Development of an experiment-based robust design paradigm for multiple quality characteristics using physical programming

The well-known quality improvement methodology, robust design, is a powerful and cost-effective technique for building quality into the design of products and processes. Although several approaches to robust design have been proposed in the literature, little attention has been given to the development of a flexible robust design model. Specifically, flexibility is needed in order to consider multiple quality characteristics simultaneously, just as customers do when judging products, and to capture design preferences with a reasonable degree of accuracy. Physical programming, a relatively new optimization technique, is an effective tool that can be used to transform design preferences into specific weighted objectives. In this paper, we extend the basic concept of physical programming to robust design by establishing the links of experimental design and response surface methodology to address designers’ preferences in a multiresponse robust design paradigm. A numerical example is used to show the proposed procedure and the results obtained are validated through a sensitivity study

A case study on Application of FUZZY logic in Electrical Discharge Machining(EDM)

Electrical Discharge Machining (EDM) is one of the most accurate manufacturing processes available for creating complex or simple shapes and geometries within parts and assemblies. EDM works by eroding material in the path of electrical discharges that form an arc between an electrode tool and the work piece. EDM manufacturing is quite affordable and a very desirable manufacturing process when low counts or high accuracy is required. Turn around time can be fast and depends on manufacturer back log. The EDM system consists of a shaped tool or wire electrode, and the part. The part is connected to a power supply. Sometimes to create a potential difference between the work piece and tool, the work piece is immersed in a dielectric (electrically non-conducting) fluid which is circulated to flush away debris

Multi Objective Optimization of Weld Parameters of Boiler Steel Using Fuzzy Based Desirability Function

The high pressure differential across the wall of pressure vessels is potentially dangerous and has caused many fatal accidents in the history of their development and operation. For this reason the structural integrity of weldments is critical to the performance of pressure vessels. In recent years much research has been conducted to the study of variations in welding parameters and consumables on the mechanical properties of pressure vessel steel weldments to optimize weld integrity and ensure pressure vessels are safe. The quality of weld is a very important working aspect for the manufacturing and construction industries. Because of high quality and reliability, Submerged Arc Welding (SAW) is one of the chief metal joining processes employed in industry. This paper addresses the application of desirability function approach combined with fuzzy logic analysis to optimize the multiple quality characteristics (bead reinforcement, bead width, bead penetration and dilution) of submerged arc welding process parameters of SA 516 Grade 70 steels(boiler steel). Experiments were conducted using Taguchi’s L27 orthogonal array with varying the weld parameters of welding current, arc voltage, welding speed and electrode stickout. By analyzing the response table and response graph of the fuzzy reasoning grade, optimal parameters were obtained. Solutions from this method can be useful for pressure vessel manufacturers and operators to search an optimal solution of welding condition

Studies on some aspects of composite machining

In this technological era, globalization has brought new challenges for the manufacturing industries, towards improving quality and productivity simultaneously, by reducing costs and increasing the performance of the machine tools. Process simulation is one of the most important aspects in any manufacturing/production context. With upcoming worldwide applications of Glass Fiber Reinforced Polymer (GFRP) composites; machining has become an important issue which needs to be investigated in detail. Process efficiency is measured in the sense of different objective functions or process output responses weather they are acceptable for a given targeted value or tolerance. Therefore, finding the best optimal parameter combination can lead towards improvement of the overall process efficiency. The performance of the process can be improved by applying optimization to the simulation model with respect to its process parameters. Single objective optimization method often creates conflict, when more than one response variables need to be optimized simultaneously. In order to minimize cost and to maximize production rate simultaneously; multi-objective optimization approach should be explored. In this thesis, multi-objective optimization methods have been reported to study some aspects of machining of composite material i.e. Glass Fiber Reinforced Polymer (GFRP) composite. The various process parameters used were cutting speed, feed rate, and depth of cut. Optimal cutting condition has been aimed to be evaluated to satisfy contradicting multi-requirements of product quality as well as productivity. This thesis has intended towards focusing two important aspects (i) when it comes to improve productivity, material removal rate has been considered and for (ii) quality of the machined composite product, various surface roughness characteristics of statistical importance have been investigated

Modeling of the influence of input AM parameters on dimensional error and form errors in PLA parts printed with FFF technology

As is widely known, additive manufacturing (AM) allows very complex parts to be manufactured with porous structures at a relatively low cost and in relatively low manufacturing times. However, it is necessary to determine in a precise way the input values that allow better results to be obtained in terms of microgeometry, form errors, and dimensional error. In an earlier work, the influence of the process parameters on surface roughness obtained in fused filament fabrication (FFF) processes was analyzed. This present study focuses on form errors as well as on dimensional error of hemispherical cups, with a similar shape to that of the acetabular cup of hip prostheses. The specimens were 3D printed in polylactic acid (PLA). Process variables are nozzle diameter, temperature, layer height, print speed, and extrusion multiplier. Their influence on roundness, concentricity, and dimensional error is considered. To do this, adaptive neuro-fuzzy inference systems (ANFIS) models were used. It was observed that dimensional error, roundness, and concentricity depend mainly on the nozzle diameter and on layer height. Moreover, high nozzle diameter of 0.6 mm and high layer height of 0.3 mm are not recommended. A desirability function was employed along with the ANFIS models in order to determine the optimal manufacturing conditions. The main aim of the multi-objective optimization study was to minimize average surface roughness (Ra) and roundness, while dimensional error was kept within the interval. When the simultaneous optimization of both the internal and the external surface of the parts is performed, it is recommended that a nozzle diameter of 0.4 mm be used, to have a temperature of 197 °C, a layer height of 0.1 mm, a print speed of 42 mm/s, and extrusion multiplier of 94.8%. This study will help to determine the influence of the process parameters on the quality of the manufactured parts.This research was financed by the Spanish Ministry of Industry, Economy and Competitiveness, grant number PID2020-115647RB-C21

Fuzzy controller tuning of a boost rectifier unity power factor correction by experimental designs

This paper shows the validity of experimental designs as an efficient on-site tuning tool for fuzzy controllers, dedicated to electrical engineering applications with multi-objective criteria. Our purpose is to improve the input and output system characteristics that is to say the global quality of the electrical power in a boost rectifier with unity power factor correction. The desirability notion combines here time dynamic and harmonic criteria, it illustrates the trade-off that has to be satisfied between the different properties

Experimental Investigations on Machining of CFRP Composites: Study of Parametric Influence and Machining Performance Optimization

Carbon Fiber Reinforced Polymer (CFRP) composites are characterized by their excellent mechanical properties (high specific strength and stiffness, light weight, high damping capacity etc.) as compared to conventional metals, which results in their increased utilization especially for aircraft and aerospace applications, automotive, defense as well as sporting industries. With increasing applications of CFRP composites, determining economical techniques of production is very important. However, as compared to conventional metals, machining behavior of composites is somewhat different. This is mainly because these materials behave extremely abrasive during machining operations. Machining of CFRP appears difficult due to their material discontinuity, inhomogeneity and anisotropic nature. Moreover, the machining behavior of composites largely depends on the fiber form, the fiber content, fiber orientations of composites and the variability of matrix material. Difficulties are faced during machining of composites due to occurrence of various modes of damages like fiber breakage, matrix cracking, fiber–matrix debonding and delamination. Hence, adequate knowledge and in-depth understanding of the process behavior is indeed necessary to identify the most favorable machining environment in view of various requirements of process performance yields. In this context, present work attempts to investigate aspects of machining performance optimization during machining (turning and drilling) of CFRP composites. In case of turning experiments, the following parameters viz. cutting force, Material Removal Rate (MRR), roughness average (Ra) and maximum tool-tip temperature generated during machining have been considered as process output responses. In case of drilling, the following process performance features viz. load (thrust), torque, roughness average (of the drilled hole) and delamination factor (entry and exit both) have been considered. Attempt has been made to determine the optimal machining parameters setting that can simultaneously satisfy aforesaid response features up to the desired extent. Using Fuzzy Inference System (FIS), multiple response features have been aggregated to obtain an equivalent single performance index called Multi-Performance Characteristic Index (MPCI). A nonlinear regression model has been established in which MPCI has been represented as a function of the machining parameters under consideration. The aforesaid regression model has been considered as the fitness function, and finally optimized by evolutionary algorithms like Harmony Search (HS), Teaching-Learning Based Optimization (TLBO), and Imperialist Competitive Algorithm (ICA) etc. However, the limitation of these algorithms is that they assume a continuous search within parametric domain. These algorithms can give global optima; but the predicted optimal setting may not be possible to adjust in the machine/setup. Since, in most of the machines/setups, provision is given only to adjust factors (process input parameters) at some discrete levels. On the contrary, Taguchi method is based on discrete search philosophy in which predicted optimal setting can easily be achieved in reality.However, Taguchi method fails to solve multi-response optimization problems. Another important aspect that comes into picture while dealing with multi-response optimization problems is the existence of response correlation. Existing Taguchi based integrated optimization approaches (grey-Taguchi, utility-Taguchi, desirability function based Taguchi, TOPSIS, MOORA etc.) may provide erroneous outcome unless response correlation is eliminated. To get rid of that, the present work proposes a PCA-FuzzyTaguchi integrated optimization approach for correlated multi-response optimization in the context of machining CFRP composites. Application potential of aforementioned approach has been compared over various evolutionary algorithms

Fuzzy rule based optimization in machining of glass fiber reinforced polymer (GFRP) composites

With the increasing use of Fiber Reinforced Polymer (FRP) composites outside the defense, space and aerospace industries; machining of these materials is gradually assuming a significant role. The current knowledge of machining FRP composites is in transition phase for its optimum economic utilization in various fields of applications. Therefore, material properties and theoretical mechanics have become the predominant research areas in this field. With increasing applications, economical techniques of production are indeed very important to achieve fully automated large-scale manufacturing cycles. Although FRP composites are usually molded, for obtaining close fits and tolerances and also achieving near-net shape, certain amount of machining has to be carried out. Due to their anisotropy, and non-homogeneity, FRP composites face considerable problems in machining like fibre pull-out, delamination, burning, etc. There is a remarkable difference between the machining of conventional metals and their alloys and that of composite materials. Further, each composite differs in its machining behavior since its physical and mechanical properties depend largely on the type of fibre, the fibre content, the fibre orientation and variabilities in the matrix material. Considerable amount of literature is readily available on the machinability of conventional metals/alloys and also polymers to some extent; with very limited work on FRP composites. Therefore, machining process optimization for all types FRP composites is still an emerging area of research. In this context, the present research highlights a multi-objective extended optimization methodology to be applied in machining FRP-polyester/epoxy composites with contradicting requirements of quality as well as productivity. Attempt has been made to develop a robust methodology for multi-response optimization in FRP composite machining 6 for continuous quality improvement and off-line quality control. Design of Experiment(DOE) has been be selected based on Taguchi’s orthogonal array design with varying process control parameters like: spindle speed, feed rate and depth of cut. Multiple surface roughness parameters of the machined FRP product along with Material Removal Rate (MRR) of the machining process have been optimized simultaneously. A Fuzzy Inference System (FIS) integrated with Taguchi’s philosophy has been proposed for providing feasible means for meaningful aggregation of multiple objective functions into an equivalent single performance index (MPCI). This Multi Performance Characteristic Index (MPCI) has been optimized finally. Detailed methodology of the proposed fuzzy based optimization approach has been illustrated in this reporting and validated by experiments