Jaime Andres Camelio

With the latest advances in computer and networking technologies, the threat of cyber-physical attacks against manufacturing systems is growing. Unlike traditional cyber-attacks, cyber-physical attacks are not limited to intellectual property theft and affect the physical world, which could be devastating to manufacturing, if they are undetected. Relying on traditional quality control to defend against these malicious attacks, manufacturers can choose to either closely monitor a large number of potential quality characteristics or only monitor a specific subset of the characteristics. However, the former choice may be impractical when a large number of potential characteristics exists, whereas the latter might be susceptible to an intelligently designed attack that targets unmonitored characteristics. Therefore, a novel random variable-selection approach that is both resilient to malicious cyber-physical attacks and sensitive to shifts over a small subset of characteristics is proposed in this work. Such an approach is based upon random sampling strategies when using multivariate Hotelling T ² control charts. To assess its usefulness, the proposed approach was compared to an established variable-selection method, using a simplified cost model. The obtained results show that the proposed approach is both cost-effective and well-suited for industrial applications where the number of quality characteristics to monitor is quite significant.

With advances in computer and networking technologies, along with the increasing dependency on interconnected cyber-physical components, the threat of cyber-physical attacks against manufacturing is on the rise. As opposed to traditional cyber-attacks, cyber-physical attacks go beyond intellectual property theft and can affect the physical world. In manufacturing, such attacks can result in changed product designs, manipulated manufacturing equipment, and altered final products. For over a century, manufacturing systems have relied heavily on Quality Control (QC) systems to ensure stable processes and product integrity. However, previous research has suggested that current QC tools could be exploited by an adversary, making it difficult or even impossible to detect attacks. Unfortunately, there has been little to no effort to identify/understand opportunities where QC tools could be exploited, which is an essential step toward developing new cyber-security solutions for manufacturing. In response, this paper establishes a systematic approach to effectively categorize QC tool vulnerabilities. Furthermore, to highlight the importance of this research to the manufacturing community, the negative effects of exploiting QC tools by cyber-physical attacks are demonstrated in this paper. Finally, best practices and guidelines for better cyber-physical security in manufacturing are also presented.

Compliance with tighter emission regulations has increased the proportion of parasitic weight in commercial vehicles. In turn, the amount of payload must be reduced to comply with transportation weight requirements. A re-design of commercial vehicle components is necessary to decrease the vehicle weight and improve payload capacity. Side rails have traditionally been manufactured from high strength steels, but significant weight reductions can be achieved by substituting steel side rails with 6013 high strength aluminum alloy side rails. Material and stress analyses are presented in this paper in order to understand the effect of manufacturing process on the material's mechanical behavior. Metallographic and tensile test experiments for the 6013-T4 alloy were performed in preparation for residual stress measurements of a punching operation. Punched holes are critical to the function of the side rail and can lead to high stress levels and cracking. Residual stresses were measured for holes positioned in the web center and near the forming edge of the side rail using x-ray diffraction methods. Comparisons are made between 6013-T4 and 6013-T6 as well as roll-formed and nonroll-formed specimens. The results have been used to identify critical areas that may be susceptible to cracking and failure.

Compliant sheet metal assembly is a hierarchical manufacturing process that plays a significant role in automotive product development. Parts are joined in different stations to form the final product (e.g., the vehicle body structure). Dimensional variation is a product attribute of major importance that characterizes quality, and is mainly affected by the variability of parts, fixtures, and joining methods at each of the multiple stations. The propagation of dimensional variation through the multistation assembly system is modeled as a linear process, where all three aforementioned sources of variability are taken into account at each station using finite element models. In this article we apply the analytical target cascading process to the tolerance allocation problem in multistation assembly systems. Specifically, we translate final product variation targets to tolerance specifications for subassemblies and incoming parts. We demonstrate the methodology by means of a vehicle side frame assembly example.