Ferritic nitrocarburising of tool steels

Four different tool steel materials, P20, H13, M2 and D2, were nitrocarburised at 570°C in a fluidised bed furnace. The reactive diffusion of nitrogen and carbon into the various substrate microstructures is compared and related to the different alloy carbide distributions. The effect of carbon bearing gas (carbon dioxide, natural gas) on carbon absorption is reported, as well as its influence on compound layer growth and porosity. Partial reduction of Fe3O4 at the surface resulted in the formation of a complex, epsi-nitride containing oxide layer. In H13, carbon was deeply absorbed throughout the entire diffusion zone, affecting the growth of grain boundary cementite, nitrogen diffusivity and the sharpness of the compound layer: diffusion zone interface. When natural gas was used, carbon became highly concentrated in the compound layer, while surface decarburisation occurred with carbon dioxide. These microstructural effects are discussed in relation to hardness profiles, and compound layer hardness and ductility. The surfaces were characterised using glow discharge optical emission spectroscopy, optical and scanning electron microscopy and X-ray diffraction.

Electron back scattered diffraction (EBSD) characterization of warm rolled and accumulative roll bonding (ARB) processed ferrite

An interstitial free (IF) steel was severely deformed using accumulative roll bonding (ARB) process and warm rolling. The maximum equivalent strains for ARB and warm rolling were 4.8 and 4.0, respectively. The microstructure and micro-texture were studied using optical microscopy and scanning electron microscopy equipped with electron back scattered diffraction (EBSD). The grain size and misorientation obtained by both methods are in the same range. The microstructure in the ARB samples after 6 cycles is homogeneous, although a grain size gradient is observed at the layers close to the surface. The through thickness texture gradient in the ARB samples is different from the warm rolled samples. While a shear texture (⟨110⟩//rolling plane normal direction (ND)) at the surface and rolling texture at the center region is developed in the ARB  samples, the overall texture is weak. The warm rolled samples display a sharp rolling texture through the thickness with increasing the sharpness toward the center. These differences are attributed to the fact that the central region of ARB strip is comprised of material that was once at the surface. The ARB process  can suppress the formation of shear bands which are conventional at warm rolled IF steels. EBSD study on the sample with 6th cycle of ARB following the annealing at 750 ◦C verified a texture gradient through the thickness of the sheet. The shear orientations at the surface and at the quarter thickness layers can be identified even after annealing. The overall weak texture and existence of shear orientations make ARB processed samples unfavorable for sheet metal forming in compare with warm rolled samples.

Path planning for CNC machines considering centripetal acceleration and jerk

In planning an s-curve speed profile for a computer numerical control (CNC) machine, centripetal acceleration and its derivative have to be considered. In a CNC machine, these quantities dictate how much voltage and current should be applied to servo motor windings. In this paper, the necessity of considering centripetal jerk in speed profile generation especially in the look-ahead mode is explained. It is demonstrated that the magnitude of centripetal jerk is proportional to the curvature derivative of the path known as "sharpness". It is also explained that a proper limited jerk motion is only possible when a G2-continuous machining path is planned. Then using a simplified mathematical representation of clothoids, a novel method for approximating a given path with a sequence of clothoid segments is proposed. Using this method, a semi-parallel G2-continuous path with adjustable deviation from the original shape for a sample machining contour is generated. Maximum permissible feed rate for the generated path is also calculated.

Prediction interval-based neural network modelling of polystyrene polymerization reactor - a new perspective of data-based modelling

In this paper, prediction interval (PI)-based modelling techniques are introduced and applied to capture the nonlinear dynamics of a polystyrene batch reactor system. Traditional NN models are developed using experimental datasets with and without disturbances. Simulation results indicate that traditional NNs cannot properly handle disturbances in reactor data and demonstrate a poor forecasting performance, with an average MAPE of 22% in the presence of disturbances. The lower upper bound estimation (LUBE) method is applied for the construction of PIs to quantify uncertainties associated with forecasts. The simulated annealing optimization technique is employed to adjust NN parameters for minimization of an innovative PI-based cost function. The simulation results reveal that the LUBE method generates quality PIs without requiring prohibitive computations. As both calibration and sharpness of PIs are practically and theoretically satisfactory, the constructed PIs can be used as part of the decision-making and control process of polymerization reactors. © 2014 The Institution of Chemical Engineers.

Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.

Representing images using curvilinear feature driven subdivision surfaces.

This paper presents a subdivision-based vector graphics for image representation and creation. The graphics representation is a subdivision surface defined by a triangular mesh augmented with color attribute at vertices and feature attribute at edges. Special cubic B-splines are proposed to describe curvilinear features of an image. New subdivision rules are then designed accordingly, which are applied to the mesh and the color attribute to define the spatial distribution and piecewise-smoothly varying colors of the image. A sharpness factor is introduced to control the color transition across the curvilinear edges. In addition, an automatic algorithm is developed to convert a raster image into such a vector graphics representation. The algorithm first detects the curvilinear features of the image, then constructs a triangulation based on the curvilinear edges and feature attributes, and finally iteratively optimizes the vertex color attributes and updates the triangulation. Compared with existing vector-based image representations, the proposed representation and algorithm have the following advantages in addition to the common merits (such as editability and scalability): 1) they allow flexible mesh topology and handle images or objects with complicated boundaries or features effectively; 2) they are able to faithfully reconstruct curvilinear features, especially in modeling subtle shading effects around feature curves; and 3) they offer a simple way for the user to create images in a freehand style. The effectiveness of the proposed method has been demonstrated in experiments.