Fused deposition modeling (FDM) is a process by which functional parts can be produced rapidly through deposition of fused layers of material according to a numerically defined cross-sectional geometry. Literature suggests that process parameters largely influence on quality characteristics of rapid prototyping (RP) parts. A functional part is subjected to different loading conditions in actual practice. Therefore, process parameters need to be determined in such a way that they collectively optimize more than one response simultaneously. To address this issue, effect of important process parameters viz., layer thickness, orientation, raster angle, raster width, and air gap have been studied. The responses considered in this study are mechanical property of FDM produced parts such as tensile, bending and impact strength. The multiple responses are converted into a single response using principal component analysis (PCA) so that influence of correlation among the responses can be eliminated. Resulting single response is nothing but the weighted sum of three principal components that explain almost hundred percent of variation. The experiments have been conducted in accordance with Taguchi's orthogonal array to reduce the experimental runs. The results indicate that all the factors such as layer thickness, orientation, raster angle, raster width and air gap and interaction between layer thickness and orientation significantly influence the response. Optimum parameter settings have been identified to simultaneously optimize three responses. The mechanism of failure is explained with the help of SEM micrographs.

Short lignocellulosic fibres are extensively used these days as reinforcing materials in many thermoset and thermoplastic matrices due to their low cost, lower density than inorganic fibres, environmentally-friendliness, and the relative ease of obtaining them. Such fibres would not contribute to the wear and tear of polymer processing equipment and may not suffer from size reduction during processing, both of which occur when inorganic fibres or fillers are used. These fibres can also be easily moulded to wide variety of shapes during composite preparation. However, modelling and analysis of behaviour of composites reinforced with short fibre drawn from agricultural resources has been studied to a limited extent. Particularly, the optimum size of short fibre just capable of transferring the load and flexibility during preparation has not been studied through a simple systematic modelling approach due to the complexity involved in its modelling aspect. To this end, an attempt has been made in this work to study the abrasive behaviour of untreated sugarcane fibre reinforced composites in a simplified manner and develop empirical model. The effect of various test parameters and their interactions have been studied using Taguchi method to find out optimal parameter setting for minimum wear (weight loss). It has been observed that fibre length plays a major role in wear phenomenon. The length of the fibre has been optimized using a popular evolutionary technique known as particle swarm optimization (PSO) and neural network. The study recommends that fibre length should be 7-8 mm for minimum wear of the composites.

As the population of the world is continuously increasing, the demand of the mechanical manufactured products is also increasing. Machining is the most important process in any mechanical manufacturing, and in machining two factors i.e. Material removal rate (MRR) and Surface roughness (SR) are the most important responses. If the MRR will be high, the product will get desired shape in minimum time so the production rate will be high, but we could not scarify with the surface finishing also because in close tolerance limit parts like in automobile industry if the surface is rough exact fit cannot take place. So here aim is to maximise MRR and minimise surface roughness and process control variable are taken to be transverse speed, standoff distance, abrasive flow rate, and water pressure. Here Grey relational analysis is used to convert multi responses into single response and optimal parameter setting and most significant parameter is found with the help of S/N ratio.

Rapid Prototyping (RP) technology has become the powerful tool for product development in almost every branch of engineering. Many new and upcoming processes offer means for the fast creation of models with steadily increasing accuracy, built speed, other model properties and economic advantages. Fused Deposition Modeling (FDM) is the most famous and commercially available RP system. This paper presents the application of Utility concept with Taguchi method for multiresponse optimization of the FDM process. Stratatys Fortus 400[Formula: see text]mc FDM setup is used to conduct experiments as per Taguchi’s L9 orthogonal array. FDM parameters: Layer thickness, part orientation and raster angle were optimized based on multiple responses, i.e. tensile, flexural, impact and compressive strength. The optimum process parameters are calculated using utility concept. The Analysis of variance (ANOVA) is applied to find out the most significant factor. It has been found that layer thickness is the most significant factor, followed by part orientation and raster angle. The confirmation tests with optimal levels of process parameters are conducted to illustrate the efficacy of the proposed method. It is found that optimum combination of process parameters gives the highest utility value, which indicates that multiresponses of the FDM process can be improved through this approach.

We seldom realized that without this form of metal work, many structures would cease to be in existence. A skilled journeymen welder joins metal in such a way that it is not able to be parted unless it is cut. Welding is an absolutely essential technique used in various industries like automotive industry, construction industry as well as in the aviation industry. Hence welding process parameters are required to be optimized for their responses or welding characteristics. This study incorporates entropy measurement technique based on grey Taguchi method to analyze multiple quality characteristic optimization of metal inert gas welding of low carbon steel plates. For this study, four control variables are selected current, voltage, gas flow rate and wire feed rate and analysing their effect on the four quality characteristics ultimate tensile strength, elongation %, bending strength and hardness of the weldments have been investigated in this paper. In order to optimize the multiple quality characteristic of the MIG welding, grey relational analysis coupled with entropy measurement method has been employed. Using entropy measurement method, value of weight corresponding to each quality characteristic has been assigned, so that the importance can be properly determined. Using the theory of grey relational analysis, these have been accumulated to calculate the overall grey relational grade. Signal to Noise ratio (S/N ratio) is applied to find the optimal parameter setting. To determine the contribution of MIG welding process parameters, Analysis of variance (ANOVA) on grey relational grade has been calculated. The confirmatory test also has been done for verifying the results. A foresaid methodology has been found fruitful in the cases where simultaneous optimization of huge number of responses is required.