Die-Casting Parameter Sizing for AZ91D in Notebook Computer Base Shell

2006 ◽  
Vol 21 (5) ◽  
pp. 489-494 ◽  
Author(s):  
Yung-Kuang Yang ◽  
Chorng-Jyh Tzeng
Keyword(s):  
Die Casting ◽  
Notebook Computer ◽  
Casting Parameter

scholarly journals Data-Analytics-Based Factory Operation Strategies For Die-Casting Quality Enhancement

2021 ◽  
Author(s):  
Jun Kim ◽  
Ju Yeon Lee
Keyword(s):  
Data Analytics ◽  
Die Casting ◽  
Parameter Tuning ◽  
Prediction Algorithm ◽  
Quality Enhancement ◽  
Input And Output ◽  
Diagnosis Algorithm ◽  
Casting Parameter ◽  
Worker Experience ◽  
Factory Operation

Abstract This paper proposes data-analytics-based factory operation strategies for the quality enhancement of die-casting. We first define the four main problems of die casting that result in lower quality: [P1] gaps between the input and output casting parameter values, [P2] occurrence of preheat shots, [P3] lateness of defect distinction, and [P4] worker-experience-based casting parameter tuning. To address these four problems, we derived seven tasks that should be conducted during factory operation: [T1] implementation of exploratory data analysis (EDA) for investigating the trends and correlations between data, [T2] deduction of the optimal casting parameter output values for the production of fair-quality products, [T3] deduction of the upper and lower control limits for casting parameter input–output gap management, [T4] development of a preheat shot diagnosis algorithm, [T5] development of a defect prediction algorithm, [T6] development of a defect cause diagnosis algorithm, and [T7] development of a casting parameter tuning algorithm. The details of the proposed data-analytics-based factory operation strategies with regard to the casting parameter input and output data, data preprocessing, data analytics method used, and implementation are presented and discussed. Finally, a case study of a die-casting factory in South Korea that has adopted the proposed strategies is introduced.

scholarly journals Establishment of an IoT-based smart factory and data analysis model for the quality management of SMEs die-casting companies in Korea

2019 ◽  
Vol 15 (10) ◽  
pp. 155014771987937
Author(s):  
Sangwoo Park ◽  
Kim Changgyun ◽  
Sekyoung Youm
Keyword(s):  
Data Analysis ◽  
Internet Of Things ◽  
Die Casting ◽  
Smart Factory ◽  
Analysis Model ◽  
Data Mining Technique ◽  
Casting Parameter ◽  
The Government ◽  
Casting Industry ◽  
Smart Factories

In this research, an Internet of things–based smart factory was established for a die-casting company that produces automobile parts, and the effect of casting parameters on quality was analyzed using data collected from the system. Most of the die-casting industry in Korea consists of small- and medium-sized enterprises with inferior finances and skeptical views about the establishment of a smart factory. In response, the Korean government is providing various types of support to spread the implementation of smart factories for small- and medium-sized enterprises. Although small- and medium-sized enterprises have become more active in establishing smart factories according to the government policies, the effect of smart factories requires real-time monitoring. A monitoring system has been built but the data collected are not being utilized properly. Therefore, it is necessary to establish a system suitable for the die-casting environment and data analysis purposes and to utilize it to enable the analysis of data. To this end, we established to smart factory that provides data based on the Internet of things. Among the data collected, casting parameter data were analyzed through a data mining technique to establish a relationship between casting parameters and the quality of production. It is expected that a method of systematic implementation will be provided to die-casting companies that want to build smart factories in the future and that a plan for managing casting parameter by-product will be established. In addition, algorithms that can solve the problem of multi-collinearity among the casting parameters and aid in the development of new products are needed to detect optimum casting parameters.

APEX 39

Alloy Digest ◽  
1959 ◽  
Vol 8 (1) ◽  
Keyword(s):  
Fracture Toughness ◽  
Corrosion Resistance ◽  
High Temperature ◽  
Physical Properties ◽  
Tensile Properties ◽  
Die Casting ◽  
Casting Alloy ◽  
Alloy Ingot ◽  
Temperature Performance

Abstract APEX 39 is an aluminum die casting alloy ingot containing copper and silicon. It has excellent castability and improved machinability. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties as well as fracture toughness. It also includes information on high temperature performance and corrosion resistance as well as casting and machining. Filing Code: Al-75. Producer or source: Apex Smelting Company.

MAGNESIUM AZ91B

Alloy Digest ◽  
1958 ◽  
Vol 7 (1) ◽  
Keyword(s):  
Fracture Toughness ◽  
Corrosion Resistance ◽  
Shear Strength ◽  
Physical Properties ◽  
Surface Treatment ◽  
Tensile Properties ◽  
Die Casting ◽  
Casting Alloy ◽  
Casting Industry ◽  
Magnesium Die Casting

Abstract MAGNESIUM AZ91B is a standard magnesium die casting alloy patterned for the needs of the commercial casting industry. This datasheet provides information on composition, physical properties, hardness, elasticity, tensile properties, and compressive and shear strength as well as fracture toughness and fatigue. It also includes information on corrosion resistance as well as casting, machining, and surface treatment. Filing Code: Mg-36. Producer or source: Apex Smelting Company.

COPPER ALLOY No. 878

Alloy Digest ◽  
1979 ◽  
Vol 28 (12) ◽  
Keyword(s):  
Fracture Toughness ◽  
Wear Resistance ◽  
Corrosion Resistance ◽  
Copper Alloy ◽  
Die Casting ◽  
Heat Treating ◽  
Casting Alloys ◽  
Copper Zinc ◽  
Die Castings ◽  
Very High

Abstract Copper Alloy No. 878 is a copper-zinc-silicon alloy for die castings. Among the brass die-casting alloys, it has the highest strength, hardness and wear resistance; however, it is the most difficult to machine. It is used where very high requirements must be met for strength and wear resistance. Its many applications include tools, pump impellers, gears and marine hardware. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties as well as fracture toughness. It also includes information on corrosion resistance as well as casting, heat treating, machining, and joining. Filing Code: Cu-386. Producer or source: Copper alloy producers.

BIRMAL P.83

Alloy Digest ◽  
1960 ◽  
Vol 9 (12) ◽  
Keyword(s):  
Corrosion Resistance ◽  
Shear Strength ◽  
High Temperature ◽  
Physical Properties ◽  
Tensile Properties ◽  
Die Casting ◽  
Heat Treating ◽  
Casting Alloy ◽  
Temperature Performance ◽  
Aluminum Castings

Abstract Birmal P.83 is an aluminum-copper-silicon die casting alloy, having excellent castability and improved machinability. This datasheet provides information on composition, physical properties, hardness, elasticity, tensile properties, and shear strength as well as fatigue. It also includes information on high temperature performance and corrosion resistance as well as casting, heat treating, and machining. Filing Code: Al-98. Producer or source: Birmingham Aluminum Castings Company Ltd.

AUR-O-MET 145

Alloy Digest ◽  
1965 ◽  
Vol 14 (8) ◽  
Keyword(s):  
Physical Properties ◽  
Tensile Properties ◽  
Die Casting ◽  
Heat Treating ◽  
Casting Alloy ◽  
Silicon Bronze

Abstract AUR-O-MET 145 is a silicon bronze die casting alloy having good castability and good weldability. It is suitable for gears, valve parts and marine fittings. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties. It also includes information on heat treating and machining. Filing Code: Cu-154. Producer or source: Aurora Metal Company.

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