IoT feasibility aspects of mold temperature monitoring and casting simulation for smart foundry

Barot, R.S.; patel, harshad; Devmurari, R.; Shah, Kalpak; Sharma, Bharat K.; Shah, Jay P.

doi:10.1016/j.matpr.2020.05.153

Cited by 4 publications

(1 citation statement)

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“…Moreover, deep learning methods to process images of the castings can further identify visually hard to find defects. The possibility of preventing defects and improve quality in castings through implementing Internet of things (IoT) has been studied by a few researchers (Barot et al , 2020). However, the application of IoT in various stages of casting process must be explored in future to have end-to-end visibility and integration of material, product and information flow.…”

Section: Research Gaps and Future Directionsmentioning

confidence: 99%

Critical parameters influencing the quality of metal castings: a systematic literature review

Suthar

Persis

Gupta

2021

IJQRM

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PurposeCasting is one of the well-known manufacturing processes to make durable parts of goods and machinery. However, the quality of the casting parts depends on the proper choice of process variables related to properties of the materials used in making a mold and the product itself; hence, variables related to product/process designs are taken into consideration. Understanding casting techniques considering significant process variables is critical to achieving better quality castings and helps to improve the productivity of the casting processes. This study aims to understand the computational models developed for achieving better quality castings using various casting techniques.Design/methodology/approachA systematic literature review is conducted in the field of casting considering the period 2000–2020. The keyword co-occurrence network and word cloud from the bibliometric analysis and text mining of the articles reveal that optimization and simulation models are extensively developed for various casting techniques, including sand casting, investment casting, die casting and squeeze casting, to improve quality aspects of the casting's product. This study further investigates the optimization and simulation models and has identified various process variables involved in each casting technique that are significantly affecting the outcomes of the processes in terms of defects, mechanical properties, yield, dimensional accuracy and emissions.FindingsThis study has drawn out the need for developing smart casting environments with data-driven modeling that will enable dynamic fine-tuning of the casting processes and help in achieving desired outcomes in today's competitive markets. This study highlights the possible technology interventions across the metal casting processes, which can further enhance the quality of the metal casting products and productivity of the casting processes, which show the future scope of this field.Research limitations/implicationsThis paper investigates the body of literature on the contributions of various researchers in producing high-quality casting parts and performs bibliometric analysis on the articles. However, research articles from high-quality journals are considered for the literature analysis in identifying the critical parameters influencing quality of metal castings.Originality/valueThe systematic literature review reveals the analytical models developed using simulation and optimization techniques and the important quality characteristics of the casting products. Further, the study also explores critical influencing parameters involved in every casting process that significantly affects the quality characteristics of the metal castings.

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Section: Research Gaps and Future Directionsmentioning

confidence: 99%