## 1 Introduction
[PERSON] and [PERSON] [20] addressed the issue of freeform surface contour errors' sensitivity to geometric errors in ultra-precision machine tools and proposed a mathematical mapping model of geometric errors and contour errors and an error compensation method based on sensitivity parameters. Experiments indicated that this method could reduce freeform surface contour errors by approximately 30.85%. [PERSON] et al. [21] introduced an accurate error compensation method for multi-axis parallel machines based on Jacobi geometric parameter correction and coupling error evaluation, reducing the position and posture errors of the parallel mechanism by 64.93% and 55.29%, respectively, and significantly improving motion accuracy. [PERSON] et al. [22] proposed an error compensation method using a radial basis function neural network optimized by the whale optimization algorithm, enhancing the average pose accuracy of parallel mechanisms by 85.07% and thereby validating the effectiveness of intelligent algorithms in error compensation for complex systems. [PERSON] et al. [23] proposed a unified geometric error model for three-axis machine tools, reducing the geometric errors of machine tools by over 85% and significantly simplifying the complexity of machine tool error compensation. [PERSON] et al. [24] proposed a mechanism-data hybrid approach for dual-drive machine tools, establishing a comprehensive error model based on multi-body system dynamics and optimizing geometric errors through sensitivity-guided grey wolf optimization, which reduced tool center positioning errors by 41% and axis non-synchronization errors by 35%. [PERSON] et al. [25] proposed a deep learning-based approach for comprehensive pose error prediction in parallel robots, integrating an encoder-decoder network framework with multi-layer convolutional sparse coding (CSC) and efficient channel attention (ECA) modules. The method leverages Monte Carlo dropout to estimate prediction uncertainties, addressing both geometric and non-geometric error components. Experimental validation on a Stewart platform demonstrated high accuracy and robustness under various loading conditions, with superior performance compared to typical models. The results highlight the method's potential to enhance operational precision and reliability in high-precision applications. In summary, significant advancements have been made in the field of error modelling and compensation for complex mechanical systems. However, there is a paucity of studies focusing on error compensation methods for heavy-load precision assembly equipment in enclosed or semi-enclosed and confined spaces. HLTSTs are rated for a minimum load of 2.5 t, with a maximum reach of less than 500 mm \(\times\) 500 mm \(\times\) 200 mm. This often makes it challenging to reasonably layout the position detection sensor, thereby making it difficult to directly measure the coupling error generated by the multi-axis motion. As a result, full closed-loop control cannot be achieved, and the focus of the research shifts to how to compensate for coupling error under these restricted conditions. Furthermore, the existing error compensation methods do not trace the geometric errors from the perspective of machining, assembly, and other intrinsic mechanisms, resulting in the inability to provide guidance for the design and machining of parts at the source. To address the issues outlined above, this study introduces a precise error compensation technique that encompasses both geometric parameter correction and process optimization. By correcting the key geometric parameters of the inverse kinematics model of the table's pitch and roll layers, the effect of contour errors on the table's pose is reduced. The coupling error model of rotary axes is established, and the decoupling compensation strategy for the table's position error is proposed. The main characteristics of independent and coupling errors are analyzed through numerical simulation to optimize the tolerance design of key parts.