Analysis of trajectory deviation during high speed robotic trimming of carbon-fiber reinforced polymers

Although carbon-fiber reinforced polymers (CFRPs) are used extensively in the aerospace industry, trimming of CFRPs in high speed robotic end milling has however not yet received its due attention within the research community. For such an application, the robot should be very stiff for the machining operation to be generated. If the robot is not sufficiently stiff, deviations in shape and position of the workpieces will occur.     

An intelligent approach for the prediction of surface roughness in ball-end machining of polypropylene

Manufacturing paradigms over the last 150 years have changed from craft production, to mass production and now to mass customisation. One further extension of mass customisation is personalised manufacture, which is the concept of providing bespoke products to the individual consumer. As a result this has brought about the need for a greater degree of sophistication in manufacturing practises and the technologies employed. This bespoke form of manufacture of consumer goods is now being pursued on CNC machining centres as opposed to the alternative of highly expensive rapid prototyping methods. The problem with this form of manufacture is that the products are generally free formed objects which require sophisticated setups and machining. Ball-end machining is a method used to create cusp-type geometry, which is employed on CNC machines to create sculptured surfaces. The objective of this research is to provide a predictive model using a design of experiments strategy to obtain optimised machining parameters for a specific surface roughness in ball-end machining of polypropylene. This paper reports on new manufacturing knowledge to machine polypropylene using ball-end tooling in order to generate personalised sculptured surface products.     

Artificial neural network models for the prediction of surface roughness in electrical discharge machining

In the present paper Artificial Neural Networks (ANNs) models are proposed for the prediction of surface roughness in Electrical Discharge Machining (EDM). For this purpose two well-known programs, namely Matlab^® with associated toolboxes, as well as Netlab^®, were emplo- yed. Training of the models was performed with data from an extensive series of EDM experiments on steel grades; the proposed models use the pulse current, the pulse duration, and the processed material as input parameters. The reported results indicate that the proposed ANNs models can satisfactorily predict the surface roughness in EDM. Moreover, they can be considered as valuable tools for the process planning for EDMachining.     

Prediction of surface roughness in CNC face milling using neural networks and Taguchi''s design of experiments

In this paper, a neural network modeling approach is presented for the prediction of surface roughness (R_a) in CNC face milling. The data used for the training and checking of the networks’ performance derived from experiments conducted on a CNC milling machine according to the principles of Taguchi design of experiments (DoE) method. The factors considered in the experiment were the depth of cut, the feed rate per tooth, the cutting speed, the engagement and wear of the cutting tool, the use of cutting fluid and the three components of the cutting force. Using feedforward artificial neural networks (ANNs) trained with the Levenberg–Marquardt algorithm, the most influential of the factors were determined, again using DoE principles, and a 5×3×1 ANN based on them was able to predict the surface roughness with a mean squared error equal to 1.86% and to be consistent throughout the entire range of values.     

Estimation of cutting forces and surface roughness for hard turning using neural networks

Metal cutting mechanics is quite complicated and it is very difficult to develop a comprehensive model which involves all cutting parameters affecting machining variables. In this study, machining variables such as cutting forces and surface roughness are measured during turning at different cutting parameters such as approaching angle, speed, feed and depth of cut. The data obtained by experimentation is analyzed and used to construct model using neural networks. The model obtained is then tested with the experimental data and results are indicated.     

Automated surface subdivision and tool path generation for -axis CNC machining of sculptured parts

As an innovative and cost-effective method for carrying out multiple-axis CNC machining, -axis CNC machining technique adds an automatic indexing/rotary table with two additional discrete rotations to a regular 3-axis CNC machine, to improve its ability and efficiency for machining complex sculptured parts. In this work, a new tool path generation method to automatically subdivide a complex sculptured surface into a number of easy-to-machine surface patches; identify the favorable machining set-up/orientation for each patch; and generate effective 3-axis CNC tool paths for each patch is introduced. The method and its advantages are illustrated using an example of sculptured surface machining. The work contributes to automated multiple-axis CNC tool path generation for sculptured part machining and forms a foundation for further research.     

Large-scale DES computations of the forward speed diffraction and pitch and heave problems for a surface combatant

This paper aims at presenting the most resolved solutions to date for the ship forward speed diffraction and pitch and heave problems, and discuss the method that enables these computations. Large-scale DES computations (60-115 million grid points, 276-500 processors) of ship hydrodynamics problems are presented for the DTMB model 5512 surface combatant. The forward speed diffraction problem is studied at Fr = 0.28 with waves of amplitude a = 0.006 and wavelength λ=1.5, with the ship static allowing the overset assembly to be a pre-processing step. In the pitch and heave problem the ship faces head waves at Fr = 0.41 with waves of amplitude a = 0.006 and wavelength λ=1.5, with the ship is allowed to pitch and heave, thus requiring dynamic overset grid processing. The code CFDShip-Iowa version 4 and the overset assembly code Suggar were modified to carry out some large scale simulations of free surface ship hydrodynamics. These modifications were focused on reducing the memory requirement and optimizing the per-processor and parallel performance at the implementation and algorithmic levels, plus the addition of a lagged mode for the overset domain connectivity computation. The simulation results show very significant improvements in the local flow and free surface results, but minor in forces and moments when compared with previous URANS computations performed with grids with about three million points.     

The experimental investigation of the effects of uncoated,PVD- and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks

In this study the machining of AISI 1030 steel (i.e. orthogonal cutting) uncoated, PVD- and CVD-coated cemented carbide insert with different feed rates of 0.25, 0.30, 0.35, 0.40 and 0.45 mm/rev with the cutting speeds of 100, 200 and 300 m/min by keeping depth of cuts constant (i.e. 2 mm), without using cooling liquids has been accomplished. The surface roughness effects of coating method, coating material, cutting speed and feed rate on the workpiece have been investigated. Among the cutting tools—with 200 mm/min cutting speed and 0.25 mm/rev feed rate—the TiN coated with PVD method has provided 2.16 μm, TiAlN coated with PVD method has provided 2.3 μm, AlTiN coated with PVD method has provided 2.46 μm surface roughness values, respectively. While the uncoated cutting tool with the cutting speed of 100 m/min and 0.25 mm/rev feed rate has yielded the surface roughness value of 2.45 μm. Afterwards, these experimental studies were executed on artificial neural networks (ANN). The training and test data of the ANNs have been prepared using experimental patterns for the surface roughness. In the input layer of the ANNs, the coating tools, feed rate (f) and cutting speed (V) values are used while at the output layer the surface roughness values are used. They are used to train and test multilayered, hierarchically connected and directed networks with varying numbers of the hidden layers using back-propagation scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) algorithms with the logistic sigmoid transfer function. The experimental values and ANN predictions are compared by statistical error analyzing methods. It is shown that the SCG model with nine neurons in the hidden layer has produced absolute fraction of variance (R²) values about 0.99985 for the training data, and 0.99983 for the test data; root mean square error (RMSE) values are smaller than 0.00265; and mean error percentage (MEP) are about 1.13458 and 1.88698 for the training and test data, respectively. Therefore, the surface roughness value has been determined by the ANN with an acceptable accuracy.     

A tool path generation method for freeform surface machining by introducing the tensor property of machining strip width

Due to the complexity of geometry, the feed direction with maximal machining strip width usually varies among different regions over a freeform surface or a shell of surfaces. However, in most traditional tool path generation methods, the surface is treated as one machining region thus only local optimisation might be achieved. This paper presents a new region-based tool path generation method. To achieve the full effect of the optimal feed direction, a surface is divided into several sub-surface regions before tool path computation. Different from the scalar field representation of the machining strip width, a rank-two tensor field is derived to evaluate the machining strip width using ball end mill. The continuous tensor field is able to represent the machining strip widths in all feed directions at each cutter contact point, except at the boundaries between sub-regions. Critical points where the tensor field is discontinuous are defined and classified. By applying critical points in the freeform surface as the start for constructing inside boundaries, the surface could be accurately divided to such that each region contain continuous distribution of feed directions with maximal machining strip width. As a result, tool paths are generated in each sub-surface separately to achieve better machining efficiency. The proposed method was tested using two freeform surfaces and the comparison to several leading existing tool path generation methods is also provided.     

Comparison of surface plasmon resonance spectroscopy and quartz crystal microbalance for human IgE quantification

Surface plasmon resonance (SPR) and quartz crystal microbalance (QCM) have been known independently as surface sensitive analytical devices capable of label-free and in situ bioassays. In this study a SPR device and a 10 MHz QCM sensor are employed for the study of human IgE and anti-human IgE-binding reactions upon immobilizing the latter on the gold electrodes. The SPR and QCM response curves to the antibody immobilization and antigen binding are similar in shape but different in time scale, reflecting different resonation principles. Through optimization of the anti-human IgE coating, both the SPR and QCM sensors could detect IgE in a linear range from 5 to 300 IU/ml. Although the intrinsic sensitivity of the SPR device is five times of the 10 MHz QCM, the IgE detection sensitivity of the two methods is, however, different only in a factor of 2. The acceptable QCM sensitivity for the IgE detection is attributed to the fact that QCM measures the sum of molar mass of a protein layer and the entrapped water. Although both the devices use open, stand still liquid cell, and all the measurements are performed at room temperature, the SPR reproducibility and reliability are better than QCM, as the QCM frequency is more sensitive to temperature fluctuations, press changes and mechanical disturbances.     

Comparison of Fourier,principal component and wavelet analyses for high speed flame measurements

The continuing advancement of high speed, combustion diagnostics calls for mathematical techniques that can extract key information from large datasets. This paper therefore describes a case study to compare the characterization of combustion dynamics behind a V-gutter flame holder using three mathematical methods: Fourier analysis, principal component analysis, (PCA), and wavelet analysis (WA). The comparison focuses on the analysis of the characteristic frequencies of flow–flame interactions, with a particular emphasis on the analysis of transient and unsteady combustion procedures, such as lean blow off. Experimental data obtained under a range of conditions were analyzed using all three methods, and several observations were made. When applied to the analysis of stable combustion processes, all three methods reported frequency characteristics that were similar both quantitatively and qualitatively. Under unstable and transient combustion conditions, the WA method is capable of revealing the dynamics of the frequency components in the measurements, while traditional Fourier and PCA methods encounter application restrictions. Lastly, these applications also demonstrated WA’s suitability for practical combustion measurements beyond chemiluminescence, such as its applicability to discrete signals, insensitivity to the choice of wavelet basis, and insensitivity to the target signal extracted from the raw measurements.     

Deep learning-based optimal segmentation of 3D printed product for surface quality improvement and support structure reduction

Large-sized product cannot be printed as one piece by a 3D printer because of the volume limitation of most 3D printers. Some products with the complex structure and high surface quality should also not be printed into one piece to meet requirement of the printing quality. For increasing the surface quality and reducing support structure of 3D printed models, this paper proposes a 3D model segmentation method based on deep learning. Sub-graphs are generated by pre-segmenting 3D triangular mesh models to extract printing features. A data structure is proposed to design training data sets based on the sub-graphs with printing features of the original 3D model including surface quality, support structure and normal curvature. After training a Stacked Auto-encoder using the training set, a 3D model is pre-segmented to build an application set by the sub-graph data structure. The application set is applied by the trained deep-learning system to generate hidden features. An Affinity Propagation clustering method is introduced in combining hidden features and geometric information of the application set to segment a product model into several parts. In the case study, samples of 3D models are segmented by the proposed method, and then printed using a 3D printer for validating the performance.     

Langasite based surface acoustic wave sensors for high temperature chemical detection in harsh environment: Design of the transducers and packaging

This work presents the design and the thermal behavior characterization of an innovative self-test portable surface acoustic wave platform for chemical detection under high temperature. Before the forthcoming deposition of the sensitive coating, the thermal behavior of the bare LGS acoustic platform has been focused on. The system includes a (0°, 140°, 25°) crystallographic cut langasite (LGS) piezoelectric substrate, a ceramic heater, and a platform with RF connections for remote measurements. The packaging consists in a hermetic stainless steel cell, which enables safe gas detection. Its thermal behavior was successfully investigated in the temperature range 25-500 °C thanks to the integrated heater, without using an external furnace. Finite element modeling aided the development of this platform structure by predicting the thermal behavior of each of its parts and their cross-influences. The structure of the platform was specifically designed so that 500 °C could be reached on the LGS acoustic device while the temperature on the PCB connections should not exceed 50 °C. Then, the temperature-dependence on the waves generated by the acoustic transducers has been investigated through numerical modeling by resolving the wave propagation equations with several sets of LGS constants. Corresponding simulations showed good agreement with experiments, Thermal cycling up to 350 °C highlighted satisfactory hardiness and response-reproducibility of the system towards thermal stress, after a first burn effect.     

A general framework for three-dimensional surface reconstruction by self-consistent fusion of shading and shadow features

In this paper a novel framework for three-dimensional surface reconstruction by self-consistent fusion of shading and shadow features is presented. Based on the analysis of at least two pixel-synchronous images of the scene under different illumination conditions, this framework combines a shape from shading approach for estimating surface gradients and altitude variations on small scales with a shadow analysis method that allows for the determination of the large-scale properties of the surface. As a first step, the result of shadow analysis is used for selecting a consistent solution of the shape from shading reconstruction algorithm. As a second step, an additional error term derived from the fine-structure of the shadow is incorporated into the reconstruction algorithm. This approach is extended to the analysis of two or more shadows under different illumination conditions leading to an appropriate initialization of the shape from shading algorithm. The framework is applied to the astrogeological task of three-dimensional reconstruction of regions on the lunar surface using ground-based CCD images and to the machine vision task of industrial quality inspection.     

Models for surface reflection of radiance and polarized radiance: Comparison with airborne multi-angle photopolarimetric measurements and implications for modeling top-of-atmosphere measurements

In this paper, we investigate the surface-atmosphere radiative interaction in application to the problem of aerosol satellite remote sensing over land. First, we test different models of the Bidirectional Reflectance and Polarization Distribution Function (BRDF and BPDF) for bare soil and vegetation surfaces using multi-angle, multi-spectral photopolarimetric airborne measurements of the Research Scanning Polarimeter (RSP). Then, we investigate the performance of different models of BRDF and BPDF for modeling top-of-atmosphere measurements. We have found that different BRDF models can describe the RSP measurements equally well. However, for soil surfaces, the different BRDF models show a different dependence on illumination geometry (solar zenith and azimuth angles), as well as a different dependence on viewing angle outside the range of RSP measurements. This implies that different models describe the surface-atmosphere interaction differently, leading for soil surfaces to differences in the top-of-atmosphere reflectance up to 4-5%, whereas at surface level the models agree within 2% for RSP illumination and measurement geometry. For vegetation, the different BRDF models show more similar dependence on illumination geometry, meaning that, in general, the differences in top-of-atmosphere reflectances are smaller than the differences in surface total reflectances. For the BPDF, we compare the empirical model of Nadal and Breon (1999) and the model developed by Maignan et al. (2009) with a newly developed model. The latter model compares better with RSP measurements. It was shown that, though all models have essentially different angular profiles at different illumination and viewing geometries, the difference of the top-of-atmosphere degree of linear polarization is less or is of the same order as the degree of linear polarization difference at the surface level taken at RSP illumination and measurement geometry. For the considered models, it can be up to 0.015 but is mostly below 0.005.     

Utilization of albumin-based sensor chips for the detection of metal content and characterization of metal–protein interaction by surface plasmon resonance

We report here the use of albumin-based biosensor chips for the determination of metal content and characterization of metal–protein interaction by surface plasmon resonance. Bovine serum albumin was immobilized onto a carboxymethylated dextran matrix and used for metal detection. The temperature for the analysis was defined and the highest interaction was observed at 25 °C. The albumin sensor chip binds cadmium, zinc or nickel in a concentration-dependent manner, but not magnesium, manganese and calcium. The optimal buffer condition used for the analysis contains 0.01 M HEPES, pH 7.4, 1 mM NaCl and 0.005% Tween-20. Using this condition, a linear calibration curve within the range of 10⁻⁸ to 10⁻⁴ M can be established for the metals. However, a dramatic increase in binding capacity was observed when metal concentration was higher than 10⁻⁴ M and reached a plateau at 10⁻² M. The detection limit for Cd can reach as low as 1 ppb. When measuring a solution containing two species of metal ions with the albumin chip, an additive effect was observed for Ni and Zn. However, 20–30% reduction in resonance response was found upon mixing Cd with Zn or Ni. These observations are consistent with the binding characteristics of albumin. The feasibility of measuring serum metal content by the albumin chip was examined. A linear calibration curve can be established if the samples are boiled and passed through a gel filtration column. The binding affinity of metal with albumin can also be achieved by using the sensor chip. The binding affinity follows the order of Ni > Zn > Cd. These results indicate that the albumin-based sensor chip is useful not only in the quantification of metal content, but also in the characterization of the biochemical properties of albumin.