Biblio

In this paper, we use selective laser melting (SLM) technology to fabricate AlNiCo magnetic materials, and the effects of laser processing parameters on the density and mechanical properties of AlNiCo magnetic materials were studied. We tested the magnetic properties of the heat-treated magnets. The results show that both laser power and scanning speed affect the forming. In this paper, the influence of laser power on the density of samples far exceeds the scanning speed. Through the experiment, we obtained the optimal range of process parameters: laser power (150 170W) and laser scanning speed (800 1000mm/s). Although the samples formed within this range have higher density, there are still many cracks, further research work should be done.

Within “Industrie 4.0” approach 3D printing technology is characterized as one of the disruptive innovations. Conventional supply chains are replaced by value-added networks. The spatially distributed development of printed components, e.g. for the rapid delivery of spare parts, creates a new challenge when differentiating between “original part”, “copy” or “counterfeit” becomes necessary. This is especially true for safety-critical products. Based on these changes classic branded products adopt the characteristics of licensing models as we know them in the areas of software and digital media. This paper describes the use of digital rights management as a key technology for the successful transition to Additive Manufacturing methods and a key for its commercial implementation and the prevention of intellectual property theft. Risks will be identified along the process chain and solution concepts are presented. These are currently being developed by an 8-partner project named SAMPL (Secure Additive Manufacturing Platform).

Additive manufacturing (AM, or 3D printing) is a novel manufacturing technology that has been adopted in industrial and consumer settings. However, the reliance of this technology on computerization has raised various security concerns. In this paper, we address issues associated with sabotage via tampering during the 3D printing process by presenting an approach that can verify the integrity of a 3D printed object. Our approach operates on acoustic side-channel emanations generated by the 3D printer’s stepper motors, which results in a non-intrusive and real-time validation process that is difficult to compromise. The proposed approach constitutes two algorithms. The first algorithm is used to generate a master audio fingerprint for the verifiable unaltered printing process. The second algorithm is applied when the same 3D object is printed again, and this algorithm validates the monitored 3D printing process by assessing the similarity of its audio signature with the master audio fingerprint. To evaluate the quality of the proposed thresholds, we identify the detectability thresholds for the following minimal tampering primitives: insertion, deletion, replacement, and modification of a single tool path command. By detecting the deviation at the time of occurrence, we can stop the printing process for compromised objects, thus saving time and preventing material waste. We discuss various factors that impact the method, such as background noise, audio device changes and different audio recorder positions.

In cyber-physical systems, due to the tight integration of the computational, communication, and physical components, most of the information in the cyber-domain manifests in terms of physical actions (such as motion, temperature change, etc.). This leads to the system being prone to physical-to-cyber domain attacks that affect the confidentiality. Physical actions are governed by energy flows, which may be observed. Some of these observable energy flows unintentionally leak information about the cyber-domain and hence are known as the side-channels. Side-channels such as acoustic, thermal, and power allow attackers to acquire the information without actually leveraging the vulnerability of the algorithms implemented in the system. As a case study, we have taken cyber-physical additive manufacturing systems (fused deposition modeling-based three-dimensional (3D) printer) to demonstrate how the acoustic side-channel can be used to breach the confidentiality of the system. In 3D printers, geometry, process, and machine information are the intellectual properties, which are stored in the cyber domain (G-code). We have designed an attack model that consists of digital signal processing, machine-learning algorithms, and context-based post processing to steal the intellectual property in the form of geometry details by reconstructing the G-code and thus the test objects. We have successfully reconstructed various test objects with an average axis prediction accuracy of 86% and an average length prediction error of 11.11%.

Alternatives to rare earth permanent magnets, such as alnico, will reduce supply instability, increase sustainability, and could decrease the cost of permanent magnets, especially for high-temperature applications, such as traction drive motors. Alnico magnets with moderate coercivity, high remanence, and relatively high-energy product are conventionally processed by directional solidification and (significant) final machining, contributing to increased costs and additional material waste. Additive manufacturing (AM) is developing as a cost effective method to build net-shape 3-D parts with minimal final machining and properties comparable to wrought parts. This paper describes initial studies of net-shape fabrication of alnico magnets by AM using a laser engineered net shaping (LENS) system. High-pressure gas atomized pre-alloyed powders of two different modified alnico “8” compositions, with high purity and sphericity, were built into cylinders using the LENS process, and followed by heat treatment. The magnetic properties showed improvement over their cast and sintered counterparts. The resulting alnico permanent magnets were characterized using scanning electron microscopy, energy dispersive spectroscopy, electron backscatter diffraction, and hysteresisgraph measurements. These results display the potential for net-shape processing of alnico permanent magnets for use in next generation traction-drive motors and other applications requiring high temperatures and/or complex engineered part geometries.

Additive Manufacturing (AM) uses Cyber-Physical Systems (CPS) (e.g., 3D Printers) that are vulnerable to kinetic cyber-attacks. Kinetic cyber-attacks cause physical damage to the system from the cyber domain. In AM, kinetic cyber-attacks are realized by introducing flaws in the design of the 3D objects. These flaws may eventually compromise the structural integrity of the printed objects. In CPS, researchers have designed various attack detection method to detect the attacks on the integrity of the system. However, in AM, attack detection method is in its infancy. Moreover, analog emissions (such as acoustics, electromagnetic emissions, etc.) from the side-channels of AM have not been fully considered as a parameter for attack detection. To aid the security research in AM, this paper presents a novel attack detection method that is able to detect zero-day kinetic cyber-attacks on AM by identifying anomalous analog emissions which arise as an outcome of the attack. This is achieved by statistically estimating functions that map the relation between the analog emissions and the corresponding cyber domain data (such as G-code) to model the behavior of the system. Our method has been tested to detect potential zero-day kinetic cyber-attacks in fused deposition modeling based AM. These attacks can physically manifest to change various parameters of the 3D object, such as speed, dimension, and movement axis. Accuracy, defined as the capability of our method to detect the range of variations introduced to these parameters as a result of kinetic cyber-attacks, is 77.45%.