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Journal articles on the topic 'Shrinkage defects prediction'
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1
Kim, Sung Bin, Young Hoon Yim, Joong Mook Yoon, and Doru Michael Ştefănescu. "Prediction of Shrinkage Defects in Iron Castings Using a Microporosity Model." Materials Science Forum 925 (June 2018): 411–18. http://dx.doi.org/10.4028/www.scientific.net/msf.925.411.
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A numerical model for prediction of shrinkage defects in iron castings has been developed. The model is based on gas pores evolution during solidification. It describes the evolution of gas concentration using mass conservation, and the change in melt pressure due to solidification contraction using Darcy’s equation, with mixture continuity assumption in the liquid and the mushy zone. Gas pores nucleation has been calculated using the partial pressure of gas obtained from Sievert’s law. The growth of porosity has been estimated using an equation based upon the total melt pressure on the pore, concentration and temperature of the gas. The porosity model was calibrated against literature data for microporosity, and then applied to the prediction of shrinkage defects in a ductile iron casting. Comparison between the model predictions and experimental measurements indicated that the porosity model can be applied not only to the prediction of micro-shrinkage but also to that of macro-shrinkage. Existing shrinkage prediction models based upon thermal models, such as Niyama criterion and the modulus of retained melt in mushy regions cannot predict correctly both micro- and macro-shrinkage.
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Mahardika, Muslim, and A. Syamsudin. "Prediction of Shrinkage Porosity in Femoral Stem of Titanium Investment Casting." Archives of Foundry Engineering 16, no. 4 (2016): 157–62. http://dx.doi.org/10.1515/afe-2016-0102.
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Abstract Design of gating system is an important factor in obtaining defect-free casting. One of the casting defects is a porosity caused by internal shrinkage in solidification process. Prediction of the internal shrinkage porosity in the femoral stem of commercially pure titanium (CP-Ti) is investigated based on the gating system design. The objective of this research is to get the best gating system between three gating system designs. Three gating system designs of the femoral stem were simulated in an investment casting method. The internal shrinkage porosity occurs on the largest part and near the ingate of the femoral stem. The gating system design that has ingates cross section area: 78.5; 157; and 128.5 mm2 has the least of the internal shrinkage porosity. This design has the most uniform solidification in the entire of the femoral stem. An experiment is conducted to validate the simulation data. The results of internal shrinkage porosity in the three gating system designs in the simulation were compared with the experiment. Based on the comparison, the trend of internal shrinkage porosity at the three gating system designs in the simulation agrees with the experiment. The results of this study will aid in the elimination of casting defect.
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Hajkowski, J., P. Roquet, M. Khamashta, E. Codina, and Z. Ignaszak. "Validation Tests of Prediction Modules of Shrinkage Defects in Cast Iron Sample." Archives of Foundry Engineering 17, no. 1 (2017): 57–66. http://dx.doi.org/10.1515/afe-2017-0011.
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Abstract The paper presents the results of experimental-simulation tests of expansion-shrinkage phenomena occurring in cast iron castings. The tests were based on the standard test for inspecting the tendency of steel-carbon alloys to create compacted discontinuities of the pipe shrinkage type. The cast alloy was a high-silicone ductile iron of GJS - 600 - 10 grade. The validation regarding correctness of prognoses of the shrinkage defects was applied mostly to the simulation code (system) NovaFlow & Solid CV (NFS CV). The obtained results were referred to the results obtained using the Procast system (macro- and micromodel). The analysis of sensitivity of the modules responsible for predicting the shrinkage discontinuities on selected pre-processing parameters was performed, focusing mostly on critical fractions concerning the feeding flows (mass and capillary) and variation of initial temperature of the alloy in the mould and heat transfer coefficient (HTC) on the casting - chill interface.
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Tavakoli, Rouhollah. "On the prediction of shrinkage defects by thermal criterion functions." International Journal of Advanced Manufacturing Technology 74, no. 1-4 (2014): 569–79. http://dx.doi.org/10.1007/s00170-014-5995-0.
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Konečná, Radomila, S. Fintová, and Gianni Nicoletto. "Shrinkage Pores and Fatigue Behavior of Cast Al-Si Alloys." Key Engineering Materials 465 (January 2011): 354–57. http://dx.doi.org/10.4028/www.scientific.net/kem.465.354.
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The fatigue strength of cast Al-Si alloys is strongly sensitive to the presence of casting defects. Extensive rotating bending fatigue testing of cast AlSi7Mg alloy at room temperature and 50 Hz is reported showing that shrinkage pores are the critical casting defect. The porosity of selected fatigue specimens extracted from castings is characterized with metallography using different pore sizing criteria. Data are fitted to EVS distributions and used for critical size prediction. Fatigue fracture surfaces are inspected in the SEM and the critical pores originating the fatigue cracks identified and measured according to criteria used in the metallographic inspection.
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Sowa, Leszek, Tomasz Skrzypczak, and Paweł Kwiatoń. "The influence of the riser dimensions on the effectiveness of feeding solidifying cast elements." MATEC Web of Conferences 254 (2019): 02016. http://dx.doi.org/10.1051/matecconf/201925402016.
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The mathematical model and numerical simulation results of metals alloy solidification process based on the finite element method (FEM) are presented in this paper. After completion of the mould filling process the main solidified of molten metal takes place and its shrinkage. The phenomenon of casting shrinkage cannot be avoided. However, it is possible to minimize the occurrence of its negative effects on the casting quality. The phenomenon of shrinkage defects formation was included in the numerical calculations and they were tried to reduce them. It is important aspect of this work which makes possible the prediction of the location of the casting defect depending on shape of the riser. The results of computer calculations of the solidification process of the three-dimensional casting together with the conical or cylindrical riser are discussed in detail and presented.
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Ignaszak, Z. "Discussion on Usability of the Niyama Criterion for Porosity Predicting in Cast Iron Castings." Archives of Foundry Engineering 17, no. 3 (2017): 196–204. http://dx.doi.org/10.1515/afe-2017-0115.
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Abstract The paper refers to previous publications of the author, focused on criteria of casting feeding, including the thermal criterion proposed by Niyama. On the basis of this criterion, present in the post-processing of practically all the simulation codes, danger of casting compactness (in the sense of soundness) in form of a microporosity, caused by the shrinkage phenomena, is predicted. The vast majority of publications in this field concerns shrinkage and feeding phenomena in the cast steel castings – these are the alloys, in which parallel expansion phenomenon does not occur as in the cast irons (graphite crystallization). The paper, basing on the simulation-experimental studies, presents problems of usability of a classic, definition-based approach to the Niyama criterion for the cast iron castings, especially of greater massiveness, for prediction of presence of zones of dispersed porosity, with relation to predictions of the shrinkage type defects. The graphite expansion and its influence on shrinkage compensation during solidification of eutectic is also discussed.
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Reis, A., Z. A. Xu, R. V. Tol, et al. "Model for prediction of shrinkage defects in long and short freezing range materials." International Journal of Cast Metals Research 20, no. 3 (2007): 171–75. http://dx.doi.org/10.1179/136404607x239771.
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Landry, Benoit, and Pascal Hubert. "Process modelling of discontinuous long fibre carbon/polyether ether ketone composites: Defect prediction." Journal of Composite Materials 53, no. 18 (2019): 2505–15. http://dx.doi.org/10.1177/0021998319831757.
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A numerical model was developed to predict the defect formation during processing of compression moulded discontinuous long fibre carbon/polyether ether ketone composites. The model inputs are the material's temperature-dependant properties (through-thickness modulus and thermal shrinkage), the temperature distribution of the part during cooling and the applied moulding pressure. The material properties of carbon/polyether ether ketone prepreg were measured during cooling from melt using thermal analyses. The model was employed to identify regions on manufactured panels where pressure could be lost during cooling, which are prone to defect formation. Validation was performed by comparing the predicted defect areas against those found on flat panels moulded at pressures ranging from 10 to 110 bar. The model was then employed in a case study to show the importance of the cooling strategy in order to prevent defects on complex-shape components.
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Huang, Zhicheng, Jean-Yves Dantan, Alain Etienne, Mickaël Rivette, and Nicolas Bonnet. "Geometrical deviation identification and prediction method for additive manufacturing." Rapid Prototyping Journal 24, no. 9 (2018): 1524–38. http://dx.doi.org/10.1108/rpj-07-2017-0137.
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Purpose One major problem preventing further application and benefits from additive manufacturing (AM) nowadays is that AM build parts always end up with poor geometrical quality. To help improving geometrical quality for AM, this study aims to propose geometrical deviation identification and prediction method for AM, which could be used for identifying the factors, forms and values of geometrical deviation of AM parts. Design/methodology/approach This paper applied the skin model-based modal decomposition approach to describe the geometrical deviations of AM and decompose them into different defect modes. On that basis, the approach to propose and extend defect modes was developed. Identification and prediction of the geometrical deviations were then carried out with this method. Finally, a case study with cylinders manufactured by fused deposition modeling was introduced. Two coordinate measuring machine (CMM) machines with different measure methods were used to verify the effectiveness of the methods and modes proposed. Findings The case study results with two different CMM machines are very close, which shows that the method and modes proposed by this paper are very effective. Also, the results indicate that the main geometrical defects are caused by the shrinkage and machine inaccuracy-induced errors which have not been studied enough. Originality/value This work could be used for identifying and predicting the forms and values of AM geometrical deviation, which could help realize the improvement of AM part geometrical quality in design phase more purposefully.
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Yıldirimm, Kenan, Hamdi Ogut, and Yusuf Ulcay. "Comparing the Prediction Capabilities of Artificial Neural Network (ANN) and Nonlinear Regression Models in Pet-Poy Yarn Characteristics and Optimization of Yarn Production Conditions." Journal of Engineered Fibers and Fabrics 12, no. 3 (2017): 155892501701200. http://dx.doi.org/10.1177/155892501701200302.
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In the manufacture of yarn, predicting the effect of changing production conditions is vital to reducing defects in the end product. This study compares, for the first time, non-linear regression and artificial neural network (ANN) models in predicting 10 yarn properties shaped by the influence of winding speed, quenching air temperature and/or quenching air speed during production. A multilayer perceptron ANN model was created by training 81 patterns using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. The hyperbolic tangent, or TanH, activation function and logistic activation functions were used for the hidden and output layers respectively. Results showed that the ANN approach exhibited a greater prediction capability over the nonlinear regression method. ANN simultaneously predicted all of the 10 final properties of a yarn; tensile strength, tensile strain, draw force, crystallinity ratio, dye uptake based on the colour strengths (K/S), brightness, boiling shrinkage and yarn evenness, more accurately than the non-linear regression model (R2=0.97 vs. R2=0.92). These results lend support to the idea that the ANN analysis combined with optimization can be used successfully to prevent production defects by fine tuning the production environment.
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Muikku, Arto, Jari Hartikainen, Sami Vapalahti, and Tuomo Tiainen. "Experimental Work on Possibilities to Predict Casting Defects in LPDC Brass Castings." Materials Science Forum 508 (March 2006): 561–66. http://dx.doi.org/10.4028/www.scientific.net/msf.508.561.
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In water tap production 0.5 mm of material needs to be ground off from the surface of LPDC (Low Pressure Die Casting) brass castings in order to remove the defects deteriorating the quality of later applied coating. In order to minimize the amount of removed material the causes of these defects need to be discovered and properly connected with the process parameter window. At Oras Oy foundry in Finland, nearly 100 castings were produced under actual process conditions. To monitor the process seven thermocouples were inserted into the die. Thermal camera was also used for monitoring the die conditions during the open time of the die. Castings were divided into sets of ten pieces for statistical reasons. A few key process parameters were selected based on the basis of earlier knowledge and they were systematically varied during casting experiments. Each cast piece was marked and later analysed in order to find the dependencies between detected defects and process parameters. Computer simulations of the process were conducted to study the possibility to use numerical simulations for defect prediction. It was found that shrinkage defects could be reasonably well predicted and the influence of the process parameters on their formation was also apparent. The predictability of surface defects, however, was poor and only indirect conclusions could be made. Observations were made using as cast, ground and polished and cut surfaces from certain sections of the castings. It is very difficult to make any conclusions on surface defect formation based on parameter variation. One reason probably is the too narrow process window, but several promising ideas on the influence of e.g. mould shape, temperature and composition of the graphite coating on the defect formation was discovered.
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Shuaib, Norshah Afizi, M. Fathullah, Z. Shayfull, S. M. Nasir, and M. F. M. A. Hamzas. "Warpage Analysis between Straight and Conformal Cooling Channels on Thin Shallow Shell." Key Engineering Materials 594-595 (December 2013): 676–85. http://dx.doi.org/10.4028/www.scientific.net/kem.594-595.676.
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Warpage is a type mold defect due to non-uniform temperature variation which causes differential shrinkage rate on the moulded parts. An accurate warpage prediction is so important in helping mould designers to achieve successful mold design with minimum warpage defects. This work is performed with a purpose to determine and compare the best parameters can be selected in manufacturing of thin shallow shell using two different types of cooling channels which are straight cooling channels and conformal cooling channels. The results were obtained using Taguchi Method and Analysis of Variance (ANOVA) and run through simulation software. Both parameters are then compared with each other in recommending to the mold designers which is the best to be applied at mold design stage. It has been found from this work that two factors that significantly cause warpage on both cooling channels are packing pressure and filling time.
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Zhang, Dawei, Yu Zeng, Mingshan Fang, and Weiliang Jin. "Service Life Prediction of Precast Concrete Structures Exposed to Chloride Environment." Advances in Civil Engineering 2019 (May 2, 2019): 1–14. http://dx.doi.org/10.1155/2019/3216328.
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Chloride-induced corrosion is widely accepted as one of the primary causes of premature deterioration for concrete structures in marine or deicing salt environment. For precast concrete (PC) structures, such durability problems may even be severer because defects in joint areas, e.g., cracks caused by grout shrinkage and improper construction, can accelerate chloride ion transportation process and may cause the interface shear failure when subjected to seismic load. By applying the path probability model (PPM) and reliability theory, a probabilistic framework was proposed to predict three limit states of PC structures, including corrosion initiation, serviceability limit state, and ultimate limit state. Using Monte Carlo simulation, a beam-to-column joint was further analyzed to illustrate the differences between PC structures and those cast in situ. The analysis indicates that corrosion initiation and serviceability limit state are sensitive to chloride diffusivity at connection area, and the higher pitting factor can significantly influence the bearing capacities of PC structures.
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Ly, Hai-Bang, Eric Monteiro, Tien-Thinh Le, et al. "Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees." Materials 12, no. 9 (2019): 1544. http://dx.doi.org/10.3390/ma12091544.
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The presence of defects like gas bubble in fabricated parts is inherent in the selective laser sintering process and the prediction of bubble shrinkage dynamics is crucial. In this paper, two artificial intelligence (AI) models based on Decision Trees algorithm were constructed in order to predict bubble dissolution time, namely the Ensemble Bagged Trees (EDT Bagged) and Ensemble Boosted Trees (EDT Boosted). A metadata including 68644 data were generated with the help of our previously developed numerical tool. The AI models used the initial bubble size, external domain size, diffusion coefficient, surface tension, viscosity, initial concentration, and chamber pressure as input parameters, whereas bubble dissolution time was considered as output variable. Evaluation of the models’ performance was achieved by criteria such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and coefficient of determination (R2). The results showed that EDT Bagged outperformed EDT Boosted. Sensitivity analysis was then conducted thanks to the Monte Carlo approach and it was found that three most important inputs for the problem were the diffusion coefficient, initial concentration, and bubble initial size. This study might help in quick prediction of bubble dissolution time to improve the production quality from industry.
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Song, Donghwan, Adrian Matias Chung Baek, Jageon Koo, Moise Busogi, and Namhun Kim. "Forecasting Warping Deformation Using Multivariate Thermal Time Series and K-Nearest Neighbors in Fused Deposition Modeling." Applied Sciences 10, no. 24 (2020): 8951. http://dx.doi.org/10.3390/app10248951.
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Over the past decades, additive manufacturing has rapidly advanced due to its advantages in enabling diverse material usage and complex design production. Nevertheless, the technology has limitations in terms of quality, as printed products are sometimes different from their desired designs or are inconsistent due to defects. Warping deformation, a defect involving layer shrinkage induced by the thermal residual stress generated during manufacturing processes, is a major factor in lowering the quality and raising the cost of printed products. This study utilized a variety of thermal time series data and the K-nearest neighbors (KNN) algorithm with dynamic time warping (DTW) to detect and predict the warping deformation in the printed parts using fused deposition modeling (FDM) printers. Multivariate thermal time series data extracted from thermocouples were trained using DTW-based KNN to classify warping deformation. The results showed that the proposed approach can predict warping deformation with an accuracy of over 80% by only using thermal time series data corresponding to 20% of the whole printing process. Additionally, the classification accuracy exhibited the promising potential of the proposed approach in warping prediction and in actual manufacturing processes, so the additional time and cost resulting from defective processes can be reduced.
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Yuan, Zhijun, Hui Wang, Xuebing Wei, Kui Yan, and Cheng Gao. "Multiobjective Optimization Method for Polymer Injection Molding Based on a Genetic Algorithm." Advances in Polymer Technology 2019 (July 24, 2019): 1–17. http://dx.doi.org/10.1155/2019/9012085.
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To solve the quality problem of polymer injection parts, a quality prediction and multiobjective optimization method is established. In this method, the parameters that have an important effect on the part quality are selected using an orthogonal testing method, and then a central composite design experiment is performed using these parameters. A mathematical model considering an objective and impact factors is developed using the response surface method. The optimal combination of the impact parameters is determined using a multiobjective genetic algorithm. The injection molding of a typical interior trim part of a car, i.e., the seat belt cover plate, is used as an example to demonstrate the method. The two most troublesome problems in this process—the sink marks and warpage—are multiobjectively analyzed using the established method, and the optimal combination of impact parameters that minimized the defects is determined. The errors of the sink marks and warpage between the experimental and theoretical values were 7.95% and 0.2%, respectively. The optimized parameters were tested in actual injection molding. The results show that the shrinkage and warpage of the parts are obviously improved by optimization using the proposed method, allowing the parts to satisfy the requirements of assembly and appearance.
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Hou, Hua, Guo Wei Zhang, Hong Kui Mao, and Jun Cheng. "A New Prediction Way to Shrinkage Cavity Formation for Ductile Iron Castings." Materials Science Forum 575-578 (April 2008): 127–34. http://dx.doi.org/10.4028/www.scientific.net/msf.575-578.127.
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Based on the solidification features of ductile iron and affecting factors for ductile iron shrinkage defect, the model of the ductile iron solidification is built and put forward a new defect predictive method EIECAM (Enclosed-Isolated area Expansion and Contraction Accumulation Method) model to predict defect. in DECAM, the liquid shrinkage, solidified shrinkage and graphitizing expansion during solidification are computed dynamically in the enclosed-isolated area , and the effect of graphite expansion on the wall movement is also accounted. Based on this method end cover of QT500 ductile iron casting is simulated and made the defect predictive, study its solidification process and the defect generation position, and make the experimental identification on the defect. It is resulted that the method can be able to predict the casting defect authentically.
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Xiong, Jia Ji, Fan Lin Meng, Qing Jin Liang, and Chong Cao. "Casting Process Design and Simulation of Large Cast Steel Bracket." Key Engineering Materials 815 (August 2019): 125–30. http://dx.doi.org/10.4028/www.scientific.net/kem.815.125.
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The casting process design of large cast steel supports is carried out, and the special integrated sand core and forged steel cast lugs are used to simplify the cavity manufacturing process. The ProCAST software was used to simulate the casting process of the stent, simulating the filling and solidification of the casting, and predicting the occurrence of defects such as shrinkage and shrinkage of the casting. According to the simulation results, the cause of the defects is analyzed, and the casting process of the stent is optimized. The simulation results show that the optimization scheme effectively reduces the casting defects and the surface of the stent is free from defects.
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Huang, Pei Hsing, Wei Jen Wu, and Chung Han Shieh. "Numerical Simulations of Low Pressure Die-Casting for A356 Aluminum Rims." Materials Science Forum 893 (March 2017): 276–80. http://dx.doi.org/10.4028/www.scientific.net/msf.893.276.
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This study conducted mold flow analyses on low pressure die-casting A356 aluminum rims to improve the shrinkage and porosity defects which usually occurs in die-castings so as to enhance the quality of die-casting wheels. We adopted different lift tube designs with cylindrical, taper opening and back taper opening structures while discussing the filling, exhaust, and solidification of molten flows and predicting the shrinkage and porosity formed based on the retained melt modulus. The study found that the configuration of lift tube as well as the optimization of process parameters such as the processing pressure and progressive time could effectively reduce the formations of shrinkage and porosity defects and improve the quality of die-castings.
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Rathod, Hardik, Jay K. Dhulia, and Nirav P. Maniar. "Prediction of Shrinkage Porosity Defect in Sand Casting Process of LM25." IOP Conference Series: Materials Science and Engineering 225 (August 2017): 012237. http://dx.doi.org/10.1088/1757-899x/225/1/012237.
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Baron, A. A., L. V. Palatkina, and S. V. Palatkin. "RESULTS OF SHRINKAGE ORIGIN DEFECTS DISTRIBUTION COMPUTER SIMULATION IN STANDARD CAST SAMPLE VOLUME OF GREY CAST IRON." IZVESTIA VOLGOGRAD STATE TECHNICAL UNIVERSITY, no. 6(253) (June 22, 2021): 71–78. http://dx.doi.org/10.35211/1990-5297-2021-6-253-71-78.
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For cast iron grades SCH 15 and SCH 20, using the computer modeling system of casting processes in the environment of the LVMFlow software package, the possibility of predicting defects of shrinkage origin when obtaining a standard cast sample that determines the grade and quality of the castings of responsible purpose poured simultaneously with it is considered
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Monde, Aniket D., Anirban Bhattacharya, and Prodyut R. Chakraborty. "Shrinkage induced flow and Free surface evolution during solidification of pure metal." E3S Web of Conferences 128 (2019): 06011. http://dx.doi.org/10.1051/e3sconf/201912806011.
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A numerical model is developed to study Shrinkage induced convection and free surface evolution caused by the density difference between the solid and liquid phases during the solidification of pure aluminium. For the analysis, a 2–D rectangular cavity field with aluminium melt undergoing solidification process is considered. Conservation of mass, momentum, and energy are formulated based on volume averaging technique and are solved using the SIMPLER algorithm. The free surface evolution is captured using the Volume of fluid (VOF) method. The proposed model focuses on predicting macro–scale shrinkage induced surface defects during the solidification process.
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Zheng, Kaikui, Youxi Lin, Weiping Chen, and Lei Liu. "Numerical simulation and optimization of casting process of copper alloy water-meter shell." Advances in Mechanical Engineering 12, no. 5 (2020): 168781402092345. http://dx.doi.org/10.1177/1687814020923450.
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The water-meter shell has a complex-structured thin-walled cavity, and it can cause casting defects such as shrinkage and misrun. On the basis of structural analysis of a water-meter shell, a three-dimensional model and a finite element model of the water-meter shell were constructed using the SOLIDWORKS and ProCAST software as a modeling tool and a casting numerical simulation tool, respectively. Three processes associated with the bottom gating system without a riser, a step gating system with a preliminary riser, and a step gating system with an optimum riser were successively numerically simulated. The mold-filling sequence, temperature distribution, liquid-phase distribution during solidification, and shrinkage distribution of these three processes are discussed here. The numerical simulation results indicated that optimization of the casting process and the rational assembling of the riser led to the shrinkage volumes at the inlet position, regulating sleeve, and sealing ring of the water-meter shell decreasing from 0.68 to 0 cm3, 1.39 to 0.22 cm3, and 1.32 to 0.23 cm3, respectively. A comparison between model predictions and experimental measurements indicated that the castings produced by the optimized process had good surface quality and beautiful appearance, without casting defects, demonstrating that numerical simulation can be used as an effective tool for improving casting quality.
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Zheng, Hong Liang, Yu Cheng Sun, Ning Zhang, Kai Zhang, and Xue Lei Tian. "Shrinkage Porosity Simulation of Spheroidal Graphite Iron Castings Based on Macro-Micro Models." Materials Science Forum 689 (June 2011): 190–97. http://dx.doi.org/10.4028/www.scientific.net/msf.689.190.
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Shrinkage porosity is often found in Spheroidal graphite iron (S. G. Iron) castings because of the mushy zone and special volume change during their solidification. Although the volume expansion is very important to the shrinkage porosity simulation of S.G. Iron castings, conventional methods for predicting the porosity defects do not consider it. A Series of macro-micro models such as macro heat transfer calculation and microstructure formation simulation are proposed to simulate the solidification of S. G. Iron castings. The nucleation and growth models are employed to calculate the accurate latent heat and volume change especially graphite expansion during the solidification. The pressure induced by graphite expansion is introduced as a parameter to predict the shrinkage porosity and a new shrinkage porosity criterion is developed. Cooling curves and solid fraction of each phase are compared with experimental castings. At the same time, the porosity area ratio of castings is compared with the results calculated by several porosity criterions. The results show that the new shrinkage porosity simulation criterion of S. G. Iron castings based on macro-micro models is accurate on shrinkage porosity shape, size and distribution simulation.
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Chang, Tong Chen, Hong Yu Zhu, and Hai Hong Wu. "The Prediction Method on Warpage of Injection Molded Parts." Advanced Materials Research 317-319 (August 2011): 211–14. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.211.
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Warpage is a common defect resulted from uneven thermal shrinkage during injection molding process. In this paper, the authors investigated finite element method to predict the warpage of injection moldings with thin shell theory. In order to improve calculating accuracy, discrete Kirchhoff element combined membrane element with rotational degrees of freedom was used to build finite element model. The results predicted with this model were compared with experimental data. The results showed that this finite element model was effective to increase the prediction accuracy of the warpage because of bring transfer matrices to improve the element accuracy.
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Liu, Cheng Cai, and Jing Shan He. "Numerical Prediction of Shrinkage Defect in Aluminum Alloy Electron Beam Weld Based on Niyama Criterion Program." Advanced Materials Research 1030-1032 (September 2014): 130–33. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.130.
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The root shrinkage cavity (SCRT) is one special kind of high-energy welding defect which can remarkably decreases mechanical strength and induces fatigue crack, so it is of great significance to numerically study its formation mechanism. In this paper, a set of Niyama criterion program based on temperature field was developed and adopted to numerically predict it in non-penetration 2219 aluminum alloy electron beam weld. The results indicate that the formation possibility of SCRT defect decreases with the increased welding speed. In addition, the error of simulated SCRT in the weld penetration direction is 8.76%, while the error in the weld width direction is 23.46%, which shows good agreement with experimental comparison samples. All the simulation and experimental results validate the correctness and feasibility of the developed Niyama criterion program.
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Skrzypczak, Tomasz, Ewa Węgrzyn-Skrzypczak, and Leszek Sowa. "Numerical model of solidification process of Fe-C alloy taking into account the phenomenon of shrinkage cavity formation." MATEC Web of Conferences 254 (2019): 02009. http://dx.doi.org/10.1051/matecconf/201925402009.
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Mathematical and numerical models of Fe-C alloy solidification process based on the finite element method (FEM) are presented in the paper. The phenomenon of the defect called "shrinkage cavity" is introduced to the model. It is very important aspect of presented work which makes possible the prediction of the location and shape of mentioned defect depending on the geometry of the casting and cooling condition of the process. An original computer program using Visual C++ environment has been written in order to simulate described process. The results of computer calculations of the three-dimensional solidification problem of the casting with riser are presented and discussed in details.
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Chang, Qing Ming, Yin Kai Yang, Jing Yuan, and Xia Chen. "Numerical Simulation of Mold Filling and Solidification Behavior in Permanente Casting Process." Applied Mechanics and Materials 313-314 (March 2013): 179–83. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.179.
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Melt flow and casting solidification are essential parts of the permanent mold casting process and affect significantly the quality of castings．For this reason, accurate prediction of mold filling pattern and temperature field in permanent mold castings plays on an important role in producing sound castings. In this paper, the model filling and solidification of a box casting produced from an aluminum alloy is studied. Different casting processes are employed, simulated and optimized to obtain sound castings. Simulation results reveal that with appropriate gating system, pouring rate, cooling line, a smooth mold filling, reduced shrinkages and other defects are available and desired sound castings can be produced.
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Li, Bing, Hui Ling Zhong, Hong Jie Li, Ling Chen, Lin Li, and Xiao Xi Li. "Prediction of the Properties of an Alumina Green Body Using an Artificial Neural Network by a New PSO-Gain Backpropagation Algorithm." Key Engineering Materials 368-372 (February 2008): 1642–44. http://dx.doi.org/10.4028/www.scientific.net/kem.368-372.1642.
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Artificial neural networks have been successfully used in classification, formulation optimization, defect diagnosis and performance prediction in ceramic industry. However, an artificial neural network based on the traditional backpropagation (BP) algorithm showed some disadvantages in mapping the nonlinear relationship between the composition and contents of the ceramic materials and their properties. In this paper, a new PSO-Grain (Particle Swarm Optimization Gain) BP algorithm was introduced, and an improved artificial neural network model was employed to predict the properties of an alumina green body. The training performance of the neural network using the PSO-Gain BP algorithm was analyzed and it was indicated the POS-Gain BP based neural network could reduce convergence to local minima and was more efficient than the traditional BP based network. The prediction accuracy of the properties such as linear shrinkage and bending strength using the PSO-Gain BP based neural network was higher than that of the BP based neural network.
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Perzyk, M., A. Kochański, P. Mazurek, and K. Karczewski. "Selected Principles of Feeding Systems Design: Simulation vs Industrial Experience." Archives of Foundry Engineering 14, no. 4 (2014): 77–82. http://dx.doi.org/10.2478/afe-2014-0090.
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Abstract Simulation software dedicated for design of casting processes is usually tested and calibrated by comparisons of shrinkage defects distribution predicted by the modelling with that observed in real castings produced in a given foundry. However, a large amount of expertise obtained from different foundries, including especially made experiments, is available from literature, in the form of recommendations for design of the rigging systems. This kind of information can be also used for assessment of the simulation predictions. In the present work two parameters used in the design of feeding systems are considered: feeding ranges in horizontal and vertical plates as well as efficiency (yield) of feeders of various shapes. The simulation tests were conducted using especially designed steel and aluminium castings with risers and a commercial FDM based software. It was found that the simulations cannot predict appearance of shrinkage porosity in horizontal and vertical plates of even cross-sections which would mean, that the feeding ranges are practically unlimited. The yield of all types of feeders obtained from the simulations appeared to be much higher than that reported in the literature. It can be concluded that the feeding flow modelling included in the tested software does not reflect phenomena responsible for the feeding processes in real castings properly. Further tests, with different types of software and more fundamental studies on the feeding process modelling would be desirable.
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Suprapto, Wahyono, Bambang Suharno, Johny Wahyuadi Soedarsono, and Dedi Priadi. "Analytical and Experimental Models of Porosity Formation of Duralumin Cast in Vacuum Casting System." Advanced Materials Research 277 (July 2011): 76–83. http://dx.doi.org/10.4028/www.scientific.net/amr.277.76.
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Porosity in cast metals often leads to cracking of components due to stress concentration and leakage, and as the result, the castings need be repaired or rejected. Disharmony in casting process was resulting in porosity. Prediction of porosity in the casting is necessary as a step to avoid the waste products and reduce costs. But to ensure whether these predictions are accurate and precise, it is still necessary to validate the test trials and testing. This paper aims to provide early information when, where, and how large a defect occurs in particular foundry casting porosity on duralumin. The analytical study of porosity formation based analytic equilibrium wt% of element, the behavior of the thermodynamic, hydrodynamic, and rules of metallurgical on vacuum casting of duralumin. Experiments as a validation study are conducted by duralumin remelting on stainless-steel bowl in a vacuum casting furnace. Analytical simulation and experiments of the casting that has been vacuumed by melting 10 cmHg pressures higher than the pressure solidification, and duralumin melt is poured automatically into permanent mold carbon steel. In the study cast duralumin created five different thicknesses. Both these studies assume the addition of copper (2.5%, 3.0%, 3.5 %, 4.0%, and 4.5% Cu) and vacuum pressure (76, 50, 40, cmHg), as independent variables, while dependent variable in the studies is porosity characteristics, which includes morphology, number and dimensions of the porosity. Optical emission spectrometry test, Reynold's and Niyama numbers, Sievert's law, Archimedes' principle (Pycnometry and Straube-Pfeiffer tests), and Eichenauer equation are instruments which are used to determine the characterization of duralumin casting porosity. Duralumin ingots remelting process was performed by the control pressure (p1) and temperature (T1). Vacuuming process performed after the smelting room temperature reaches 600 °C. Once melted, it followed by duralumin into a permanent mold (p2, T2). As a control parameter is the height of pouring (7 cm), pour temperature and mold temperature respectively at 750 °C and 300 °C. The porosity characteristics studies of two models produce two types of porosity (gas and shrinkage), the quantity dimension and porosity, and distribution of porosity in the cast duralumin.
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Park, Heyjun, Ahmed Metwally, Dalia Perelman, et al. "Effects of Habitual Sleep on Glucose Regulation in Individuals at Risk for Type 2 Diabetes." Current Developments in Nutrition 4, Supplement_2 (2020): 60. http://dx.doi.org/10.1093/cdn/nzaa040_060.
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Abstract Objectives This study sought to advance understanding of the impact of habitual sleep patterns on glucose regulation in individuals at risk for type 2 diabetes (T2D). Methods To achieve this aim, we examined associations between a comprehensive panel of sleep parameters and glucose metabolism marker among people with prediabetes (n = 19, age = 60.0y, male = 47.4%) using wearable technology. Briefly, participants underwent fasting plasma glucose (FPG) test and wore Fitbit Ionic band to assess their habitual sleep patterns. Sleep parameters were obtained for a median of 50 days per each participant such as total sleep duration, duration of each sleep stage per night, bed-time, wake-time, etc. To examine associations of sleep parameters with blood glucose levels, a least absolute shrinkage and selection operator (LASSO) regression was used to identify sleep parameters that predict FPG levels with the enhanced prediction accuracy. Mixed effects regression was also performed. Results In LASSO regression of FPG levels, wake-time (β = −0.013) and percentage of the rapid eye movement (REM) sleep duration out of the total sleep duration (REM%; β = −0.231) were found to inversely predict FPG levels among participants. Mixed effects regression model also showed that REM% is inversely associated with FPG levels (R2 = 0.61; β = −1.57, P = 0.058) after adjusting other covariates. In sum, people with prediabetes who have earlier wake-time and shorter REM proportion have shown higher FPG levels. Conclusions Overall, these findings suggest that habitual sleep patterns may influence physiologic defect underlying dysglycemia and progression to T2D in individuals with prediabetes. Funding Sources Precision Health and Integrated Diagnostic (PHIND) Center at Stanford University.
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Ali, Burhan, and Mike Marshall. "Automated Optical Inspection (AOI) for FOPLP with Simultaneous Die Placement Metrology." International Symposium on Microelectronics 2019, no. 1 (2019): 000203–10. http://dx.doi.org/10.4071/2380-4505-2019.1.000203.
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Abstract As the final step of IC fabrication, packaging is the process to encapsulate the chip and provide the interconnections for the I/O of the final form factor. The demand for increasingly higher I/O density, shrinking device size and lower cost that drive wafer processing also apply to the packaging process. Various technologies have been developed in order to achieve these goals with most of them being wafer-level packaging (WLP). Unlike traditional packaging process, most I/O interconnections are done at the wafer-level with redistribution layers (RDLs). RDLs are the layer where copper lines and vias form the electrical connections. Depending on the applications' market such as mobile, memory or the Internet of Things (IoT), fan-out wafer level packaging (FOWLP) provides the most promising method to support the I/O density requirements and fine RDL line/space. Moreover, fan-out panel level packaging (FOPLP) was also developed in order to capitalize on economies of scale and optimize substrate utilization. In this technology, a rectangular substrate is used in the process instead of a round-shape substrate like a wafer. Processes and equipment have long been developed for the wafer substrate market, but the previous developments cannot be directly applied to panel substrates. For instance, in the wafer line, spin on processes are very prevalent but these are not at all practical for a panel line. Some capital equipment manufacturers have been reluctant to embrace panel-level manufacturing due to the uncertainty as to whether it will prevail. Struggles with yield have been very common; some of which are due to die placement and others due to the lack of process control capabilities. With the explosion and adoption of FOWLP to enhance package shrinkage and performance the panel market becomes more and more viable. The companies that have embraced panel level manufacturing from the beginning have a distinct advantage due to their intimate knowledge and experience with the substrates as well as the relationship developed with capital equipment suppliers to develop the necessary technology in order to process the panels. However, there is still a great need to ensure the product mix deployed in panel form can have an acceptable yield; automated optical inspection and die placement metrology bridge that gap. Automated optical inspection allows for defect detection with traditional bright field (BF) or dark field (DF) illumination and also a new novel illumination technique that enables the detection of organic particles and/or residues that are often used in panel-level packaging processes. A system capable of macro defect detection with sub-micron capabilities allows for multi-purpose panel inspections. The system is also equipped with metrology capabilities for critical dimension and die placement measurements which meet the process node dimensional requirements. These features allow for process control of pick and place, overlay as well as feed-forward capabilities for die placement corrections. In a FOWLP/FOPLP process, chip first and chip last can be concluded among all available methods in the market. Die placement either start from the initial phase of the process or in the final phase of the process. In the chip first scenario, the chips are placed on a carrier by a pick-and-place system and then followed by an encapsulating molding process to reconstitute a substrate (reconstituted wafer or reconstituted panel). At this point a semi-additive process (SAP) is typically followed which includes a photo resist layer being coated, exposed and developed following copper (Cu) plating in order to form the redistribution layer. In this workflow, the die position are dominated by the accuracy of the pick-and-place tool and coefficient of thermal expansion (CTE) mismatch of the molding material and carrier. The trade-off between throughputs, placement accuracy and a feedback mechanism is the main impact from the pick-and-place tool in this process step. This affects both the chip first and chip last scenarios. The thermal expansion of the molding process not only adds additional die shift but also causes warpage of the reconstituted substrate that becomes an issue for automated handling systems and local process variation. Therefore, to know the actual die position and orientation after the die placement and molding process is crucial for matching with the following redistribution layers development. In one scenario it is possible to utilize the lithography system to perform die position metrology, however, this is time consuming and impacts the cost of ownership and overall throughput for the lithography process. A solution to this problem is provided by implementation of an optical metrology system. Since this information needs to be passed to the lithography tool in a usable manner for variable exposure positioning, the alignment of the stage coordinate system between the die metrology tool and lithography tool is a key point to ensure the correctness of the feed forward loop. For RDL development overlay between die and RDL via directly impact yield and are just as critical to the process as defect inspection and critical dimension measurements. Based on the corrections for each die, a yield prediction can be made and provides different strategies for the lithography tool's exposure field in order to balance throughput and exposure yield rate. In this paper, we demonstrate a solution using an automatic optical inspection (AOI) system to perform the die metrology for chip placement and RDL development in FOPLP and FOWLP. This includes die shift, die rotation, RDL inspection as well as the overlap between a reconstituted substrate and RDLs. This solution provides comprehensive coverage for packaging process control and significantly impacts yield optimization and throughput enhancement. With a multifunctional AOI system, it also reduces the cost of ownership for packaging processes.
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Liu, Xin, Xiying Fan, Yonghuan Guo, Yanli Cao, and Chunxiao Li. "Multi-objective optimization of the GFRP injection molding process parameters by using GA-ELM, MOFA and GRA-TOPSIS." Transactions of the Canadian Society for Mechanical Engineering, June 14, 2021. http://dx.doi.org/10.1139/tcsme-2021-0053.
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Due to the influence of injection molding process, warpage and volume shrinkage are two common quality defects for products manufactured by the glass fiber-reinforced plastic (GFRP) injection molding. In order to minimize the two defects, the extreme learning machine optimized by genetic algorithm (GA-ELM), multi-objective firefly algorithm (MOFA) and a multi-objective decision-making method called GRA-TOPSIS are implemented in this study. All experiments based on Latin hypercubic sampling (LHS) are conducted by Moldflow software to obtain results of warpage and volume shrinkage. The prediction accuracy of defect prediction models based on the extreme learning machine (ELM) and GA-ELM algorithm is compared. The results show that GA-ELM models can better predict defect values. Finally, MOFA is utilized to find the Pareto optimal front, and the GRA-TOPSIS method is used to find the optimum solution from the Pareto optimal front. According to the results of the simulation verification, the warpage and volume shrinkage are effectively reduced by 12.25% and 6.11% compared with those before optimization, respectively, which indicates the effectiveness and reliability of the optimization method.
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"Predictive Modelling for Quality Prediction and Assurance of Extrusion Blow Molding." International Journal of Innovative Technology and Exploring Engineering 8, no. 11 (2019): 1364–68. http://dx.doi.org/10.35940/ijitee.j9676.0981119.
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Extrusion Blow Molding process plays an important role in manufacturing of hollow products with wide variety of materials like polyethylene (PE), polypropylene (PP), polyvinylchloride (PVC). Extrusion blow molded products are rejected due to the occurrence of defects such as die lines, blowouts, shrinkage, over weight of part. The complex relationships that exist between the process variables, and causes of defects are investigated for 1 litre container made of highdensity polyethylene (HDPE) using data mining techniques in order to reduce scrap. In this paper Data Mining approach is implemented by applying Decision Tree, k-Nearest Neighbors, Rule Induction and Vote techniques in RapidMiner for quality assurance and prediction of the quality of the extrusion blow molded product
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Kumar, Rajesh. "Micromechanics Approach for the Overall Elastic Properties of Ceramic Matrix Composites Incorporating Defect Structures." Journal of Engineering for Gas Turbines and Power, January 4, 2021. http://dx.doi.org/10.1115/1.4049485.
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Abstract Initial mechanical behavior of Ceramic Matrix Composites (CMCs) is linear until the proportional limit. This initial behavior is characterized by linear elastic properties, which are anisotropic due to the orientation and arrangement of fibers in the matrix. The linear elastic properties are needed during analysis and design of CMC components. CMCs are made with ceramic unidirectional or woven fiber preforms embedded in a ceramic matrix formed via various processing routes. The matrix processing of interest in this work is the Polymer Impregnation and Pyrolysis (PIP) process. As this process involves pyrolysis to convert a pre-ceramic polymer into ceramic, considerable volume shrinkage occurs in the material. This leads to significant defects in the form of porosity of various size, shape, and volume fraction. These defect structures can have a significant impact on the elastic and damage response of the material. In this paper, we develop a new micromechanics modeling framework to study the effects of processing-induced defects on linear elastic response of a PIP-derived CMC. A combination of analytical and computational micromechanics approaches is used to derive the overall elastic tensor of the CMC as a function of the underlying constituents and/or defect structures. It is shown that the volume fraction and aspect ratio of porosity at various length-scales plays an important role in accurate prediction of the elastic tensor. Specifically, it is shown that the through-thickness elastic tensor components cannot be predicted accurately using the micromechanics models unless the effects of defects are considered.
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Gu, Yan, Qianqian Li, Rui Lin, et al. "Prognostic Model to Predict Postoperative Adverse Events in Pediatric Patients With Aortic Coarctation." Frontiers in Cardiovascular Medicine 8 (May 21, 2021). http://dx.doi.org/10.3389/fcvm.2021.672627.
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Background: Postoperative adverse events remain excessively high in surgical patients with coarctation of aorta (CoA). Currently, there is no generally accepted strategy to predict these patients' individual outcomes.Objective: This study aimed to develop a risk model for the prediction of postoperative risk in pediatric patients with CoA.Methods: In total, 514 patients with CoA at two centers were enrolled. Using daily clinical practice data, we developed a model to predict 30-day or in-hospital adverse events after the operation. The least absolute shrinkage and selection operator approach was applied to select predictor variables and logistic regression was used to develop the model. Model performance was estimated using the receiver-operating characteristic curve, the Hosmer–Lemeshow test and the calibration plot. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) compared with existing risk strategies were assessed.Results: Postoperative adverse events occurred in 195 (37.9%) patients in the overall population. Nine predictive variables were identified, including incision of left thoracotomy, preoperative ventilation, concomitant ventricular septal defect, preoperative cardiac dysfunction, severe pulmonary hypertension, height, weight-for-age z-score, left ventricular ejection fraction and left ventricular posterior wall thickness. A multivariable logistic model [area under the curve = 0.8195 (95% CI: 0.7514–0.8876)] with adequate calibration was developed. Model performance was significantly improved compared with the existing Aristotle Basic Complexity (ABC) score (NRI = 47.3%, IDI = 11.5%) and the Risk Adjustment for Congenital Heart Surgery (RACHS-1) (NRI = 75.0%, IDI = 14.9%) in the validation set.Conclusion: Using daily clinical variables, we generated and validated an easy-to-apply postoperative risk model for patients with CoA. This model exhibited a remarkable improvement over the ABC score and the RACHS-1 method.
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Lanzotti, Nicholas, Nauman Jahangir, Kyle Gollon, et al. "Abstract TP148: A Simplified Novel Stroke Prognostication Scale for Patients Receiving Mechanical Thrombectomy." Stroke 51, Suppl_1 (2020). http://dx.doi.org/10.1161/str.51.suppl_1.tp148.
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Introduction: Various stroke scales exist to predict outcomes after acute ischemic stroke (AIS). We propose a simplified novel scale based on individual variables previously used in other scales to predict outcome after AIS. Methods: 166 consecutive patients presented with AIS between 2015 and 2018 who received mechanical thrombectomy. We collected the following variables: age, sex, NIHSS, HTN, diabetes, atrial fibrillation, stroke subtype, CHF, cancer, renal dialysis, preadmission disability, systolic blood pressure, 3-item stroke severity scale, last known well (LKW) to tPA administration, LKW to ED arrival, use of thrombolytic therapy, presence of visual field defect, level of consciousness, and visible hypodensity on CT (none, <1/3 MCA territory, or ≥1/3 MCA territory). All other missing data was addressed using multiple imputation. Logistic regression analysis was performed to find their association with poor prognosis (mRS 2 or greater). Because we desired a relatively simple model for clinical use, we fit models using the least absolute shrinkage and selection operator (LASSO) to both select important predictors and prevent overfitting. Results: The LASSO selected sex, age, 3-item stroke severity, and visible hypodensity on a CT to be in the predictive model. Females, older individuals, more severe strokes, and patients with a visible hypodensity covering ≥1/3 of the MCA territory were found to be associated with increased risk of a 90 day MRS of 2 or greater. This model had reasonable discriminative ability in line with many of the other stroke scores studied (internally validated AUC = 0.69, 95% CI [0.55, 0.72]). A risk threshold of 0.78 maximized the sum of specificity (0.90) and sensitivity (0.52). This model was further discretized into an easy to use risk score (Table 1). Conclusion: We propose a simplified novel stroke prognostication scale with high specificity in predicting poor prognosis in stroke patients receiving mechanical thrombectomy.