Optimal Configuration of Inspection and Rework Stations in a Multistage Flexible Flowline

Inspection and rework are two important issues of quality control. In this research, an N-stage flowline is considered to make decisions on these two issues. When defective items are detected at the inspection station the items are either scrapped or reworked. A reworkable item may be repaired at the regular defect-creating workstation or at a dedicated off-line rework station. Two problems (end-of-line and multistage inspections) are considered here to deal with this situation. The end-of-line inspection (ELI) problem considers an inspection station located at the end of the line while the multistage inspection (MSI) problem deals with multiple in-line inspection stations that partition the flowline into multiple flexible lines. Models for unit cost of production are developed for both problems. The ELI problem is formulated for determining the best decision among alternative policies for dealing with defective items. For an MSI problem a unit cost function is developed for determining the number and locations of in-line inspection stations along with the alternative decisions on each type of defects. Both of the problems are formulated as fractional mixed-integer nonlinear programming (f-MINLP) to minimize the unit cost of production. After several transformations the f-MINLP becomes a mixed-integer linear programming (MILP) problem. A construction heuristic, coined as Inspection Station Assignment (ISA) heuristic is developed to determine a sub-optimal location of inspection and rework stations in order to achieve minimum unit cost of production. A hybrid of Ant-Colony Optimization-based metaheuristic (ACOR) and ISA is devised to efficiently solve large instances of MSI problems. Numerical examples are presented to show the solution procedure of ELI problems with branch and bound (B&B) method. Empirical studies on a production line with large number of workstations are presented to show the quality and efficiency of the solution processes involved in both ELI and MSI problems. Computational results present that the hybrid heuristic ISA+ACOR shows better performance in terms of solution quality and efficiency. These approaches are applicable to many discrete product manufacturing systems including garments industry

Throughput and Yield Improvement for a Continuous Discrete-Product Manufacturing System

A seam-welded steel pipe manufacturing process has mainly four distinct major design and/or operational problems dealing with buffer inventory, cutting tools, pipe sizing and inspection-rework facility. The general objective of this research is to optimally solve these four important problems to improve the throughput and yield of the system at a minimum cost. The first problem of this research finds the optimal buffer capacity of steel strip coils to minimize the maintenance and downtime related costs. The total cost function for this coil feeding system is formulated as a constrained non-linear programming (NLP) problem which is solved with a search algorithm. The second problem aims at finding the optimal tool magazine reload timing, magazine size and the order quantity for the cutting tools. This tool magazine system is formulated as a mixed-integer NLP problem which is solved for minimizing the total cost. The third problem deals with different type of manufacturing defects. The profit function of this problem forms a binary integer NLP problem which involves multiple integrals with several exponential and discrete functions. An exhaustive search method is employed to find the optimum strategy for dealing with the defects and pipe sizing. The fourth problem pertains to the number of servers and floor space allocations for the off-line inspection-rework facility. The total cost function forms an integer NLP structure, which is minimized with a customized search algorithm. In order to judge the impact of the above-mentioned problems, an overall equipment effectiveness (OEE) measure, coined as monetary loss based regression (MLBR) method, is also developed as the fifth problem to assess the performance of the entire manufacturing system. Finally, a numerical simulation of the entire process is conducted to illustrate the applications of the optimum parameters setting and to evaluate the overall effectiveness of the simulated system. The successful improvement of the simulated system supports this research to be implemented in a real manufacturing setup. Different pathways shown here for improving the throughput and yield of industrial systems reflect not only to the improvement of methodologies and techniques but also to the advancement of new technology and national economy

Analyzing Robotics Software Vulnerabilities

Robots are widely used in our day-to-day life in various domains. For example, eldercare robots, such as CareO-Bots [1]are used to perform household tasks and provide mobility assistance [2]. Amazon uses manufacturing robots to accomplish manufacturing labor activities, such as welding and assembling equipment [2]. According to the International Data Corporation, spending on robotics is expected to reach USD 241.4 billion by the end of 2023 [4]. However, malicious users can exploit security vulnerabilities in hardware and software components of robotics systems to conduct security attacks and cause malfunction, i.e., deviate robots from their expected behaviors. Security attacks on robots can have serious consequences such as (i) bottlenecks and shutdowns in the assembly line, (ii) disruption in the food supply chain, (iii) incorrect treatment for patients, and (iv) unwanted military attacks injuring or killing civilians and military personnel [2]. Researchers [3] have observed a lack of awareness amongst practitioners related to security issues that can exist in robotics systems. Using qualitative analysis, the project aims to determine the software vulnerabilities that commonly appear in robotics systems. In this work in progress, we plan to discuss our initial findings using Robotics Vulnerability Database (RVD) repositories [5] the following questions – (i) what are the most frequent security vulnerabilities in robotics systems? (ii) what types of components are affected by the vulnerabilities? (iii) what categories of vulnerabilities exist and severity for robotics systems

Classification of Image using Convolutional Neural Network (CNN)

Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images. We have used Convolutional Neural Networks (CNN) in automatic image classification systems. In most cases, we utilize the features from the top layer of the CNN for classification; however, those features may not contain enough useful information to predict an image correctly. In some cases, features from the lower layer carry more discriminative power than those from the top. Therefore, applying features from a specific layer only to classification seems to be a process that does not utilize learned CNN2019;s potential discriminant power to its full extent. Because of this property we are in need of fusion of features from multiple layers. We want to create a model with multiple layers that will be able to recognize and classify the images. We want to complete our model by using the concepts of Convolutional Neural Network and CIFAR-10 dataset. Moreover, we will show how MatConvNet can be used to implement our model with CPU training as well as less training time. The objective of our work is to learn and practically apply the concepts of Convolutional Neural Network

Machine Learning-Oriented Predictive Maintenance (PdM) Framework for Autonomous Vehicles (AVs): Adopting Blockchain for PdM Solution

Autonomous Vehicles (AVs) refers to smart, connected and multimedia cars with technological megatrends of the fourth industrial revolution (Industry 4.0) and have gained huge strive in today\u27s world. AVs adopt automated driving systems (ADS) technique that permits the vehicle to manage and control driving points without human drivers by utilizing advanced equipment including a combination of sensors, controllers, onboard computers, actuators, algorithms, and advanced software embedded in the different parts of the vehicle. These advanced sensors provide unique inputs to the ADS to generate a path from point A to point B. Ensuring the safety of sensors by limiting maintenance costs has become a major challenge for AVs community. The predictive maintenance (PdM) approach has the potential to address the AVs failures. In this paper, we propose a novel, conceptual, and high-level domain-specific software architecture for the machine learning-oriented predictive maintenance (PdM) framework that shall enable predicting early malfunctioning, quality, safety, and performance deficiencies of AVs. The novel framework collects the data from sensors and major equipment and stores the collected data in immutable and transparent blockchain technology. Collected data shall be validated, extracted, and classified by adopting machine learning (ML) techniques. ML module shall predict the possible malfunctioning of the sensors while providing potential solutions from the stored data in the blockchain network. In this paper, our effort was to conduct a feasibility study, elicit and specify all the requirements for the proposed framework. In future research, we aim to extend the conceptual work and implement a prototype in real-world scenarios

Students Certification Management (SCM): Hyperledger Fabric-Based Digital Repository

The higher education sector has been heavily impacted financially by the economic downturn caused by the pandemic that has resulted a decline in student enrollments. Finding cost-effective novel technology for storing and sharing student\u27s credentials among academic institutions and potential employers is a demand. Within the current conventional approach, ensuring authentication of a candidate’s credentials is costly and time-consuming which gives burdens to thousands of prospective students and potential employees. As a result, candidates fail to secure opportunities for either delay or non-submission of credentials all over the world. Blockchain technology has the potential for students\u27 control over their credentials; degrees and transcripts for instance that will allow seamless streamlining of the sharing of educational records during changing and transferring schools, higher education, or even employment processes when need to show credentials. To implement the novel idea, we conduct a preliminary survey, study the existing applications, and investigate the feasibility of a Blockchain-based system to exploit the potential. Based on our findings, we propose a Students Certification Management System (SCM) by adopting Emerging Hyperledger Fabric that will offer a universal, tamper-evident, immutable, and secure educational certificate storing and sharing network. Our primary aim is to construct the proposed system into an educational certificate repository network using consortium blockchain for different entities including, (i) educational institutes to manage the network (ii) students and authorized third parties to access verifiable digital certificates and transcripts. Initially, we introduce an advanced architectural framework of the proposed system that has the potential in improving data flow between academic institutions, students, and potential employers. For ensuring transparency, each attempt in storing, sharing, and accessing credentials by the authenticated users within the proposed network shall be stored in the ledger which is secure and non-corruptible. Our future direction is to implement the architectural framework into an educational certification repository network within a private blockchain network

An Exploratory Analysis of Mobile Security Tools

The growing market of the mobile application is overtaking the web application. Mobile application development environment is open source, which attracts new inexperienced developers to gain hands on experience with applicationn development. However, the security of data and vulnerable coding practice is an issue. Among all mobile Operating systems such as, iOS (by Apple), Android (by Google) and Blackberry (RIM), Android dominates the market. The majority of malicious mobile attacks take advantage of vulnerabilities in mobile applications, such as sensitive data leakage via the inadvertent or side channel, unsecured sensitive data storage, data transition and many others. Most of these vulnerabilities can be detected during mobile application analysis phase. In this paper, we explore vulnerability detection for static and dynamic analysis tools. We also suggest limitations of the tools and future directions such as the development of new plugins

Effects on natural frequency of a plate due to distributed and positional concentrated mass

The concentrated masses attached rigidly to the plate due to different kinds of loading for the installation of the machineries on that plate may play a vital role to shift the bare plate natural frequencies. This paper presents the effect of natural frequencies when the amount of concentrated masses, the position of the concentrated masses and the distribution of a specific amount of concentrated masses into different positions, which is practically possible when installing the machineries, are introduced. The investigations have been done numerically through ANSYS and also carried out to observe the change of mode shapes due to the concentrated masses. A typical single concentrated mass applied at the middle of the plate is validated by the analytical approach found in the literature

Emotional Analysis of Learning Cybersecurity with Games using IoT

The constant rise of cyber-attacks poses an increasing demand for more qualified people with cybersecurity knowledge. Games have emerged as a well-fitted technology to engage users in learning processes. In this paper, we analyze the emotional parameters of people while learning cybersecurity through computer games. The data are gathered using a non-invasive Brain-Computer Interface (BCI) to study the signals directly from the users’ brains. We analyze six performance metrics (engagement, focus, excitement, stress, relaxation, and interest) of 12 users while playing computer games to measure the effectiveness of the games to attract the attention of the participants. Results show participants were more engaged with parts of the games that are more interactive instead of those that present text to read and type