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This paper addresses the construction of digital twins (exact mirror images of real-world in cyberspace) using hidden Markov models for the futuristic manufacturing systems known as Industry 4.0. The proposed digital twin consists of two components namely model component and simulation component. The model component forms a Markov chain that encapsulates the dynamics underlying the phenomenon by using some discrete states and their transition probabilities. The simulation component recreates the phenomenon using a Monte Carlo simulation process. The efficacy of the proposed digital twin construction methodology is shown by a case study, where the digital twin of the surface roughness of a surface created by successive grinding operations is described. The developers of the cyber-physical systems will be benefitted from the outcomes of this study because these systems need the computable virtual abstractions of the manufacturing phenomena to address the issues related to the maturity index of futuristic manufacturing systems (i.e., understand, predict, decide, and adopt).

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This article addresses the issue of educating engineering students with the knowledge and skills of Computer-Aided Design and Manufacturing (CAD/CAM). In particular, three carefully designed tutorials-cutting tool offsetting, tool-path generation for freeform surfaces, and the integration of advanced machine tools (e.g., hexapod-based machine tools) with solid modeling-are described. The tutorials help students gain an in-depth understanding of how the CAD/CAM-relevant hardware devices and software packages work in real-life settings. At the same time, the tutorials help students achieve the following educational outcomes: (1) an ability to apply the knowledge of mathematics, science, and engineering; (2) an ability to design a system, component, or process to meet the desired needs, (3) an ability to identify, formulate, and solve engineering problems; and (4) an ability to use the techniques, skills, and modern engineering tools that are necessary for engineering practice. The tutorials can be modified for incorporating other contemporary issues (e.g., additive manufacturing, reverse engineering, and sustainable manufacturing), which can be delved into as a natural extension of this study.

Products made from natural materials are eco-friendly. Therefore, it is important to supply product developers with reliable information regarding the properties of natural materials. In this study, we consider a widely used natural material called jute, which grows in Bangladesh, India, and China. We described the results of tensile tests on jute yarns, as well as the energy absorption patterns leading to yarn failure. We have also used statistical analyses and possibility distributions to quantify the uncertainty associated with the following properties of jute yarn: tensile strength, modulus of elasticity, and strain to failure. The uncertainty and energy absorption patterns of jute yarns were compared with those of jute fibers. We concluded that in order to ensure the reliability and durability of a product made from jute, it is good practice to examine the material properties of yarns rather than those of fibers.