fiber, matrix, and interphase) and the structure level and includes the fabrication process effects. The thermo-mechanical properties of the constituents are considered to be nonlinearly dependent on several parameters including temperature, stress, and stress rate. The computational procedure employs an incremental iterative nonlinear approach utilizing a multifactor-interactive constituent material behavior model. Various features of the code are demonstrated through example problems for typical structures

Ceramic component reliability with the restructured NASA/CARES computer program

The Ceramics Analysis and Reliability Evaluation of Structures (CARES) integrated design program on statistical fast fracture reliability and monolithic ceramic components is enhanced to include the use of a neutral data base, two-dimensional modeling, and variable problem size. The data base allows for the efficient transfer of element stresses, temperatures, and volumes/areas from the finite element output to the reliability analysis program. Elements are divided to insure a direct correspondence between the subelements and the Gaussian integration points. Two-dimensional modeling is accomplished by assessing the volume flaw reliability with shell elements. To demonstrate the improvements in the algorithm, example problems are selected from a round-robin conducted by WELFEP (WEakest Link failure probability prediction by Finite Element Postprocessors)

Conceptual modelling: Towards detecting modelling errors in engineering applications

Rapid advancements of modern technologies put high demands on mathematical modelling of engineering systems. Typically, systems are no longer “simple” objects, but rather coupled systems involving multiphysics phenomena, the modelling of which involves coupling of models that describe different phenomena. After constructing a mathematical model, it is essential to analyse the correctness of the coupled models and to detect modelling errors compromising the final modelling result. Broadly, there are two classes of modelling errors: (a) errors related to abstract modelling, eg, conceptual errors concerning the coherence of a model as a whole and (b) errors related to concrete modelling or instance modelling, eg, questions of approximation quality and implementation. Instance modelling errors, on the one hand, are relatively well understood. Abstract modelling errors, on the other, are not appropriately addressed by modern modelling methodologies. The aim of this paper is to initiate a discussion on abstract approaches and their usability for mathematical modelling of engineering systems with the goal of making it possible to catch conceptual modelling errors early and automatically by computer assistant tools. To that end, we argue that it is necessary to identify and employ suitable mathematical abstractions to capture an accurate conceptual description of the process of modelling engineering systems

Development of a structural parameter estimation program for finite element model updating

The condition of America\u27s infrastructure is highlighted by major collapses and overcrowded roadways remind us that our infrastructure is aging and in need of effective maintenance. The American Society of Civil Engineers report card for 2009 graded the nation\u27s bridges as a C. In this period of renovation, rebuilding and limited funding, it is important to use the latest technologies to help make America\u27s roadways safe and establish efficient management protocols. This research develops a program for the purpose of pairing structural health monitoring systems with the power of structural modeling, for the use of model updating and parameter estimation, can help to create a smarter and more efficient method of bridge health monitoring and management. A current and accurate analytical bridge model can help owners assess structural needs as they arise. A first step towards this goal is the creation of a program that utilizes field measurements, bridge inspection reports, analytical structural modeling and the powerful computer based structural model updating methods for bridge condition assessment

Finite element formulation to study thermal stresses in nanoencapsulated phase change materials for energy storage

Nanoencapsulated phase change materials (nePCMs) – which are composed of a core with a phase change material and of a shell that envelopes the core – are currently under research for heat storage applications. Mechanically, one problem encountered in the synthesis of nePCMs is the failure of the shell due to thermal stresses during heating/cooling cycles. Thus, a compromise between shell and core volumes must be found to guarantee both mechanical reliability and heat storage capacity. At present, this compromise is commonly achieved by trial and error experiments or by using simple analytical solutions. On this ground, the current work presents a thermodynamically consistent and three-dimensional finite element (FE) formulation considering both solid and liquid phases to study thermal stresses in nePCMs. Despite the fact that there are several phase change FE formulations in the literature, the main novelty of the present work is its monolithic coupling – no staggered approaches are required – between thermal and mechanical fields. Then, the FE formulation is implemented in a computational code and it is validated against one-dimensional analytical solutions. Finally, the FE model is used to perform a thermal stress analysis for different nePCM geometries and materials to predict their mechanical failure by using Rankine’s criterion

Model Based Prediction Approach for Internal Machine Tool Heat Sources on the Level of Subsystems

AbstractModern machine tools are highly sophisticated mechatronic systems. Each subsystem's energy efficiency is important regarding thermal effects of the machine: Losses in the subsystems are mainly heat sources, causing temperature gradients and thermal elongation. Knowledge about the internal heat sources is therefore mandatory for high precision machining, as well as for the design of compensation strategies. This paper presents a modeling approach to estimate the heat release of machine tool subsystems and predict boundary conditions for thermal models. The simulation results are verified by measurements on an internal cooling system of a lathe.Video abstrac