Energy Efficient Parallel Configuration Based Six Degree of Freedom Machining Bed

• Main Authors: Zareena Kausar, Muhammad Faizan Shah, Zeeshan Masood, Hafiz Zia Ur Rehman, Sardor Khaydarov, Muhammad Tallal Saeed, Omid Razmkhah, Haseeb Yaqoob
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Energies
• Subjects: energy efficiency; energy consumption; parallel manipulator; machining bed; inverse kinematics; computation time
• Online Access: https://www.mdpi.com/1996-1073/14/9/2642

The process of material removal from a workpiece to obtain the desired shape is termed machining. Present-day material removal technologies have high spindle speeds and thus allow quick material removal. These high-speed spindles are highly exposed to vibrations and, as a result, the accuracy of the final workpiece’s dimensions is compromised. To overcome this problem, the motion of the tool is restricted, and multiple degrees of freedom are given through the motion of the workpiece in different axes. A machining bed configured as a parallel manipulator capable of giving six degrees of freedom (DOF) to the workpiece is proposed in this regard. However, the proposed six DOF machining bed should be energy efficient to avoid an increase in machining cost. The benefit of using the proposed configuration is a reduction in dimensional error and computational time which, as a result, reduces the energy utilization, vibrations, and machining time in practice. This paper presents kinematics, dynamics and energy efficiency models, and the development of the proposed configuration of the machining bed. The energy efficiency model is derived from the dynamics model. The models are verified in simulation and experimentally. To minimize error and computation time, a PID controller is also designed and tested in simulation as well as experimentally. The resulting energy efficiency is also analyzed. The results verify the efficacy of the proposed configuration of the machining bed, minimizing position error to 2% and reducing computation time by 27%, hence reducing the energy consumption and enhancing the energy efficiency by 60%.