Optimisation of grinding parameters for wheel loading and dressing
Grinding is a finishing process often used to produce desired surface roughness with required precision and quality. The performance of grinding wheel significantly depends on the nature of the wheel, morphology of the abrasive grits and the grinding parameters namely speed, feed and depth of cut. As the wheel is performing its grinding operation, its cutting surface is being loaded with metal particles of the workpiece. This wheel loading also affects the surface finish of the workpiece. Hence monitoring the grinding wheel loading and dressing at proper time intervals becomes mandatory to get the desired surface finish. In the quest of developing a low cost monitoring system, it is good enough to analyse the wheel loading on widely used metal specimens such as mild steel, HCHCr steel and aluminium under optimised cutting conditions using the conventional silicon carbide and aluminium oxide wheels for better evaluation of the results. In this work the grinding parameters are optimised by using ANOVA technique. A full factorial experiment was carried out at 5% level of significance, i.e. at 95% confidence level.
Optimal Selection Of Machining Parameters In CNC Turning Process Of EN-31 Using Intelligent Hybrid Decision Making Tools
Abstract Nowadays numerical and Artificial Neural Networks (ANN) methods are widely used for both modeling and optimizing the performance of the manufacturing technologies. Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. Therefore the present research is aimed at finding the optimal process parameters for turning process of EN-31. EN-31 is chosen as the work material because of its wide applicability as material for Ball and roller bearings, spinning tools, Beading rolls, Punches and dies and by its character it has very high resistance nature against wear and can be used for components subjected to severe abrasion, wear or high surface loading. Turning is used to produce rotational, typically axi-symmetric, parts that have many features, such as holes, grooves, threads, tapers, various diameter steps, and even contoured surfaces. Turning is also commonly used as a secondary process to add or refine features on parts that were manufactured using a different process. Due to the high tolerances and surface finishes that turning can offer, it is ideal for adding precision rotational features to a part whose basic shape has already been formed. Keeping in view of the importance of turning process, it is very important to automate the process. In order to automate the system or process it is essential to find the optimal process parameters. Hence intelligent hybrid decision making tools are applied to find the optimal process parameters. First the experiments were conducted as per the design of experiments, and then ANN is applied to predict the models for the chosen output responses. Later the models after testing for its adequacy using ANOVA Analysis, they may be chosen for subsequent optimization of the process parameters using Evolutionary techniques. The obtained optimal process parameters will be used to automate the process
The best location for speed bump installation using experimental design methodology
Speed bumps, as traffic calming devices, have been extensively used to reduce traffic speed on local streets. This study represents a unique application of experimental design methodology where the effects of some controllable factors in determining the best location for installing speed bumps before stop points (e.g. entry gates, road junctions) were investigated. Through Classical Design of Experiments (DOE), the optimum location of the speed bump was obtained based on the graphical plots of the significant effects. The speed at the stop point was treated as the response and minimum speed is desirable. Design-Expert® software was used to evaluate and analyze the results obtained. The suggested mathematical model effectively explains the performance indicators within the ranges of the factors. The car speed is the most significant factor that affects the distance-time in comparison with other factors, which provides secondary contributions.