Skyhook surface sliding mode control on semi-active vehicle suspension systems for ride comfort enhancement

A skyhook surface sliding mode control method was proposed and applied to the control on the semi-active vehicle suspension system for its ride comfort enhancement. A two degree of freedom dynamic model of a vehicle semi-active suspension system was given, which focused on the passenger’s ride comfort perform-ance. A simulation with the given initial conditions has been devised in MATLAB/SIMULINK. The simula-tion results were showing that there was an enhanced level of ride comfort for the vehicle semi-active sus-pension system with the skyhook surface sliding mode controller

Intelligent machining methods for Ti6Al4V: a review

Digital manufacturing is a necessity to establishing a roadmap for the future manufacturing systems projected for the fourth industrial revolution. Intelligent features such as behavior prediction, decision- making abilities, and failure detection can be integrated into machining systems with computational methods and intelligent algorithms. This review reports on techniques for Ti6Al4V machining process modeling, among them numerical modeling with finite element method (FEM) and artificial intelligence- based models using artificial neural networks (ANN) and fuzzy logic (FL). These methods are intrinsically intelligent due to their ability to predict machining response variables. In the context of this review, digital image processing (DIP) emerges as a technique to analyze and quantify the machining response (digitization) in the real machining process, often used to validate and (or) introduce data in the modeling techniques enumerated above. The widespread use of these techniques in the future will be crucial for the development of the forthcoming machining systems as they provide data about the machining process, allow its interpretation and quantification in terms of useful information for process modelling and optimization, which will create machining systems less dependent on direct human intervention.publishe

Cutting parameters optimisation in milling: expert machinist knowledge versus soft computing method

In traditional machining operations, cutting parameters are usually selected prior to machining according to machining handbooks and user’s experience. However, this method tends to be conservative and sub-optimal since part accuracy and non machining failures prevail over machining process efficiency. In this paper, a comparison between traditional cutting parameter optimisation by an expert machinist and an experimental optimisation procedure based on Soft Computing methods is conducted. The proposed methodology increases the machining performance in 6.1% and improves the understanding of the machining operation through the use of Adaptive Neuro-fuzzy Inference System

Studying the Effect of Cutting Conditions in Turning Process on Surface Roughness for Different Materials

Surfaces quality is one of the most specified customer requirements for machine parts. The major indication of surfaces quality on machined parts is surface roughness. The research aim is to study the cutting conditions and their effects on the surface roughness. This research will use regression models and neuro-fuzzy to predict surface roughness over the machining time for variety of cutting conditions in turning. In the experimental part for turning, different types of materials (Aluminum alloy, brass alloy, and low carbon steel) were considered with different cutting speed, and feed rate. A linear regression and neuro-fuzzy model depending on statistical-mathematical method between surface roughness, Ra, and cutting condition will be derived, for the three materials. The effect of cutting parameters on surface roughness is evaluated and the optimum cutting condition for minimizing the surface roughness will be determined. The model will be established between the cutting conditions and surface roughness using regression and neuro-fuzzy model. As the results of this work, the linear regression and neuro-fuzzy model will be used in predicting surface roughness, can be used in manufacturing systems, this modeling helps engineer to reduce the efforts and improve the quality

A hybrid approach of anfis—artificial bee colony algorithm for intelligent modeling and optimization of plasma arc cutting on monel™ 400 alloy

This paper focusses on a hybrid approach based on genetic algorithm (GA) and an adaptive neuro fuzzy inference system (ANFIS) for modeling the correlation between plasma arc cutting (PAC) parameters and the response characteristics of machined Monel 400 alloy sheets. PAC experiments are performed based on box-behnken design methodology by considering cutting speed, gas pressure, arc current, and stand-off distance as input parameters, and surface roughness (Ra), kerf width (kw), and micro hardness (mh) as response characteristics. GA is efficaciously utilized as the training algorithm to optimize the ANFIS parameters. The training, testing errors, and statistical validation parameter results indicated that the ANFIS learned by GA outperforms in the forecasting of PAC responses compared with the results of multiple linear regression models. Besides that, to obtain the optimal combination PAC parameters, multi-response optimization was performed using a trained ANFIS network coupled with an artificial bee colony algorithm (ABC). The superlative responses, such as Ra of 1.5387 µm, kw of 1.2034 mm, and mh of 176.08, are used to forecast the optimum cutting conditions, such as a cutting speed of 2330.39 mm/min, gas pressure of 3.84 bar, arc current of 45 A, and stand-off distance of 2.01 mm, respectively. Furthermore, the ABC predicted results are validated by conducting confirmatory experiments, and it was found that the error between the predicted and the actual results are lower than 6.38%, indicating the adoptability of the proposed ABC in optimizing real-world complex machining processes

Drilling of Glass Fiber Reinforced Polymer (GFRP) Composites: Multi Response Optimization Using Grey Relation Analysis with Taguchi’s Method

Nowadays, GFRP (Glass Fiber Reinforced Polymer) composites are widely used in manufacturing industries specially aircraft, aerospace, and automobile industries due to their excellent mechanical and thermal properties such as more specific strength, better specific modulus of elasticity, high damping factor or damping capacity, better resistance to corrosion, effective fatigue resistance, low thermal expansion coefficient. Hence, it is necessary to understand the machinability behavior of these composites. Drilling is widely used to assemble the components in aforementioned industries. But machining of these composites is dissimilar to conventional metals due to their isotropic nature and in-homogeneity. Major drawbacks of these composites in machining are fiber pull out, delaminating and burring of fibers. So, appropriate selection of process parameters is an important concern in machining of GFRP composites. This work mainly focuses on assessing the effects of process parameters i.e. spindle speed, feed and drill diameter on thrust, torque, delamination factor (both at entry and exit) in drilling of GFRP composites using TiAlN coated drill bit. The study also utilizes the Grey methodology coupled with Taguchi L16 OA to determine the optimal parametric combination