Tag Archives: Efficiency

Fig. 6. Analyze of the cavities filling with Flow3D software

Reducing Porosity Defects in Aluminum HPDC: Key Causes and Practical Solutions

This technical summary is based on the academic paper “ANALYZE OF THE POSSIBLE CAUSES OF POROSITY TYPE DEFECTS IN ALUMINIUM HIGH PRESSURE DIECAST PARTS” by Ference Peti and Lucian Grama, published by SC CIE Matronca SA, Romania. It was analyzed and summarized for HPDC experts by CASTMAN experts with the help of LLM AI such

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Table 3-1 Solid solubility of elements in aluminum [2]

Beyond Cooling Rates: A Deep Dive into Chemical Composition and Its Impact on Secondary Dendrite Arm Spacing (SDAS) Control

This technical summary is based on the academic dissertation “Impact of casting parameters and chemical composition on the solidification behaviour of Al-Si-Cu hypoeutectic alloy” by Dipl.-Ing. Jelena Pavlovic-Krstic (2010). It has been analyzed and summarized for technical experts by CASTMAN with the assistance of AI. Keywords Executive Summary A 30-second overview for busy professionals. The

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Figure 6. Aluminum spaceframe (Source: Paefgen 1994)

Unlocking Lightweight Automotive Innovation: The Potential of Wrought Magnesium

This technical summary is based on the academic paper “Analysis of the Potential for New Automotive Uses of Wrought Magnesium” published by L. Gaines, R. Cuenca, F. Stodolsky, and S. Wu at the Center for Transportation Research, Argonne National Laboratory (1996). It was analyzed and summarized for automotive engineers by CASTMAN experts with the help

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Figure 1.1: Gravity die mold [3].

Boosting Casting Quality: Why Vertical Gravity Die Casting Outperforms Horizontal Arrangements

This technical summary is based on the academic paper “Analysis of Mechanical Properties and Microstructure of Multiple Die Cavity Products Produced in Vertical and Horizontal Arrangement by Gravity Die Casting” by Ong Chin Yee, published by UTHM, Batu Pahat (2012). It was analyzed and summarized for HPDC experts by CASTMAN experts with the help of

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Fig. 1. Schematic of Continuous Rod Casting Machine

4 Key Levers for Casting Process Optimization: Lessons for HPDC from a Copper Alloy Study

This technical summary is based on the academic paper “Effect of water flow rate, casting speed, alloying elements and pull distance on tensile strength, elongation percentage and microstructure of continuous cast copper alloys” published by Bagherian, E-R., Fan, Y., Cooper, M., Frame, B., & Abdolvand, A. in Metallurgical Research and Technology (2016). It was analyzed

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Fig. 1 Example of (a) porosity in the end ring of an Al die cast rotor; and measurement of rotor weight (b) before and (c) after die-casting to estimate the porosity level

Beyond Visuals: A New Method for Aluminum Die-Cast Rotor Testing to Detect Hidden Porosity

This technical summary is based on the academic paper “Quality Assurance Testing for Screening Defective Aluminum Die-cast Rotors of Squirrel Cage Induction Machines” published by Jeong, M., Yun, J., Park, Y., Lee, S.B. and Gyftakis, K. in IEEE (2017). It was analyzed and summarized for HPDC experts by CASTMAN with the support of AI. Keywords

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Fig.1 – (a) The rotating furnace-sonication system; (b) Impeller with nitrogen degassing, (c) Shock tower.

How Rotary Degassing-Ultrasonic Methods Can Slash Porosity in HPDC Products

This technical summary is based on the academic paper “An investigation on effect of rotary degassing-ultrasonic method on high pressure die casting products” published in La Metallurgia Italiana (2022). It was analyzed and summarized for HPDC experts by CASTMAN experts with the help of LLM AI such as Gemini, ChatGPT, and Grok. Keywords Executive Summary

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Figure 3: Residual Plots for Yield Strength of a FSW Joint in ANOVA

Maximizing Friction Stir Welding (FSW) Strength: A Statistical Optimization for Joining Dissimilar Aluminum Alloys

This technical brief is based on the academic paper “A STATISTICAL ANALYSIS OF JOINT STRENGTH OF DISSIMILAR ALUMINIUM ALLOYS FORMED BY FRICTION STIR WELDING USING TAGUCHI DESIGN APPROACH, ANOVA FOR THE OPTIMIZATION OF PROCESS PARAMETERS” by Mohammed Yunus & Mohammad S. Alsoufi, published in [IMPACT: International Journal of Research in Engineering & Technology (IMPACT: IJRET)]

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Table 1- Coefficients of quadratic (i.e., +,- ) and linear (i.e., ./ - ) effects as well as the p-value of the quadratic effects of explanatory variables with negative +,- values in the logistic, probit, and cloglog models, where the best choice (i.e., maximizer) is calculated given by −./ -/0+,-.

TOWARDS AN AI-Driven Smart Manufacturing of Diecastings

From Root Cause Analysis to Predictive Quality: A Dual AI Approach to Slash Die Casting Scrap Rates This technical brief is based on the academic paper “TOWARDS AN AI-Driven Smart Manufacturing of Diecastings” by F. Liu, S. Wang, X. Liu, T. Zhang, B. Yang, Q. Han, D. Yang, and Corey Vian, published in NADCA Transactions

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