DOI:10.20894/IJDMTA.
Periodicity: Bi Annual.
Impact Factor:
SJIF:4.893 & GIF:0.787
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded
Manufacturing, being referred to, as an integrated management process has been radically transformed with the advent of latest technologies and advancements. Data generated by machines are continuously encapsulated and archived in storage systems for analysis and futuristic applications.Such storage necessitates enormous warehouse capacities thus vitalizing methods deployed for storage. Apart from vitality, they serve as critical parameters in improving efficiency, as well. With regard to engine assembly plants, these data can comprehend subtle characteristics of the engine assembly and further their testing processes, as well.Simply put, each engine assembly comprises a minimum of 50 major assembly process parameters and 15 pivotal testing parameters meant for observation and record. Of course, they are of great significance to restore lineage and traceability.Competition demands the use of such legacy data repositories of automotive engine manufacturing companies owing to market demand. Hence trigger the need to deploy appropriate techniques to secure business insights for effective decision-making.Data Mining (DM) is one such subject that mandates a thorough and detailed study. Here in this paper it is handled with the clustering analysis of the subject DM with a sample data volume of five hundred engines� performance test result files.
