Fuzzy Linguistic Optimization on Multi-Attribute Machining

Most existing multi-attribute optimization researches for the modern CNC (computer numerical control) turning industry were either accomplished within certain manufacturing circumstances, or achieved through numerous equipment operations. Therefore, a general deduction optimization scheme proposed is deemed to be necessary for the industry. In this paper, four parameters (cutting depth, feed rate, speed, tool nose runoff) with three levels (low, medium, high) are considered to optimize the multi-attribute (surface roughness, tool wear, and material removal rate) finish turning. Through FAHP (Fuzzy Analytic Hierarchy Process) with eighty intervals for each attribute, the weight of each attribute is evaluated from the paired comparison matrix constructed by the expert judgment. Additionally, twenty-seven fuzzy control rules using trapezoid membership function with respective to seventeen linguistic grades for each attribute are constructed. Considering thirty input and eighty output intervals, the defuzzifierion using center of gravity is thus completed. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is moreover utilized to integrate and evaluate the multiple machining attributes for the Taguchi experiment, and thus the optimum general deduction parameters can then be received. The confirmation experiment for optimum general deduction parameters is furthermore performed on an ECOCA-3807 CNC lathe. It is shown that the attributes from the fuzzy linguistic optimization parameters are all significantly advanced comparing to those from benchmark. This paper not only proposes a general deduction optimization scheme using orthogonal array, but also contributes the satisfactory fuzzy linguistic approach for multiple CNC turning attributes with profound insight

A Study On Parametric Appraisal of Fused Deposition Modelling (FDM) Process

The manufacturing industries are contemplating to develop new technologies for production of complex end use parts possessing high strength and low product development cycle in order to meet the global competition. Rapid prototyping (RP) is one of the proficient processes having the ability to build complex geometry parts in reasonably less time and material waste. Fused deposition modelling (FDM) is one of the RP processes that can manufacture 3D complex geometry accurately with good mechanical strength and durability. Normally, the FDM process is a parametric dependant process due to its layer-by-layer build mechanism. As FDM build parts are used as end use parts, it is prudent to study the effect of process parameters on the mechanical strength under both static and dynamic loading conditions and wear (sliding) behaviour. In order to investigate the behaviour of build parts in a systematic manner with less number of experimental runs, design of experiment (DOE) approach has been used to save cost and time of experimentation. As the selection of input process parameters influence on build mechanism, the mechanical properties and wear behaviour of FDM build parts change with process parameters. Notably, the raster fill pattern during part building causes FDM build parts to exhibit anisotropic behaviour when subject to loading (static or dynamic). In this research work, an attempt has been made to minimise the anisotropic behaviour through controlling the raster fill pattern during part building by adequate selection of process parameters. Statistical significance of the process parameters is analysed using analysis of variance (ANOVA). Influence of process parameters on performance characteristics like mechanical strength, fatigue life and wear of build part is analysed with the help of surface plots. Internal structure of rasters, failure of rasters, formation of pits and crack are evaluated using scanning electron machine (SEM) micro-graphs. Empirical models have been proposed to relate the performance characteristics with process parameters. Optimal parameter setting has been suggested using a nature inspired metaheuristic firefly algorithm to improve the mechanical strength. Finally, genetic programming (GP) and least square support vector machine (LS-SVM) are adopted to develop predictive models for various performance characteristic