Unauthorized usage and cybersecurity risks in additively manufactured composites: Toolpath reconstruction using imaging and machine learning techniques

The field of Additive manufacturing techniques has witnessed tremendous growth in recent years. It is considered to be one of the forerunners of the Industry 4.0 paradigm, owing to its immense potential to transform the manufacturing industry with its high efficiency compared to the conventional techniques. AM techniques are increasingly being employed for producing 'customized' composite materials with tailored properties suitable for user-specific applications, including safety-critical systems like satellite components. However, there exists some drawbacks in AM parts such as inconsistency in part quality, porosity control, etc. which can lead to huge financial loss and damage in industrial production, thereby hindering the expected growth in adoption of AM systems by various industries. Some of the countermeasures against these cases was development of in-line monitoring systems including advanced sensors and computing capabilities including application of Artificial Intelligence (AI)/Machine Learning (ML) which has proven to be effective in defect detection and quality assessment. However, the processes develop to be more data-intensive and dependent on computerization, which makes the system vulnerable to cyber-intrusion. The chain of attacks possible includes modification of the design file and manipulation of printing parameters such as thermal (nozzle temperature) and filament values. These may not be easily detectable without mechanical testing, effectively sabotaging the manufactured part. Another risk, which has emerged recently is the possibility of counterfeit production of high-quality AM parts. It has been shown to be possible in fibre reinforced composites using reverse engineering, by the misuse of ML techniques on imaging results to reconstruct toolpath information. In this paper, we try to focus on these shortcomings of AM techniques by discussing the severity and impacts of these risks and the current state-of-the art countermeasures including steady process monitoring and intellectual property (IP) protection. This is vital in identifying future issues to be addressed for continuous process improvement and increasing adoption of AM.