49,272 research outputs found
Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms
The authors propose the implementation of hybrid Fuzzy Logic-Genetic
Algorithm (FL-GA) methodology to plan the automatic assembly and disassembly
sequence of products. The GA-Fuzzy Logic approach is implemented onto two
levels. The first level of hybridization consists of the development of a Fuzzy
controller for the parameters of an assembly or disassembly planner based on
GAs. This controller acts on mutation probability and crossover rate in order
to adapt their values dynamically while the algorithm runs. The second level
consists of the identification of theoptimal assembly or disassembly sequence
by a Fuzzy function, in order to obtain a closer control of the technological
knowledge of the assembly/disassembly process. Two case studies were analyzed
in order to test the efficiency of the Fuzzy-GA methodologies
Suitably graded THB-spline refinement and coarsening: Towards an adaptive isogeometric analysis of additive manufacturing processes
In the present work we introduce a complete set of algorithms to efficiently
perform adaptive refinement and coarsening by exploiting truncated hierarchical
B-splines (THB-splines) defined on suitably graded isogeometric meshes, that
are called admissible mesh configurations. We apply the proposed algorithms to
two-dimensional linear heat transfer problems with localized moving heat
source, as simplified models for additive manufacturing applications. We first
verify the accuracy of the admissible adaptive scheme with respect to an
overkilled solution, for then comparing our results with similar schemes which
consider different refinement and coarsening algorithms, with or without taking
into account grading parameters. This study shows that the THB-spline
admissible solution delivers an optimal discretization for what concerns not
only the accuracy of the approximation, but also the (reduced) number of
degrees of freedom per time step. In the last example we investigate the
capability of the algorithms to approximate the thermal history of the problem
for a more complicated source path. The comparison with uniform and
non-admissible hierarchical meshes demonstrates that also in this case our
adaptive scheme returns the desired accuracy while strongly improving the
computational efficiency.Comment: 20 pages, 12 figure
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Design of an instrumented smart cutting tool and its implementation and application perspectives
This paper presents an innovative design of a smart cutting tool, using two surface acoustic wave (SAW) strain sensors mounted onto the top and the side surface of the tool shank respectively, and its implementation and application perspectives. This surface acoustic wave-based smart cutting tool is capable of measuring the cutting force and the feed force in a real machining environment, after a calibration process under known cutting conditions. A hybrid dissimilar workpiece is then machined using the SAW-based smart cutting tool. The hybrid dissimilar material is made of two different materials, NiCu alloy (Monel) and steel, welded together to form a single bar; this can be used to simulate an abrupt change in material properties. The property transition zone is successfully detected by the tool; the sensor feedback can then be used to initiate a change in the machining parameters to compensate for the altered material properties.The UK Technology Strategy Board (TSB) for supporting this research (SEEM Project, contract No. BD266E
Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models
We derive new theoretical results on the properties of the adaptive least
absolute shrinkage and selection operator (adaptive lasso) for time series
regression models. In particular, we investigate the question of how to conduct
finite sample inference on the parameters given an adaptive lasso model for
some fixed value of the shrinkage parameter. Central in this study is the test
of the hypothesis that a given adaptive lasso parameter equals zero, which
therefore tests for a false positive. To this end we construct a simple testing
procedure and show, theoretically and empirically through extensive Monte Carlo
simulations, that the adaptive lasso combines efficient parameter estimation,
variable selection, and valid finite sample inference in one step. Moreover, we
analytically derive a bias correction factor that is able to significantly
improve the empirical coverage of the test on the active variables. Finally, we
apply the introduced testing procedure to investigate the relation between the
short rate dynamics and the economy, thereby providing a statistical foundation
(from a model choice perspective) to the classic Taylor rule monetary policy
model
Semiconductor manufacturing simulation design and analysis with limited data
This paper discusses simulation design and analysis for Silicon Carbide (SiC) manufacturing operations management at New York Power Electronics Manufacturing Consortium (PEMC) facility. Prior work has addressed the development of manufacturing system simulation as the decision support to solve the strategic equipment portfolio selection problem for the SiC fab design [1]. As we move into the phase of collecting data from the equipment purchased for the PEMC facility, we discuss how to redesign our manufacturing simulations and analyze their outputs to overcome the challenges that naturally arise in the presence of limited fab data. We conclude with insights on how an approach aimed to reflect learning from data can enable our discrete-event stochastic simulation to accurately estimate the performance measures for SiC manufacturing at the PEMC facility
Analysis-of-marginal-Tail-Means (ATM): a robust method for discrete black-box optimization
We present a new method, called Analysis-of-marginal-Tail-Means (ATM), for
effective robust optimization of discrete black-box problems. ATM has important
applications to many real-world engineering problems (e.g., manufacturing
optimization, product design, molecular engineering), where the objective to
optimize is black-box and expensive, and the design space is inherently
discrete. One weakness of existing methods is that they are not robust: these
methods perform well under certain assumptions, but yield poor results when
such assumptions (which are difficult to verify in black-box problems) are
violated. ATM addresses this via the use of marginal tail means for
optimization, which combines both rank-based and model-based methods. The
trade-off between rank- and model-based optimization is tuned by first
identifying important main effects and interactions, then finding a good
compromise which best exploits additive structure. By adaptively tuning this
trade-off from data, ATM provides improved robust optimization over existing
methods, particularly in problems with (i) a large number of factors, (ii)
unordered factors, or (iii) experimental noise. We demonstrate the
effectiveness of ATM in simulations and in two real-world engineering problems:
the first on robust parameter design of a circular piston, and the second on
product family design of a thermistor network
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