Mohammad Aliakbari
Papers
3
Total Citations
38
H-Index
3
About
Mohammad Aliakbari is a researcher advancing the frontiers of intelligent manufacturing and precision metrology. His work centers on the kinematic optimization and error compensation of parallel robotic systems for coordinate measuring machines (CMMs). Aliakbari’s major contributions lie in developing adaptive, computer-aided path planning algorithms that eliminate contact probe errors on free-form surfaces, and in creating self-calibrating 4-DOF parallel robots for precise geometric data collection in digital manufacturing. His most cited paper, "An adaptive computer-aided path planning to eliminate errors of contact probes on free-form surfaces using a 4-DOF parallel robot CMM and a turn-table" (2020), has garnered 24 citations, reflecting its impact on improving measurement accuracy. In a subsequent 2021 study, he introduced a computer-integrated approach to enhance the work-space quality of the C4 parallel robot CMM by modeling kinematic errors, enabling performance optimization without altering original designs—a key challenge in intelligent manufacturing. Aliakbari’s work is notable for bridging robotics, metrology, and digital manufacturing, offering practical solutions for quality control and process health monitoring in industrial settings.
Research Focus
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Top Papers
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