Details

Autor(en) / Beteiligte

• Van Bo, Nguyen
• Te, Ba
• Teo, Augustine
• Ahluwalia, Kunal
• Aramcharoen, Ampara
• Chang Wei, Kang

Titel

Process Model for Evaluating the Peen Velocity in Shot Peening Machine

Ist Teil von

• Advanced Surface Enhancement, p.26-35

Ort / Verlag

Singapore: Springer Singapore

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Peening velocity (shot velocity) is one of the key parameters in shot peening process, which directly relates to intensity and coverage area. A desired intensity and/or coverage area can be attained by controlling the peening velocity to the right value. However, this is a challenging task as the peening velocity is the function of many different variables (peening system, nozzle design, air pressure, media (shot) flow rate, shot size, etc.). In this study, we develop a process model that links the input/operating parameters of the peening machine to the average shot stream velocity upon impact. In particular, the formulation of shot stream velocity is derived as the function of input air pressure and media flow rate, which also accounted for the peening system and nature of the flow inside (e.g., nozzle shape, pressure loss, energy transfer, and turbulence, etc.). The model is validated against the experimental data for different inlet pressure as well as the media flow rates. The calculated results are in good agreement with experimental data. Furthermore, the model validity and reliability are examined for the wide range of input parameters and the system parameter. The results also indicated that the developed process model can be applied for different peening machines with different nozzle design by defining relevant model constants. There are a few key applications for the process model; which are (1) the model can support the operators to rapidly estimate and setup the working conditions of the machine to attain the desired peening intensity and coverage area to avoid the cost and time in doing experiments based on trials and errors, and (2) The model also can be used in model predictive control (MPC) to develop the controller for the peening machine.