• Design & Manufacturing
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• v.14 no.3
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• pp.1-7
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• 2020

This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.

• IE interfaces
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• v.14 no.1
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• pp.20-29
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• 2001

This paper presents a concurrent design approach that deals with manufacturing cell formation and cellular layout in Cellular Manufacturing System. Manufacturing cell formation is to group machines into machine cells dedicated to manufacture of part families, and cellular layout problem determines layout of the manufacturing cells within shop and layout of the machines within a cell. In this paper, a concurrent approach for design of machine cell and cellular layout is developed considering manufacturing parameters such as alternative process plans, alternative machines, production volume and processing time of part, and cost per unit time of operation. A mathematical model which minimizes total cost consisting of machine installation cost, machine operating cost, and intercell and intracell movements cost of part is proposed. A hybrid method based on genetic algorithm is proposed to solve the manufacturing cell formation and cellular layout design problem concurrently. The performance of the hybrid method is examined on several problems.

• Journal of Welding and Joining
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• v.34 no.3
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• pp.23-30
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• 2016

This paper discusses the optimization of friction stir spot welding (FSSW) process parameters for joining Aluminum alloy (AA6061-T6) with Magnesium alloy (AZ31B) sheets. Prior to optimization an empirical relationship was developed to predict the Tensile Shear Fracture Load (TSFL) incorporating the four most important FSSW parameters, i.e., tool rotational speed, plunge rate, dwell time and tool diameter ratio, using response surface methodology (RSM). The experiments were conducted based on four factor, five levels central composite rotatable design (CCD) matrix. The maximum TSFL obtained was 3.61kN, with the tool rotation of 1000 rpm, plunge rate of 16 mm/min, dwell time of 5 sec and tool diameter ratio of 2.5.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2001.05a
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• pp.184-188
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• 2001

The squeeze infiltration process is potentially of considerable industrial importance. The performance enhancements resulting from incorporation of short alumina fiber into aluminum are well documented. These are particularly significant for certain automobile components. But the solidification process gets complicated with manufacturing parameters and factors for porosity formation do not fully understand yet. In this study porosity defects were observed under several infiltrating conditions ; a kind of matrix, an initial temperature of melt, and a volume fraction of reinforcement. The desimetry and the microscopic image analysis were done to measure the amount of porosity. A correlation between manufacturing parameters and defects was investigated through these.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.1 no.1
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• pp.133-140
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• 2002

The object of this paper is to predict the surface roughness using the experiment equation of surface roughness, which is developed with a full factorial design in turning. $3^3$ full factorial design has been used to study main and interaction effects of main cutting parameters such as cutting speed, feed rate, and depth of cut, on surface roughness. For prediction of surface roughness, the arithmetic average (Ra) is used, and stepwise regression has been used to check the significance of all effects of cutting parameters. Using the result of these, the experimental equation of surface roughness, which consists of significant effects of cutting parameters, has been developed. The coefficient of determination of this equation is 0.9908. And the prediction ability of this equation was verified by additional experiments. The result of that, the coefficient of determination is 0.9718.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.2
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• pp.18-24
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• 2004

The effect of surface characterization parameters, such as surface roughness, skewness and kurtosis, on sliding friction and wear was studied experimentally. The friction coefficient was examined under the various parameters and sliding speed, normal load and type of lubricant with ball-on-disk type tribo-meter. The surface of the lower skewness in negative or the higher kurtosis between the same arithmetic mean value tends to indicate low friction.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.4
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• pp.46-53
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• 1997

This paper presents a method of detection localized defects on tapered roller bearing in main spindle of machine tool system. The feature vectors, i.e. statistical parameters, in time-domain analysis technique have been calculated to extract useful features from acoustic emission signals. These feature vectors are used as the input feature of an neural network to classify and detect bearing defects. As a results, the detection of bearing defect conditions could be sucessfully performed by using an neural network with statistical parameters of acoustic emission signals.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.5
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• pp.70-75
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• 1999

Adaptive control in the robotic GMA(Gas Metal Arc) welding is employed to monitor the information about weld characteristics and process paramters as well as modification of those parameters to hold weld quality within the acceptable limits. Typical characteristics are the bead geometry composition micrrostructure appearance and process parameters which govern the quality of the final weld. The main objectives of this paper are to realize the mapping characteristicso f penetration through the learning. After learning the neural network can predict the pene-traition desired from the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) were chosen from an error analysis. partial-penetration single-pass bead-on-plate welds were fabricated in 12mm mild steel plates in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the penetration with reasonable accuracy and gurarantee the uniform weld quality.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.1
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• pp.45-49
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• 2013

It is important to have a well developed strain energy function in order to understand the mechanical behavior of biomaterial like the blood vessel of artery. However, since it is not possible to have a complete form of strain energy function of artery, theoretical framework describing the behaviour of Rubber-like material which is similar to blood vessel is applied to infer useful forms of strain energy function of biomaterial. Based on Chuong-Fung model and Mooney-Rivlin model, material parameters are estimated based on experimental data. From the results, it can be inferred that the estimated parameters can be used to explain the difference of mechanical characteristics between normal vessel and vessel with stent.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1994.06a
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• pp.163-172
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• 1994

The Aerospace and automobile industries have need to avoid sheet-metal forming problem such as incorrect springback after forming and trimming process, excessive thinning/tearing, wrinking/perkering. It is common practice to use costly trial-and-error experimental methods to develop tooling and manufacturing process parameters. Experimentation should be complemented with computer simulation to reduce cost and leadtime in manufacturing and to influence the design of components. In this study, firstly we solved the springback problem after drawing and trimming process of KFP(F100-229) engine airsealing bearing support part(53H00) forming and studied on the effect of several process parameters on the gap between the formed blank and punch shape using the implicit F.E.M code(ABAQUS). Secondly by the three dimensional dynamic analysis using the explicit. F. E. M code (LS-DYNA3D), we studied on the effect of several process parameters which can be used for avoid tearing and wrinking during the drawing process.