Synthesis of an aluminum alloy metal matrix composite using powder metallurgy : role of sintering parameters

Powder metallurgy-based metal matrix composites (MMCs) are widely chosen and used for the development of components in the fields spanning aerospace, automotive and even electronic components. Engineered MMCs are known to offer a high strength-to-weight (σ/ρ) ratio. In this research study, we synthesized cylindrical sintered samples of a ceramic particle-reinforced aluminum metal matrix using the technique of powder metallurgy. The samples for the purpose of testing, examination and analysis were made by mixing aluminum powder with powders of silicon carbide and aluminum oxide or alumina. Four varieties of aluminum composite were synthesized for a different volume percent of the ceramic particle reinforcement. The hybrid composite contained 2 vol.% and 7 vol.% of silicon carbide and 3 vol.% and 8 vol.% of alumina with aluminum as the chosen metal matrix. Homogeneous mixtures of the chosen powders were prepared using conventional ball milling. The homogeneous powder mixture was then cold compacted and subsequently sintered in a tubular furnace in an atmosphere of argon gas. Five different sintering conditions (combinations of temperature and sintering time) were chosen for the purpose of this study. The density and hardness of each sintered specimen were carefully evaluated. Cold compression tests were carried out for the purpose of determining the compressive strength of the engineered MMC. The sintered density and hardness of the aluminum MMCs varied with the addition of ceramic particle reinforcements. An increase in the volume fraction of the alumina particles to the Al/SiC mixture reduced the density, hardness and compressive strength. The sintering condition was optimized for the aluminum MMCs based on the hardness, densification parameter and cold compressive strength. The proposed powder metallurgy-based route for the fabrication of the aluminum matrix composite revealed a noticeable improvement in the physical and mechanical properties when compared one-on-one with commercially pure aluminum

Influence of sintering on mechanical response of metal injection moulded parts

Sintering is often the final step during Metal Injection Molding (MIM) and does in an observable way contribute to influencing both the characteristics and performance of final products. In this research paper, the influence of sintering parameters on dimensional stability and mechanical properties of parts having three different shapes is presented and adequately discussed. A CI 90 feedstock having 90 weight percent of Carbonyl Iron powder was prepared by mixing the carbonyl iron powder with an organic binder. The GSGR75 feedstock has 75 weight percent of powder of grinding sludge and graphite. The green samples were subject to thermal debinding at 750°C. The brown samples were sintered at the temperatures of 1100°C, 1200°C, and 1300°C for 60 minutes and in atmospheres of vacuum, argon, and nitrogen. The sintering characteristics of the parts that were produced from the use of the grinding sludge powder (GSGR) were found to be inferior to those produced from the use of the carbonyl iron (CI) powder. This is essentially because of the limited degree of reduction coupled with the presence and distribution of a sizable number of pores. The sintering parameters did exert an influence on the properties of the as-sintered end-product. Distinct empirical relations were developed and verified for the purpose of predicting properties of the sintered product based on the sintering parameters used

Towards Applicability of Machine Learning in Quality Control – a Review

Quality control is one of the tasks involved in the industry that needs reform in the technological field by the use of machine learning. Machine learning will aid in the reduction of human effort and the improvement of product quality. As an emerging field, machine learning can provide a suitable solution for fast and reliable information for quality determination through the use of various algorithms such as neural networks. It improves the efficiency of the machining operations. With the coming of new providers of the internet of things and machine learning technologies, there has been a move from classical approaches of data acquisition to machine learning-based data acquisition methods. This review paper provides information about using ML-based techniques in the quality determination of the products and provides various algorithms that can be used in manufacturing.</jats:p

Digital Twins for Manufacturing Process and System: A State of the Art Review

As the industry is stepping into industry, 4.0 Digital Twin Technology has been seen as the path for the implementation of virtual manufacturing for the betterment of industry to produce more efficient and reliable products with minimal modification requirements further as per need of the hour for making the product more suitable in the market. Being a rapidly growing technology there is much research work going on it at the current stage in a different form of the industry by utilizing multiple domains of IoT, data acquisition, cloud computing, model and empirical-based design process, etc. This review article will enlighten on the research work done particularly on Digital Twin Technology previously and identify gaps in the research by characterizing the technology on state of concept, terminology, and process associated with it as well as giving a brief idea about how Digital Twin Technology is beneficial to manufacturing.</jats:p