As technology advances, the need for methods to monitor processes that produce readily-available inspection data becomes essential. In this paper, a multinomial cumulative sum (CUSUM) chart is proposed to monitor in situations where items can be classified into more than two categories, the items are not put into subgroups, and the direction of the out-of-control shift in the parameter vector can be specified. It is shown through examples that the multinomial CUSUM chart can detect shifts in category probabilities at least as quickly and, in most cases, faster than using multiple Bernoulli CUSUM charts. The properties of the multinomial CUSUM chart are determined through a Markov chain representation. If the direction of the out-of-control shift in the parameter vector cannot be specified, we recommend the use of multiple Bernoulli CUSUM charts.

The rampant increase in frequency and complexity of cyber-attacks against manufacturing firms, has motivated the development of identification and mitigation techniques for cyber-physical vulnerabilities in manufacturing. While the field of cybersecurity assessment approaches is expansive, there is no literature aimed at assessing cyber-physical vulnerabilities for manufacturing systems. In response, this paper provides a framework for systematically identifying cyber-physical vulnerabilities in manufacturing systems. The proposed approach employs intersection mapping to identify cyber-physical vulnerabilities in manufacturing. A cyber-physical vulnerability impact analysis using decision trees then provides the manufacturer with a stoplight scale between low, medium, and high levels of cyber-physical vulnerability for each analyzed production process. The stoplight scale allows manufacturers to interpret assessment results in an intuitive way. Finally, the paper provides a case study of the proposed approach at an applied manufacturing research facility and provides general recommendations to securing similar facilities from cyber-physical attacks.

State-of-the-art measurement technologies, such as 3D laser scanners, provide new opportunities for knowledge discovery and development of quality control (QC) strategies for complex manufacturing systems. These technologies can rapidly provide millions of data points to represent a manufactured part's surface. The resulting high-density (HD) datasets have a great potential to be used for inspecting parts for surface and feature abnormalities. The current use of these datasets for part inspection can be divided into two main categories: (1) extracting feature parameters, which does not complement the nature of these datasets as it wastes valuable data and (2) an ad hoc inspection process, where a visual representation of the data is manually analyzed, which tends to suffer from slow, inefficient, and inconsistent inspection results. To overcome these deficiencies, this paper proposes an adaptive generalized likelihood ratio (AGLR) technique to automate the surface defect inspection process using HD data. This paper presents the performance results of the proposed AGLR approach with respect to the probability of detecting varying size and magnitude defects in addition to the probability of false alarms. In addition, a formal approach for designing an optimal AGLR inspection system is proposed. Finally, simulation results are presented and analyzed to showcase the performance gains of the AGLR approach versus a more traditional generalized likelihood ratio (GLR) approach.

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.