Single-point incremental forming is a novel and flexible method for producing three-dimensional parts from metal sheets. Although single-point incremental forming is a suitable method for rapid prototyping of sheet metal components, there are limitations and challenges facing the commercialization of this process. Dimensional accuracy, surface quality, and production time are of vital importance in any manufacturing process. The present study is aimed at selecting proper forming parameters to produce sheet metal parts which possess dimensional accuracy and good surface quality at the shortest time. Four parameters (i.e. tool diameter, tool step depth, sheet thickness, and feed rate) are chosen as design variables. These parameters are used for the modeling of the process using Group Method of Data Handing(GMDH) artificial neural networks. The data necessary for establishing empirical models are obtained from single-point incremental forming experiments carried out on a computer numerical control milling machine using central composite design. After the evaluation of the model accuracy, single- and multi-objective optimization are performed via genetic algorithm. The performance of the design variables of a tradeoff point corresponding to one of the experiments shows the efficiency and accuracy of the models and the optimization process. Considering the priorities of objective functions, a designer will be able to set proper process parameters.
Electrochemical machining is a unique prevalent nonconventional manufacturing process used in different industries involving various process parameters, which greatly influence machining performance. Therefore, selection of proper and optimal parameters setting is a challenging issue. In this paper, differential evolution algorithm is applied to look for the optimum solution to this problem. Four parameters, i.e. voltage, tool feed rate, electrolyte flow rate, and electrolyte concentration; and two machining criteria, i.e. material removal rate and surface roughness (R a) are considered as input variables and responses, respectively. The main purpose is to maximize material removal rate and minimize R a to achieve better machining performance. In this way, comprehensive mathematical models have first been developed using response surface methodology through experimentation based on central composite design plan. Then, differential evolution algorithm has been utilized for optimizing the process parameters; both single-and multiobjective optimizations are considered, and optimal Pareto front is determined. Finally, optimization result of a trade-off design point in the Pareto front of R a and material removal rate was also verified experimentally. This machined surface was examined with field-emission scanning electron microscope images. The results showed that the proposed approach is an effective and suitable strategy for optimization of the electrochemical machining process.
In order to measure temperature fields on tool face during cutting, a cutting tool with built-in thin film thermocouples (TFTs) has been devised. The TFTs composed of a nickel and nichrome thin films were fabricated on the rake face near the cutting edge of a sintered alumina tool insert using a physical vapor deposition and photolithography technique. An empirical formula that shows Seebeck coefficient of a TFT depends on electrical resistance of the TFT circuit was established. Three different types of tools in number and size of TFTs were developed and temperature fields on the rake face in cutting of a plain carbon steel S45C were measured. The results of the cutting thermometry experiment reveal that the devised tool with built-in three TFTs can measure temperature fields on the tool face and can sense slight change in cutting situation.
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