Posture Optimization Techniques for Enhanced Robotic Machining Performance: A Review

Naiara Sebbe; Jose-Luis Lafuente; Clara Aibar; Ivan Iglesias

doi:10.53941/jmem.2026.100013

Abstract

With the advancement of technology and the digitalization of manufacturing processes, optimized production of large-scale structural components faces increasing challenges due to mass product customization, workspace constraints, and the high cost of equipment such as CNC machines. Arm-based robotic manufacturing systems, as a substitutive or complementary alternative with strong potential, have gained significant attention as a means to address current challenges in machining. This work presents a state-of-the-art review of existing techniques and methods for optimizing robotic machining performance, with a particular focus on research advances in posture optimization of articulated robots. These advances are enabling the use of serial robotic arms in material removal operations as a technically and economically viable alternative to conventional machine tools. The document is structured as a comprehensive knowledge recap and systematic analysis of the technical barriers and limitations that affect dimensional quality in robotic machining—primarily due to the limited mechanical stiffness inherent in robotic arms when performing cutting tasks. It compiles and classifies a range of robot posture optimization tools. A SWOT matrix is used to identify the key technical factors that influence the suitability of optimization methods in different industrial contexts. Unlike previous reviews that address posture or dynamics in isolation, this work (i) consolidates posture optimization methods into a practical three-pillar framework (optimize–sense–compensate), (ii) maps methods to a decision-oriented SWOT for industrial selection, and (iii) extends the analysis to collaborative machining, including posture–safety trade-offs under collaborative machining standards.

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