SUSTAINABLE PHOTO PRINTING THROUGH SMART OPTIMIZATION - Singh, Tanya; Nanda, Simranjeet; Sarna, Shilpi; Khurana, Shivam; B, Bharathi; Kaushik, Naresh; Lahane, Payal Sunil (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The increased pressure on the quality of digital imaging has exacerbated the environmental impact of photo printing, and it is necessary to have sustainable and smart production systems. With the introduction of innovative technologies and methods to combine high-quality artificial intelligence (AI), Internet of Things (IoT) sensing, and lifecycle analytics, it is possible to make a radical change in the course of eco-efficient printing operations. A clever optimization system in this study is created to reduce the resource usage and print faithfulness, creating a closed-loop system and integrating perception, computation, and control. The six-layer architecture proposed in the paper that includes the processes of sensitized input acquisition, hybrid optimization, adaptive control, process execution, performance monitoring and sustainability analytics is a self-learning ecosystem capable of continuous improvement. A Hybrid Optimization Kernel which is constructed on Multi-Objective Genetic Algorithms (MOGA), and Reinforcement Learning (RL) is used to make real-time decisions in order to balance conflicting goals like energy savings, ink saving and visual quality. The monitoring system uses such quantitative measures as the energy intensity (kWh/print), ink efficiency, image quality indices (PSNR, SSIM, DE), and input to lifecycle and eco-efficiency measurements. Findings indicate that the energy and materials saving is very high, and the quality of prints remains optimized in dynamic operation conditions. The introduction of sustainability as an operating limit instead of a goal ensures a new model of AI-powered, resource-aware photo printing in accordance with the world green manufacturing objectives.