Analyzing the evolution of tool wear area in trochoidal milling of Inconel 718 using image processing methodology. (September 2022)

• Record Type: Journal Article
• Authors: Agarwal, Ankit; Potthoff, Nils; Shah, Aash M; Mears, Laine; Wiederkehr, Petra
• Is Part Of: Manufacturing letters. Volume 33(2022)Supplement
• Page Start: 373
• Page End: 379
• Publication Date: 2022-09
• Subjects: Tool wear area -- Trochoidal milling -- Image processing -- Inconel 718; Manufacturing industries -- Periodicals; Production engineering -- Periodicals; Manufacturing industries; Periodicals; 670
• DOI: 10.1016/j.mfglet.2022.08.002
• Languages: English
• ISSNs: 2213-8463

Abstract

Abstract: Nickel-based superalloys belong to a category of material employed for extreme conditions and exhibit high strength properties at elevated temperatures that result in poor machinability. Machining such difficult-to-cut materials like Inconel 718 leads to a high rate of tool wear, and therefore trochoidal milling toolpath is used to improve productivity and tool life. The current study analyzes the evolution of the flank wear area of the tool during trochoidal milling of Inconel 718 using an image processing methodology. It is attempted by performing experimental studies until tool failure occurs at several cutting conditions. The machining is performed through several iterations on an identical cutting path, and the number of iterations to failure is recorded. The microstructural image of a flank wear area is captured upon each iteration and processed using an image processing algorithm. It is realized that the evaluation of flank wear area can be an effective parameter to analyze tool wear. Also, the image processing methodology works effectively and can be extended during real-time machining.