Development of a cyber physical production system framework for 3D printing analytics.

3D printing technology is considered one of the emerging areas to deal with global sustainability challenges and to facilitate the Industry 4.0 adoption. However, 3D printing technology is still immature due to several limitations and negative perceptions about its quality and performance. The goal of this paper is to propose a cyber physical production system (CPPS) framework for a 3D printer to (i) monitor the process, parameters, and carbon footprint, (ii) predict the nozzle's remaining useful life (RUL), and (iii) prescribe optimum 3D printing parameters for minimizing carbon footprint and printing time, simultaneously at the targeted surface quality. Experiments were designed based on Taguchi L-27 orthogonal array to investigate the relationship between printing parameters and performance characteristics. The usefulness of the proposed framework has been demonstrated for a 3D printer to predict the remaining useful life of the printer nozzle (prognostic model), and to find an optimal combination of printing parameters for the simultaneous optimization of sustainability and productivity at the targeted surface quality (prescriptive model). Layer height was found to have a statically significant impact on the specific carbon footprint followed by scale and bed temperature. Layer height is the only statically significant contributor to the surface roughness of 3D printed parts. The scale and layer height followed by infill have significant effect on the printing time. The significance of the present work lies in enhancing the performance of a conventional 3D printer using low-cost smart sensors, devices, and open-source software. The usefulness of the proposed CPPS framework is demonstrated as a decision support tool for a 3D printer real-time monitoring, visualization, and control. The proposed CPPS framework and its application for prognostic and prescriptive analytics is generic in nature, and is transferable and applicable to other FDM 3D printers, irrespective of brand and size. • Prescriptive analytics with inbuilt optimization model. • Prognostic analytics for predicting remaining useful life of a 3D printer nozzle. • Optimizes eco-design of 3D printed parts, based on optimization of printing time and specific carbon footprint. • Dashboard for ease of decision making. • Enhancing the capabilities of a conventional 3D printer using low-cost smart sensors, devices, and open-source software. [ABSTRACT FROM AUTHOR]