Laser beam cutting is one important nontraditional machining process. This paper optimizes the parameters of laser beam cutting parameters of stainless steel (316L) considering the effect of input parameters such as power, oxygen pressure, frequency and cutting speed. Statistical design of experiments is carried in three different levels and process responses such as average kerf taper (Ta), surface roughness (Ra) and heat affected zones are measured accordingly. A response surface model is developed as a function of the process parameters. Responses predicted by the models (as per Taguchi’s L27OA) are employed to search for an optimal combination to achieve desired process yield. Response Surface Models (RSMs) are developed for mean responses, S/N ratio, and standard deviation of responses. Optimization models are formulated as single objective optimization problem subject to process constraints. Models are formulated based on Analysis of Variance (ANOVA) and optimized using Matlab developed environment. Optimum solutions are compared with Taguchi Methodology results. As such, practicing engineers have means to model, analyze and optimize nontraditional machining processes. Validation experiments are carried to verify the developed models with success.

A plate forging of a drawn cup with a flange including thickening of corners was developed to improve the stiffness of the cup. In a 3-stage stamping process, the bottom and flange corners of the cup with a flange were thickened by the plate forging. A circular blank was drawn with conical punches in the 1st and 2nd stages. In the 3rd stage, the bottom and flange corners were thickened by flattening the conical bottom of the cup with a flange. Both the bottom and flange corners of the cup were thickened by compressing the flange corner in the latter half of the 3rd stage. The amounts of thickening at both corners were improved by modification of intermediate shape. The thickening was concentrated around the two corners by preventing the thickening of the inclined portion. A wheel disk was formed by 6-stage stamping having the following three stages. The disk had 15% and 10% thickening at the bottom and flange corners, respectively, and the bending stiffness of the disk was increased. It was found that the proposed plate forging of sheet metal was considerably effective in increasing in the stiffness and reducing in the weight of automobile parts.

Laser bending of sheet metals with preload offers some attractive characteristics/merits, comparing to laser free bending without prestressing on the metals. The study reported in this paper was focused on a Titanium alloy which finds widespread applications in aerospace manufacturing. FE simulation of laser bending with prestressing on the Titanium alloy sheet was conducted for the analysis of the bending process and experiment carried out to verify the model and the result. It was shown that the simulation result is close to that measured in the experiment. Based on the computed result, the load-displacement curve was analysed and transmission efficiency of the elastic energy defined to evaluate the bending effect. These enhanced understanding of the mechanism of laser bending with a preload. A method for the optimization on technological parameters was further proposed. Referring to the deformation targeted, the preload value was determined through the FE simulation. The result showed that, on the premise that the specimen surface can be prevented from damaging, transmission efficiency of the elastic energy could reach to the maximum value through adjusting technological parameters of the laser system and deformation accuracy of the specimen could also be improved through this approach. The work presented in this paper may find its application in the manufacture of Titanium alloy sheets with a more cost-effective and a more precise way.

Microstructural evolution, which is governed by temperature, strain and strain rate during hot forging, is a key factor influencing mechanical properties. Understanding the microstructural evolution of metals and alloys in hot forging has a great importance for the designers of metal forming processes. The principal objective of this paper is to provide an overview of the models for the prediction of microstructural evolution for metals and alloys during the hot forging process. In this review paper, the models are divided into four categories, including the phenomenological, physically-based, mesoscale and artificial-neural-network models, to introduce their developments, prediction capabilities and application scopes. Additionally, some limitations and objective suggestions for the further development of the modelling of microstructural evolution during hot forging are proposed.

\n\t\t\t\tResearch developments in the application of vegetable-oil based metalworking fluids (MWFs) in machining processes have witnessed a great attention in recent years, this being due to the environmental friendliness and performances recorded while machining. In the work reported in this paper, the authors surveyed relevant literatures, identified gaps in the application of vegetable-oil based MWFs, and proposed challenges and needs to fill the gaps created as a result of new development in engineering material applications. The engineering materials for the machining processes addressed were classified into three categories: ferrous materials, non-ferrous materials and super alloys materials. This survey is both timeline and process defined. It identified gaps in the research developments at various periods of time with respect to applications to various processes. It concluded by making a case for application of vegetable-oil based MWFs for milling and grinding processes and super alloy materials as a way of addressing environmental impact that is always associated with application of mineral-oil based MWFs.\n\t\t\t

Pulsed Nd:YAG laser has high intensity and high quality beam characteristics, which can be used to produce micro-grooves and micro-turning surface on advanced engineering ceramics. The present research attempts to develop mathematical models by using response surface methodology approach for correlating the machining process parameters and the process responses during laser micro-turning of aluminum oxide (Al2O3) ceramics. The process parameters such as laser average power, pulse frequency, workpiece rotating speed, assist air pressure and Y feed rate were varied during experimentation. The rotatable central composite design experimental planning has been used to design the experimentation. The performance measures considered are surface roughness (Ra) and micro-turning depth deviation. Multi-objective optimization has been carried out for achieving the desired surface roughness as well as minimum depth deviation during laser micro-turning operation. Further, an artificial neural network (ANN) model has been developed to predict the process criteria. Levenberg-Marquadt training algorithm is used for multilayer feed forward backpropagation neural network. The developed ANN model has 5-10-2 feed forward network. There are 5 neurons in the input layer, 10 neurons in the hidden layer and 2 neurons in the output layers corresponding to two output responses, respectively. The developed ANN model has been validated using data obtained by conducting additional set of experiments. It was found that the developed ANN model can predict the process criteria more accurately than response surface methodology (RSM) based developed models.

In this paper, a hybrid flow shop scheduling problem with a new approach considering time lags and sequence-dependent setup time in realistic situations is presented. Since few works have been implemented in this field, the necessity of finding better solutions is a motivation to extend heuristic or meta-heuristic algorithms. This type of production system is found in industries such as food processing, chemical, textile, metallurgical, printed circuit board, and automobile manufacturing. A mixed integer linear programming (MILP) model is proposed to minimize the makespan. Since this problem is known as NP-Hard class, a meta-heuristic algorithm, named Genetic Algorithm (GA), and three heuristic algorithms (Johnson, SPTCH and Palmer) are proposed. Numerical experiments of different sizes are implemented to evaluate the performance of presented mathematical programming model and the designed GA in compare to heuristic algorithms and a benchmark algorithm. Computational results indicate that the designed GA can produce near optimal solutions in a short computational time for different size problems.

The presented results are based on an experimental feasibility study which was carried out for the application of micro-hydroforming in the manufacture of micro-sized products made from the material PtIr10. Within the scope of this product development, the prediction of feasible and failure-free expansions was one major aspect. This paper deals with the analysis of the failure cases which limited the expansions due to necking. The experimental investigations have shown a scattering of the forming results which was due to so-called size-effects resulting from the material grain structure. Simulations with the finite element method were used to determine the development of stresses and strains within the hydroformed tube. It was demonstrated that a superimposition of axial compressive stresses reduces the influence of size-effects and increases the feasible expansion diameter. Using these simulations, the applicability of an existing failure criterion to estimate a mean forming-limit was verified.