Search results

An assessment technique that analyzes the servo and regulatory characteristics of the proportional integral derivative controller is designed for time-delayed second-order stable processes.

The minimum theoretical error expression for integral of the absolute errors (IAE_o) is obtained from the preferred servo and regulatory transfer functions dependent on the step changes in reference and load variables.

The error-based index is outlined to estimate the controller that is derived using internal model-based control or direct synthesis method. The ratio between derived IAE_o and the IAE_actual gained from the loop response that experiences step input variations gives rise to a dimensionless error index. This error index measures the behaviour of the controller by considering the index value. If the error index value is larger than 0.8, then the effort taken by the controller is good or else retuning is expected.

The efficacy of the index to validate the controller is verified by applying on a few second-order electrical processes. The results are simulated for both reference tracking and load rejection tasks to demonstrate the rationality of the presented index.

This paper aims to find the effective 3D printing process parameters based on mechanical characteristics such as tensile strength and hardness of poly lactic acid (PLA)/carbon fibre composites (CF-PLA) by implementing intelligent frameworks.

The experiment trials are conducted based on design of experiments (DoE) using Taguchi L9 orthogonal array with three factors (speed, infill % and pattern type) and three levels. The factors have been optimized by solving the regression equation which is obtained from analysis of variance (ANOVA). The contour plots are generated by response surface methodology (RSM). The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness.

The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness. The results obtained from RSM are also confirmed by implementing the machine learning classifiers, such as logistic regression, ridge classifier, random forest, K nearest neighbour and support vector classifier (SVC). The results show that the SVC can predict the optimized process parameters with an accuracy of 95.65%.

3D printing parameters which are considered in this work such as pattern types for PLA/CF-PLA composites based on intelligent frameworks has not been attempted previously.