Praveen Kumare Gopalakrishnan

With the modern age of using genetically engineered products and growing concerns about food recalls and outbreaks, businesses are looking for ways to secure their brand names and assuring consumers about food safety and quality. Recently, Blockchain has been introduced as a promising approach for increasing the visibility of the supply chain and reducing the sale of contaminated and counterfeit products. Along this line, this study discusses the capabilities of Blockchain for the collection and monitoring of product lifecycle information ranging from production, wholesale, and logistics to standards, business reputation, and certification. The particular focus of the study is to discuss the use of videogrammetry as a data collection mechanism for bringing the product lifecycle data on digital Blockchain platforms and solving the “last mile” problem and data verification issue on Blockchain platforms. A conceptual example of organic meat processing is discussed to describe the proposed procedure and show how videogrammetry in combination with RFID and fingerprints can be used to solve the data verification issue on Blockchain platforms.

The objective of this study is to develop a mathematical framework for determining the minimum number of parts required in a product to satisfy a list of functional requirements (FRs) given a set of connections between FRs. The problem is modeled as a graph coloring technique in which a graph G with n nodes (representing the FRs) and m edges (representing the connections between the FRs) is studied to determine the graph’s chromatic number χ(G), which is the minimum number of colors required to properly color the graph. The chromatic number of the graph represents the minimum number of parts needed to satisfy the list of FRs. In addition, the study calculates the computational efficiency of the proposed algorithm. Several examples are provided to show the application of the proposed algorithm.

As the concept of the visible product lifecycle and product tracking becomes important to consumers, emerging technologies such as Internet of Things (IoT)-based networks and Blockchain platforms are considered promising solutions by Original Equipment Manufacturers (OEMs) to increase their brand reputation, improve their consumers’ experiences, and reduce their operational costs. However, costly implementation, data verification issues, and the risk of uploading inaccurate information on those platforms have limited the adoption rate and capabilities offered by the aforementioned technologies. Companies often face the question of what type of distributed ledger technology and verification protocol is more efficient in terms of operational costs as well as security. The purpose of this study is to develop an optimization framework to make a trade-off between the cost of implementation of the technology and the security level. The framework is developed based on utility theory principles. The main focus is to discuss the cost associated with various aspects of technology implementation. Finally, this general cost framework is shown as a demo for an example of the food supply chain.

This study proposes a graph partitioning method to facilitate the idea of physical integration proposed in Axiomatic Design. According to the physical integration concept, the design features should be integrated into a single physical part or a few parts with the aim of reducing the information content, given that the independence of functional requirements is still satisfied. However, no specific method is suggested in the literature for determining the optimal degree of physical integration of a design artifact. This is particularly important with the current advancement in Additive Manufacturing technologies. Since additive manufacturing allows physical elements to be integrated, new methods are needed to help designers evaluate the impact of the physical integration on the design success. The objective of this paper is to develop a framework for determining the best way that functional requirements can be assigned to different parts of a product.

Data collected during product lifecycle phases ranging from the beginning of Life (BOL), to the middle of life (MOL) and the end of Life (EOL) assist designers to make informed decisions in various operational and strategic levels. The specific focus of this paper is to show that the data collected during the usage phase of Hard Disk Drives (HDDs) can be used to identify the failure rates of HDDs. Two sources of data were applied: quantitative data extracted from Self-Monitoring Analysis and Reporting Technology (SMART) software, and qualitative data collected from consumer reviews. The data were analyzed to predict the most reliable HDDS available in the market. Some of the important factors to be considered during manufacturing of an HDD are suggested based on the results obtained.

Gearbox plays a vital role in various fields in the industries. Failure of any component in the gearbox will lead to machine downtime. Vibration monitoring is the technique used for condition based maintenance of gearbox. This paper discusses the use of machine learning techniques for automating the fault diagnosis of automobile gearbox. Our experimental study monitors the vibration signals of actual automobile gearbox with simulated fault conditions in the gear and bearing. Statistical features are extracted and classified for identifying the faults using decision tree and Naïve bayes technique. Comparison of the techniques for determining the classification accuracy is discussed.