Abstract--Correct identification of failure mechanisms is essential for manufacturers to ensure the quality of their products. Certain failures of printheads developed by Canon Production Printing can be identified from the behavior of individual nozzles, the states of which are constantly recorded and can form distinct patterns in terms of the number of failed nozzles over time, and in space in the nozzle grid. In our work, we investigate the problem of printhead failure classification based on a multifaceted dataset of nozzle logging and propose a Machine Learning classification approach for this problem. We follow the feature-based framework of time-series classification, where a set of time-based and spatial features was selected with the guidance of domain experts. Several traditional ML classifiers were evaluated, and the One-vs-Rest Random Forest was found to have the best performance. The proposed model outperformed an in-house rule-based baseline in terms of a weighted F1 score for several failure mechanisms. Identifying failure mechanisms is a critical part of industrial corrective maintenance for manufacturers to ensure the quality of their products [1].

Personal use of this material is permitted. This paper has been published in the 8th IEEE Conference on Industrial Cyber-Physical Systems (ICPS) in Emden, Germany, May 12-15, 2025. Abstract --Accurately predicting the lifespan of critical device components is essential for maintenance planning and production optimization, making it a topic of significant interest in both academia and industry. In this work, we investigate the use of survival analysis for predicting the lifespan of production printheads developed by Canon Production Printing. Specifically, we focus on the application of five techniques to estimate survival probabilities and failure rates: the Kaplan-Meier estimator, Cox proportional hazard model, Weibull accelerated failure time model, random survival forest, and gradient boosting. The resulting estimates are further refined using isotonic regression and subsequently aggregated to determine the expected number of failures. The predictions are then validated against real-world ground truth data across multiple time windows to assess model reliability. Our quantitative evaluation using three performance metrics demonstrates that survival analysis outperforms industry-standard baseline methods for printhead lifespan prediction.

As robotics advances toward integrating soft structures, anthropomorphic shapes, and complex tasks, soft and highly stretchable mechanotransducers are becoming essential. To reliably measure tactile and proprioceptive data while ensuring shape conformability, stretchability, and adaptability, researchers have explored diverse transduction principles alongside scalable and versatile manufacturing techniques. Nonetheless, many current methods for stretchable sensors are designed to produce a single sensor configuration, thereby limiting design flexibility. Here, we present an accessible, flexible, printing-based fabrication approach for customizable, stretchable sensors. Our method employs a custom-built printhead integrated with a commercial 3D printer to enable direct ink writing (DIW) of conductive ink onto cured silicone substrates. A layer-wise fabrication process, facilitated by stackable trays, allows for the deposition of multiple liquid conductive ink layers within a silicone matrix. To demonstrate the method's capacity for high design flexibility, we fabricate and evaluate both capacitive and resistive strain sensor morphologies. Experimental characterization showed that the capacitive strain sensor possesses high linearity (R^2 = 0.99), high sensitivity near the 1.0 theoretical limit (GF = 0.95), minimal hysteresis (DH = 1.36%), and large stretchability (550%), comparable to state-of-the-art stretchable strain sensors reported in the literature.

Epson announced its first industrial direct-to-garment printer the SureColor F3070. Designed to provide low total cost of ownership (TCO) and reliability for exceptional performance and savings in high-production print shop environments, the SureColor F3070 is Epson's first direct-to-garment printer to leverage dual printhead technology and a bulk ink pack system, providing remarkably low ink cost and minimized waste compared to Epson cartridge systems. It touts all new automatic garment height adjustment and easy user maintenance features to increase production and reduce downtime for garment print shops creating custom apparel. The new printer will be on display in Epson's booth (#2049) at Impressions Expo in Long Beach, Calif. "The printed textile market is seeing tremendous growth, complemented by new printing technology innovation," said Tim Check, senior product manager, Professional Imaging, Epson America, Inc. "We are committed to driving the digital textile market and excited to expand Epson's award-winning product line to deliver our first industrial direct-to-garment printer for high-production print shops. Designed for mid-to-large size garment printers looking for a high-production, cost-effective equipment, the SureColor F3070 can produce a full-size shirt in about a minute, allowing shops to print hundreds of shirts per day."

However, most commercial printers are only able to build objects from a single material at a time and inkjet printers that are capable of multimaterial printing are constrained by the physics of droplet formation. Extrusion-based 3D printing allows a broad palette of materials to be printed, but the process is extremely slow. For example, it would take roughly 10 days to build a 3D object roughly one liter in volume at the resolution of a human hair and print speed of 10 cm/s using a single-nozzle, single-material printhead. To build the same object in less than 1 day, one would need to implement a printhead with 16 nozzles printing simultaneously! Now, a new technique called multimaterial multinozzle 3D (MM3D) printing developed at Harvard's Wyss Institute for Biologically Inspired Engineering and John A. Paulson School of Engineering and Applied Sciences (SEAS) uses high-speed pressure valves to achieve rapid, continuous, and seamless switching between up to eight different printing materials, enabling the creation of complex shapes in a fraction of the time currently required using printheads that range from a single nozzle to large multinozzle arrays.