An Evaluation of Data Size Reduction Techniques for Improving the Reliability of Cloud-based CNC for a 3D Printer

Abstract

Abstract Cloud-based computer numerical control (C-CNC) is an emerging paradigm of Industry 4.0 where CNC functionalities are moved to the cloud and provided to manufacturing machines as a service. Among many benefits, C-CNC allows manufacturing machines to leverage advanced control algorithms running on cloud computers to boost their performance at low cost, without need for major hardware upgrades. However, a major challenge of C-CNC is how to guarantee safe and reliable control of a manufacturing machine from the cloud, given delays and other Internet quality-of-service issues. This paper evaluates several techniques for reducing the size of control commands transmitted to a 3D printer from a cloud-based controller (with elements of C-CNC), in an effort to prevent degradation of 3D printer accuracy and speed caused by high latency Internet connection. Six of the evaluated techniques are popular statistical lossless data compression methods while one of the techniques, advocated in this paper, involves physics-based pseudo-lossless data truncation (i.e., no loss of key information content needed for control). In benchmark tests, the truncation technique is found to perform best in terms of the consistency and magnitude of data reduction ratio. Moreover, while the compression techniques require untenable amounts of memory for decompression on the microprocessor of the 3D printer, the memory requirements of the truncation technique are small. Consequently, using the truncation technique for data size reduction, frequent pauses during print that occur when controlling the 3D printer over high latency Internet connections, and cause poor surface finish of the printed part, are mitigated. As a result, high-quality prints are obtained at high-speed from the cloud-based controller over high latency Internet connections.