Arup Kumar Nandi

Design of a genetic-fuzzy system to predict surface finish and power requirement in grinding

We have developed, in this paper, a genetic-fuzzy system, in which a genetic algorithm (GA) is used to improve the performance of a fuzzy logic controller (FLC). The performance of an FLC depends on its knowledge base (KB), which consists of both the data base (membership function distributions of the variables) as well as rule base. In the developed genetic-fuzzy system, the KB of the FLC is optimized, off-line, using a GA. Three approaches are developed, in the present work. In the first approach, the membership function distributions of the variables are assumed to be triangular, whereas a second-order polynomial function and a third-order polynomial function are used in the second and third approaches, respectively. The results of these approaches are compared for making prediction of surface finish and power requirement in grinding, a machining process used to generate smooth surface on the job. For some of the test cases, comparisons are also made of the results predicted by the genetic-fuzzy system with those obtained through the real experiments.

Genetic Algorithm–Based Design and Development of Particle-Reinforced Silicone Rubber for Soft Tooling Process

In order to enhance the solidification rate of soft tooling process, design of a silicone rubber composite mold material is carried out based on multiobjective optimization (MOO) of conflicting objectives. The elitist nondominated sorting genetic algorithm (NSGA-II), a genetic algorithm–based MOO tool, is used to find the optimum parameters first by obtaining the Pareto-optimal front and then selecting a single solution or a small set of solutions for manufacturing applications using a suitable multi-criterion decision making technique. Based on the optimal design parameters, an experimental study in soft tooling process is carried out in particle-reinforced silicone, and it is observed that the solidification time is minimized appreciably keeping the same advantages of soft tooling process.

Studies on Equivalent Viscosity of Particle-Reinforced Flexible Mold Materials Used in Soft Tooling Process

To reduce the cooling time in soft tooling process, one of the possible solutions is the use of composite mold materials, but that may affect melt mold flow properties. Therefore, a study on equivalent viscosity of melt mold material, which primarily influences the flow ability is essential. In this work, we have carried out an experimental study on equivalent viscosity of flexible mold materials (such as polyurethane and silicone rubber, which are of particular type) reinforced with highly thermal conductive filler particles, namely, aluminum and graphite powder. It has been observed that in addition to an increase of equivalent viscosity, different curing behaviors were noticed in mold materials reinforced with different fillers. By analyzing the performances of various equivalent viscosity models reported in literature, it has been observed that for higher particle size, the existing models deviate much from the experimental results. We have proposed an extension of the generalized model of Arefinia and Shojaei by including a factor that depends on particle size. It is found that the extension model provides better explanations compared to other models to the experimental results, especially for suspensions of flexible mold materials with higher particle sizes. Finally, a predictive approach is suggested for the equivalent viscosity of reinforced flexible mold materials, which may be useful to decide the amount of typical filler particles to be considered for mixing with a flexible mold material.

GA-Fuzzy Approaches: Application to Modeling of Manufacturing Process

This chapter presents various techniques using the combination of fuzzy logic and genetic algorithm (GA) to construct model of a physical process including manufacturing process. First, an overview on the fundamentals of fuzzy logic and fuzzy inferences systems toward formulating a rule-based model (called fuzzy rule based model, FRBM) is presented. After that, the working principle of a GA is discussed and later, how GA can be combined with fuzzy logic to design the optimal knowledge base of FRBM of a process is presented. Results of few case studies of modeling various manufacturing processes using GA-fuzzy approaches conducted by the author are presented.

Design of particle-reinforced polyurethane mould materials for soft tooling process using evolutionary multi-objective optimization algorithms

Polyurethane is used for making mould in soft tooling (ST) process for producing wax/plastic components. These wax components are later used as pattern in investment casting process. Due to low thermal conductivity of polyurethane, cooling time in ST process is long. To reduce the cooling time, thermal conductive fillers are incorporated into polyurethane to make composite mould material. However, addition of fillers affects various properties of the ST process, such as stiffness of the mould box, rendering flow-ability of melt mould material, etc. In the present work, multi-objective optimization of various conflicting objectives (namely maximization of equivalent thermal conductivity, minimization of effective modulus of elasticity, and minimization of equivalent viscosity) of composite material are conducted using evolutionary algorithms (EAs) in order to design particle-reinforced polyurethane composites by finding the optimal values of design parameters. The design parameters include volume fraction of filler content, size and shape factor of filler particle, etc. The Pareto-optimal front is targeted by solving the corresponding multi-objective problem using the NSGA-II procedure. Then, suitable multi-criterion decision-making techniques are employed to select one or a small set of the optimal solution(s) of design parameter(s) based on the higher level information of the ST process for industrial applications. Finally, the experimental study with a typical real industrial application demonstrates that the obtained optimal design parameters significantly reduce the cooling time in soft tooling process keeping other processing advantages.

Investigating the Role of Nonmetallic Fillers in Particulate-Reinforced Mold Composites using EAs

In the soft tooling (ST) process, flexible polymeric materials (namely, silicone rubber, polyurethane, etc.) are used for making mold for producing wax pattern. Due to low thermal conductivity of mold materials, the ST process takes longer time for cooling. Hence, to reduce the cooling time, thermal conductive fillers are included in mold materials. But addition of fillers affects various properties of ST process (such as stiffness of the mold box) and the influences may vary according to the types of materials used. Therefore, in the present work, multiobjective optimizations of equivalent thermal conductivity and effective modulus of elasticity of composite mold materials are conducted using evolutionary algorithms in order to investigate the role of various nonmetallic fillers in particulate reinforced mold material composites. We have adopted NSGA-II to optimize the conflicting objectives—maximization of thermal conductivity and minimization of modulus of elasticity. A recently proposed innovization procedure is used to unveil salient properties associated with the trade-off solutions. The obtained Pareto fronts are used successfully to study the role of various parameters influencing the equivalent thermal conductivity and modulus of elasticity of the composite mold material. The optimal selection of materials is suggested in consideration with the cost implication factor based on the findings through investigations.

Modelling and Analysis of Cutting Force and Surface Roughness in Milling Operation Using TSK-Type Fuzzy Rules

The present paper discusses on development of fuzzy rule based models (FRBMs) for predicting cutting force and surface roughness in milling operation. The models use TakagiSugeno-Kang-type (TSK-type) fuzzy rule to study the effect of four (input) cutting parameters (cutting speed, feed rate, radial depth of cut and axial depth of cut) on outputs (cutting force and surface roughness). The appropriate FRBM is arrived after a thorough investigation of different structures of rule-consequent function. A combined approach of genetic algorithm and multiple linear regression method is used to determine the rule-consequent parameters. Performance analysis of models by comparing with experimental data implies its potential towards practical application. Analysis of the influence of various input parameters on different outputs is carried out based on FRBMs and experimental data. It suggests that the cutting force becomes higher with increasing feed rate, axial depth of cut and radial depth of cut and lower with increase in cutting speed, whereas surface finish is improved with increase in cutting speed and gets poorer with increase in radial depth of cut.

Studies on effective thermal conductivity of particle-reinforced polymeric flexible mould material composites

Due to poor thermal conductivity of conventional flexible polymeric mould materials, the solidification of (wax/plastic) patterns in soft tooling (ST) process takes a longer time. This problem can be solved by increasing the effective thermal conductivity of mould materials through (high thermal conductive) particle reinforcement. Therefore in this study, the equivalent thermal conductivities (ETCs) of particle-reinforced polymeric mould materials, namely silicone rubber and polyurethane are experimentally observed using hot disc technique. Findings show that not only the amount of filler content and type of filler material, but also particle size has significant influence on the effective thermal conductivity of polymer and it starts increasing drastically at 20–30 per cent volume fraction of filler content. To predict the cooling time in ST process, it is important to have an appropriate model of ETC. In this study, a new method is proposed based on a genetic algorithm fuzzy (GA-fuzzy) approach to model the effective thermal conductivity of a two-phase particle-reinforced polymer composites (PCs). The effectiveness of the model is extensively tested in comparison with various empirical expressions reported in literature based on the experimental measurements. It has been found that the model based on GA-fuzzy approach not only outperforms the existing models, but also possesses a generic one applicable to a wide range of two-phase particle-reinforced PCs.

Detecting Ambiguities in Regression Problems using TSK Models

Regression refers to the problem of approximating measured data that are assumed to be produced by an underlying, possibly noisy function. However, in real applications the assumption that the data represent samples from one function is sometimes wrong. For instance, in process control different strategies might be used to achieve the same goal. Any regression model, trying to fit such data as good as possible, must fail, since it can only find an intermediate compromise between the different strategies by which the data were produced. To tackle this problem, an approach is proposed here to detect ambiguities in regression problems by selecting a subset of data from the total data set using TSK models, which work in parallel by sharing the data with each other in every step. The proposed approach is verified with artificial data, and finally utilised to real data of grinding, a manufacturing process used to generate smooth surfaces on work pieces.

A genetic fuzzy based modeling of effective thermal conductivity for polymer composites

Evaluation of equivalent thermal conductivity ETC of particle reinforced polymer composites PRPCs is a complex process since some of the influencing parameters are associated with uncertainties and ambiguities e.g., dispersion state of filler in the matrix, uniformity of filler particle size and shape, etc. By realizing it, an attempt has been made to model the ETC of 2-phase PRPCs based on a genetic fuzzy approach. The model performance is rigorously tested in three stages to establish its practical applicability: based on experimental data not used in model development cited in literature, new measured thermal conductivities of flexible mould composites and finally by assessing the feasibility of values of missing data in the reported in-complete data set based on the developed model. Estimations of ETC by the proposed model are shown reasonable, even better compare to existing models and suggesting a generic model applicable to a wide range of 2-phase PRPCs.