Smart manufacturing and Industry 4.0 are bringing disruptive changes to the manufacturing sector. Smart manufacturing increases productivity, creates safer conditions for workers, and simplifies product customization, all while decreasing business expenses [2]. To this end, we have created a flexible three-component architecture for remote machine management, using it to build a digital twin prototype of a Creality Ender-3 Pro 3D printer located on the University at Buffalo campus. This twin provides users with the ability to monitor and control the machine from anywhere in the world through a web interface. Our system improves upon existing technologies, such as Octoprint [1], through the addition of twin views. It also relies upon cheaper components, using the Arduino and ESP32 rather than the Raspberry Pi. Finally, existing technologies tend to focus on one specific type of machine. In contrast, our framework is flexible, capable of supporting many different machines.

To address the increasing challenges of counterfeit detection and IP protection for 3D printing, we propose that every 3D printer holds unique fingerprinting features characterized by the thermodynamic properties of the extruder hot-end and can be used as a new way of 3D watermarking. We prove that these physical fingerprints resulting from manufacturing imperfections and system variations exhibit distinct heating responses, namely “ThermoTag,” which can be represented as the distinguishable thermodynamic processes and, ultimately, the temperature readings during the preheating process. Experimental results show that, by only changing the hot-ends of the same model on the same 3D printer, we can achieve about 92% identification accuracy amongst 45 hot-ends. The permanence and robustness of ThermoTag for the same hot-end were examined, throughout a period of one month with hundreds of trials under different environmental temperature settings. Leveraging the hidden ThermoTag, an example of watermarking scheme in 3D printing is presented and evaluated.

Recently, continuous 3D printing, a revolutionary branch of legacy additive manufacturing, has made its two-order time efficiency breakthrough in industrial manufacturing. As its manufacturing technique advances rapidly, the prefabrication to slice the 3D object into image layers becomes potential to impede further improvement of production efficiency. In this paper, we present two scalable and efficient graphic processing unit (GPU) enabled schemes, i.e., pixelwise parallel slicing and fully parallel slicing, to accelerate the image-projection based slicing algorithm in continuous 3D printing. Specifically, the pixelwise approach utilizes the pixel-level parallelism and exploits the in-shared-memory computing on GPU. The fully parallel method aggressively expands the parallelism on both triangle mesh size and slicing layers. The thread-level priority competing issue, resulting from full parallelism, is addressed by a critical area using atomic operation. Experiments with real 3D object benchmarks show that our pixelwise parallel slicing can gain one order of magnitude runtime reduction to CPU, and the fully parallel slicing achieves two orders improvement. We also evaluate the scalability of both proposed schemes.

As the next-generation manufacturing driven force, 3D printing technology is having a transformative effect on various industrial domains and has been widely applied in a broad spectrum of applications. It also progresses towards other versatile fields with portable battery-powered 3D printers working on a limited energy budget. While reducing manufacturing energy is an essential challenge in industrial sustainability and national economics, this growing trend motivates us to explore the energy consumption of the 3D printer for the purpose of energy efficiency. To this end, we perform an in-depth analysis of energy consumption in commercial, off-the-shelf 3D printers from an instruction-level perspective. We build an instruction-level energy model and an energy profiler to analyze the energy cost during the fabrication process. From the insights obtained by the energy profiler, we propose and implement a cross-layer energy optimization solution, called 3DGates, which spans the instruction-set, the compiler and the firmware. We evaluate 3DGates over 338 benchmarks on a 3D printer and achieve an overall energy reduction of 25%.

Inkjet 3D printing is a disruptive manufacturing technology in emerging metal- and bio-printing applications. The nozzle of the printer deposits tiny liquid droplets, which are subsequently solidified on a target location. Due to the elegant concept of micro-droplet deposition, inkjet 3D printing is capable of achieving a sub-millimeter scale manufacturing resolution. However, the droplet deposition process is dynamic and uncertain which imposes a significant challenge on quality assurance of inkjet 3D printing in terms of product reproducibility and process repeatability. To this end, we present Luban as a certification tool to examine the printing quality in the inkjet printing process. Luban is a new low-cost and in-situ droplet micro-sensing system that can precisely detect, analyze and localize a droplet. Specifically, we present a novel tiny object sensing method by exploiting the computational light beam field and its sensitive interference effect. The realization of Luban is associated with two technical thrusts. First, we study integral sensing, i.e., a new scheme towards computational light beam field sensing, to efficiently extract droplet location information. This sensing scheme offers a new in-situ droplet sensing modality, which can promote the information acquisition efficiency and reduce the sensing cost compared to prior approaches. Second, we characterize interference effect of the computational light beam field and develop an efficient integration-domain droplet location estimation algorithm. We design and implement Luban in a real inkjet 3D printing system with commercially off-the-shelf devices, which costs less than a hundred dollars. Experimental results in both simulation and real-world evaluation show that Luban can reach the certification precision of a sub-millimeter scale with a 99% detection accuracy of defect droplets; furthermore, the enabled in-situ certification throughput is as high as over 700 droplets per second. Therefore, the performance of our Luban system can meet the quality assurance requirements (e.g., cost-effective, in-situ, high-accuracy and high-throughput) in general industrial applications.

Additive manufacturing, also known as 3D printing, has been increasingly applied to fabricate highly intellectual property (IP) sensitive products. However, the related IP protection issues in 3D printers are still largely underexplored. On the other hand, smartphones are equipped with rich onboard sensors and have been applied to pervasive mobile surveillance in many applications. These facts raise one critical question: is it possible that smartphones access the side-channel signals of 3D printer and then hack the IP information? To answer this, we perform an end-to-end study on exploring smartphone-based side-channel attacks against 3D printers. Specifically, we formulate the problem of the IP side-channel attack in 3D printing. Then, we investigate the possible acoustic and magnetic side-channel attacks using the smartphone built-in sensors. Moreover, we explore a magnetic-enhanced side-channel attack model to accurately deduce the vital directional operations of 3D printer. Experimental results show that by exploiting the side-channel signals collected by smartphones, we can successfully reconstruct the physical prints and their G-code with Mean Tendency Error of 5.87% on regular designs and 9.67% on complex designs, respectively. Our study demonstrates this new and practical smartphone-based side channel attack on compromising IP information during 3D printing.