Monitoring of Self-Trapping Screw Fastenings Using Artificial Neural Networks  

Oleh:

Lara, Bruno ; Seneviratne, Lakmal D. ; Althoefer, Kaspar

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Journal of Manufacturing Science and Engineering vol. 127 no. 1 (Feb. 2005), page 236-243.
Topik: network; monitoring; self - tapping; screw; aritificial neural network

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: JJ93.6 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

Screw fastenings account for a quarter of all assembly operations and automation of the process is highly desirable. This paper presents a novel strategy for monitoring this manufacturing process, focusing on the insertion of self-tapping screws. An artificial neural network (ANN), using "Torque - versus - Insertion - Depth" signature signals as input, is designed to distinguish between successful and failed insertions. The ANN is first tested using simulation data from an analytical model for screw insertions, and then validated using experimental torque signals obtained from an electric screwdriver. The results demonstrate that ANNs can effectively monitor the screw fastening process and cope with a wide range of insertion cases interpolating for unseen insertion signals.

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