Consolidation Behavior Of Metal Power In Additive Manufacturing

Additive manufacturing (AM) is a relatively new and emerging manufacturing technology that is able to revolutionize the manufacturing industry. This is due to its high flexibility in processing different types of material under various conditions. However, capability of a product to have desirable quality comparable to traditional processing techniques is still not achievable. Consolidation behavior and influences of processing parameters are important in determining the part quality. Therefore, in this research, consolidation behavior of metal powder was examined by monitoring the real time consolidation process and surface temperature. A high-speed camera was utilized with telescopic lenses in order to monitor interaction of laser and material within the fusion zone (FZ). In order to investigate the I consolidation temperature, a two-color pyrometer was used. The influences of processing parameters were examined. It was found the temperature and consolidation behavior were affected by the processing parameters. The line consolidation characteristics were analyzed according to the line consolidation width, FZ, melt pool and splattering behavior. Based on the study, the line consolidation can be classified into five different consolidation types. These types are continuous, discontinuous, ball shaped, weak and very little consolidation. The consolidation mechanisms that occurred during line and area consolidation were also reported. Other than that, the properties of the consolidated material were studied, and its potential for the development of a permeable structure was investigated. It was found that the properties of the structures developed via AM relatively good and feasible to be used for the manufacturing of injection mold

Water pressure loss analysis of mobile machine for fire figthing purpose

Fire fighting is risky profession. They arc not only extinguishing fires in tall buildings but also must drag heavy hoses, climb high ladders and carry people from buildings and other situations. There arc many fire fighters lost their lives in the line of duty each year throughout the world. The stntistics of the fire fighter fatalities are still maintain at high level every year and it may continue to increase if there is no improvement in fire fighting techniques and technology. The paper describes the water pressure loss analysis of mobile fire fighting machine prototype

Water Pressure Loss Analysis of Mobile Machine for Fire Fighting Purpose

Fire fighting is risky profession. They are not only extinguishing fires in tall buildings but also must drag heavy hoses, climb high ladders and carry people from buildings and other situations. There are many fire fighters lost their lives in the line of duty each year throughout the world. The statistics of the fire fighter fatalities are still maintain at high level every year and it may continue to increase if there is no improvement in fire fighting techniques and technology. The paper describes the water pressure loss analysis of mobile fire fighting machine prototype

Design Optimization of Components for Additive Manufacturing-Repair: An Exploration of Artificial Neural Network Requirements and Application

The integration of artificial intelligence (AI) in additive manufacturing (AM) technology is currently apromising and leading area of research for component repair and restoration. The Issues of high cost and timeconsumption for AM repair have been a subject of discussion among researchers in this field of study. Moreover,the potential challenges in dealing with complex components for repair and restoration in the (AM) domain requirethe establishment of a critical technical platform based on hybrid (AI). At this point, the proposed optimizationmethod must cover all important parameters for the complex configuration of structural components underrestoration. For the purpose of this study, a design optimization framework was developed using a MATLAB-SIMULINK mathematical model for AM solution purposes by improving the functionality and integration ofmonitoring. This improvement is based on facilitating the real-time identification of failures with accuracy andgiving a clear monitoring vision according to the intended targets like geometric distortions, residual stressesevaluation, and defect characterization. The improvement involves overcoming a number of challenges such as thepre-fabrication stage by expanding the data repository besides offering a theoretical set of algorithmic with someoptions that improve the current procedure. Also, this study will conclude and suggest a further framework andnew knowledge for restoration and product life cycle extension. This developed ANN can be used at the real paceof modeling the MATLAB-Simulink system and merged with another suitable algorithm to form a hybrid ANN.This model development using a neural network has attained a good manipulation of AM. The predicted data fromANN model that was determined and achieved in this study can be used to facilitate and enhance any further studyas base knowledge in merging the ANN with another AI to form a hybrid algorithm. &nbsp

Stab Resistant Analysis of Body Armour Design Features Manufactured via Fused Deposition Modelling Process

Five designs of imbricate scale armour features for stab-resistant application were printed via fused deposition modelling process. Stab test on these designs against the HOSDB KR1-E1 stab-resistant body armour standard with impact energy of 24 Joules was conducted. The stab test was conducted on a number of samples measured thicknesses ranging from 4.0 to 10.0 mm by using Instron CEAST 9340 Drop Impact Tower to determine a minimum thickness that resulted in a knife penetration through the underside of sample which does not exceed the maximum penetration permissibility of 7.0 mm. Materials used for the samples were ABS-M30 and PC-ABS. Finally, one of the designs which offered the highest knife penetration resistance was selected. The results show that PC-ABS samples provide less shattering and lower overall knife penetration depth in comparison with ABS-M30. PC-ABS stab test demonstrated a minimum thickness of 8.0 mm, which was the most adequate to be used in the development of FDM manufactured body armour design features. Lastly, the design feature of D5 has shown to exhibit the highest resistance to the knife penetration due to the penetration depth of 3.02 mm, which was the lowest compared to other design features

Design Optimization of Components for Additive Manufacturing-Repair: An Exploration of Artificial Neural Network Requirements and Application

The integration of artificial intelligence (AI) in additive manufacturing (AM) technology is currently apromising and leading area of research for component repair and restoration. The Issues of high cost and timeconsumption for AM repair have been a subject of discussion among researchers in this field of study. Moreover,the potential challenges in dealing with complex components for repair and restoration in the (AM) domain requirethe establishment of a critical technical platform based on hybrid (AI). At this point, the proposed optimizationmethod must cover all important parameters for the complex configuration of structural components underrestoration. For the purpose of this study, a design optimization framework was developed using a MATLAB-SIMULINK mathematical model for AM solution purposes by improving the functionality and integration ofmonitoring. This improvement is based on facilitating the real-time identification of failures with accuracy andgiving a clear monitoring vision according to the intended targets like geometric distortions, residual stressesevaluation, and defect characterization. The improvement involves overcoming a number of challenges such as thepre-fabrication stage by expanding the data repository besides offering a theoretical set of algorithmic with someoptions that improve the current procedure. Also, this study will conclude and suggest a further framework andnew knowledge for restoration and product life cycle extension. This developed ANN can be used at the real paceof modeling the MATLAB-Simulink system and merged with another suitable algorithm to form a hybrid ANN.This model development using a neural network has attained a good manipulation of AM. The predicted data fromANN model that was determined and achieved in this study can be used to facilitate and enhance any further studyas base knowledge in merging the ANN with another AI to form a hybrid algorithm. &nbsp

Consolidation Behavior of Metal Powder in Additive Manufacturing

13301甲第4038号博士（工学）金沢大学博士論文要旨Abstract 要約Outlin

Consolidation Behavior of Metal Powder in Additive Manufacturing

13301甲第4038号博士（工学）金沢大学博士論文本文Ful

Investigation of laser consolidation process for metal powder by two-color pyrometer and high-speed video camera

This paper deals with the measurement of surface temperature on metal powder during the laser consolidation process with two-color pyrometer. Additionally, the aspect of selective laser sintering (SLS) and selective laser melting (SLM) of metal powder is visualized with high speed video camera. As a result, the surface temperature during the laser irradiation was ranged 1520-1810 °C and the consolidation phenomena was classified according to the melting point of metal powder. The metal powder at the heating process cohered intermittently to the melt pool although the laser beam was continuously irradiated to the powder surface. © 2013 CIRP

Failure Behaviour Of 3D-Printed ABS Lattice Structure Under Compression

Lattice structure is a lightweight material that can be produced using the cutting edge additive layer manufacturing process or also known as 3D printing. Lattice structure material is a periodic cellular structure material that can be utilized in various applications especially as core material in sandwich structure configuration, where the ultimate aim is to be a lightweight material with load bearing capability. Researches are yet to be done to fully understand the behavior of lattice structure materials under several loading conditions such as tensile, bending and compression. The objective of this paper is to discuss the behavior of acrylonitrile-butadiene-styrene (ABS) lattice structure material that was produced using the layer by layer manufacturing, subjected to compressive load. Lattice structure specimens with dimension 20x20x20 mm3 were designed with body centered cubic (BCC) unit cells for three sets of strut diameter size. The specimens were produced using fused deposition modelling (FDM) Cubepro 3D printer, with varying default parameters of layer thickness, print strength and print pattern. All specimens were subjected to compressive load until densification stage and the stress-strain curves of the material were plotted. The compressed specimens were observed under an optical digital microscope and a common failure behavior of 3D-printed ABS lattice structure material was highlighted. It was shown that the failure of compressed lattice structure was initiated at joint node areas due to bending tensile stress. It can be concluded that this polymer material showed hybrid between stretch and bending-dominated characteristics. This is a good indicator for lightweight material with load absorbing capability. An understanding in the failure behavior of ABS lattice structure material is enriching the knowledge on this material under stress-strain condition