Abstract: To decrease the grating scale thermal expansion error,
a novel method which based on multiple temperature detection is
proposed. Several temperature sensors are installed on the grating
scale and the temperatures of these sensors are recorded. The
temperatures of every point on the grating scale are calculated by
interpolating between adjacent sensors. According to the thermal
expansion principle, the grating scale thermal expansion error model
can be established by doing the integral for the variations of position
and temperature. A novel compensation method is proposed in this
paper. By applying the established error model, the grating scale
thermal expansion error is decreased by 90% compared with no
compensation. The residual positioning error of the grating scale is
less than 15μm/10m and the accuracy of the machine tool is
significant improved.
Abstract: Design for cost (DFC) is a method that reduces life
cycle cost (LCC) from the angle of designers. Multiple domain
features mapping (MDFM) methodology was given in DFC. Using
MDFM, we can use design features to estimate the LCC. From the
angle of DFC, the design features of family cars were obtained, such
as all dimensions, engine power and emission volume. At the
conceptual design stage, cars- LCC were estimated using back
propagation (BP) artificial neural networks (ANN) method and
case-based reasoning (CBR). Hamming space was used to measure the
similarity among cases in CBR method. Levenberg-Marquardt (LM)
algorithm and genetic algorithm (GA) were used in ANN. The
differences of LCC estimation model between CBR and artificial
neural networks (ANN) were provided. ANN and CBR separately
each method has its shortcomings. By combining ANN and CBR
improved results accuracy was obtained. Firstly, using ANN selected
some design features that affect LCC. Then using LCC estimation
results of ANN could raise the accuracy of LCC estimation in CBR
method. Thirdly, using ANN estimate LCC errors and correct errors in
CBR-s estimation results if the accuracy is not enough accurate.
Finally, economically family cars and sport utility vehicle (SUV) was
given as LCC estimation cases using this hybrid approach combining
ANN and CBR.