A Cloud-based Framework for Shop Floor Big Data Management and Elastic Computing Analytics

Advanced digitalization together with the rise of disruptive Internet technologies are key enablers of a fundamental paradigm shift observed in industrial production. This is known as the fourth industrial revolution (Industry 4.0) which proposes the integration of the new generation of ICT solutions for the monitoring, adaptation, simulation, and optimisation of factories. With the democratization of sensors and actuators, factories and machine tools can now be sensorized and the data generated by these devices can be exploited, for instance, to optimise the utilization of the machines as well as their operation and maintenance. However, analyzing the vast amount of generated data is resource demanding both in terms of computing power and network bandwidth, thus requiring highly scalable solutions. This paper presents a novel big data approach and analytics framework for the management and analysis of machine generated data in the cloud. It brings together standard open source technologies and the exploitation of elastic computing, which, as a whole, can be adapted to and deployed on different cloud computing platforms. This enables reducing infrastructure costs, minimizing deployment difficulty and providing on-demand access to a virtually infinite set of computing power, storage and network resources.H202

A Novel Method for Adaptive Control of Manufacturing Equipment in Cloud Environments

The ability to adaptively control manufacturing equipment, both in local and distributed environments, is becoming increasingly more important for many manufacturing companies. One important reason for this is that manufacturing companies are facing increasing levels of changes, variations and uncertainty, caused by both internal and external factors, which can negatively impact their performance. Frequently changing consumer requirements and market demands usually lead to variations in manufacturing quantities, product design and shorter product life-cycles. Variations in manufacturing capability and functionality, such as equipment breakdowns, missing/worn/broken tools and delays, also contribute to a high level of uncertainty. The result is unpredictable manufacturing system performance, with an increased number of unforeseen events occurring in these systems. Events which are difficult for traditional planning and control systems to satisfactorily manage. For manufacturing scenarios such as these, the use of real-time manufacturing information and intelligence is necessary to enable manufacturing activities to be performed according to actual manufacturing conditions and requirements, and not according to a pre-determined process plan. Therefore, there is a need for an event-driven control approach to facilitate adaptive decision-making and dynamic control capabilities. Another reason driving the move for adaptive control of manufacturing equipment is the trend of increasing globalization, which forces manufacturing industry to focus on more cost-effective manufacturing systems and collaboration within global supply chains and manufacturing networks. Cloud Manufacturing is evolving as a new manufacturing paradigm to match this trend, enabling the mutually advantageous sharing of resources, knowledge and information between distributed companies and manufacturing units. One of the crucial objectives for Cloud Manufacturing is the coordinated planning, control and execution of discrete manufacturing operations in collaborative and networked environments. Therefore, there is also a need that such an event-driven control approach supports the control of distributed manufacturing equipment. The aim of this research study is to define and verify a novel and comprehensive method for adaptive control of manufacturing equipment in cloud environments. The presented research follows the Design Science Research methodology. From a review of research literature, problems regarding adaptive manufacturing equipment control have been identified. A control approach, building on a structure of event-driven Manufacturing Feature Function Blocks, supported by an Information Framework, has been formulated. The Function Block structure is constructed to generate real-time control instructions, triggered by events from the manufacturing environment. The Information Framework uses the concept of Ontologies and The Semantic Web to enable description and matching of manufacturing resource capabilities and manufacturing task requests in distributed environments, e.g. within Cloud Manufacturing. The suggested control approach has been designed and instantiated, implemented as prototype systems for both local and distributed manufacturing scenarios, in both real and virtual applications. In these systems, event-driven Assembly Feature Function Blocks for adaptive control of robotic assembly tasks have been used to demonstrate the applicability of the control approach. The utility and performance of these prototype systems have been tested, verified and evaluated for different assembly scenarios. The proposed control approach has many promising characteristics for use within both local and distributed environments, such as cloud environments. The biggest advantage compared to traditional control is that the required control is created at run-time according to actual manufacturing conditions. The biggest obstacle for being applicable to its full extent is manufacturing equipment controlled by proprietary control systems, with native control languages. To take the full advantage of the IEC Function Block control approach, controllers which can interface, interpret and execute these Function Blocks directly, are necessary

Artificial Intelligence as an Enabler of Quick and Effective Production Repurposing Manufactur-ing: An Exploratory Review and Future Research Propositions

The outbreak of Covid-19 created disruptions in manufacturing operations. One of the most serious negative impacts is the shortage of critical medical supplies. Manufacturing firms faced pressure from governments to use their manufacturing capacity to repurpose their production for meeting the critical demand for necessary products. For this purpose, recent advancements in technology and artificial intelligence (AI) could act as response solutions to conquer the threats linked with repurposing manufacturing (RM). The study’s purpose is to investigate the significance of AI in RM through a systematic literature review (SLR). This study gathered around 453 articles from the SCOPUS database in the selected research field. Structural Topic Modeling (STM) was utilized to generate emerging research themes from the selected documents on AI in RM. In addition, to study the research trends in the field of AI in RM, a bibliometric analysis was undertaken using the R-package. The findings of the study showed that there is a vast scope for research in this area as the yearly global production of articles in this field is limited. However, it is an evolving field and many research collaborations were identified. The study proposes a comprehensive research framework and propositions for future research development

Aggregate Cost Model for Scalability in Manufacturing Systems

Manufacturing continues to face escalated cost challenges on a global scale. To gain a competitive advantage among their rivals, manufacturing firms continuously strive to lower their manufacturing costs than their competitors. This dissertation introduces mathematical optimization model based on an Activity-Based Costing (ABC) method, which considers the relationship between hourly rates and annual hours on each machine/workcentre. Several constraints are considered in the proposed models, such as the cost of reconfiguration, capacity, available machining hours, a decision on facility expansion and a cost-benefit analysis on industry 4.0 implementation. The model outputs are the optimum hourly rates, deciding which jobs to accept or reject, and determining reconfiguration\u27s financial feasibility. Reconfiguration in this dissertation describes system-level reconfiguration (investing in additional equipment/machinery) and/or machine-level reconfiguration (extra module to a piece of existing equipment) as well as factory-level (in terms of expanding additional factory segments to the existing facility). The model will be applied to a real-life case study of a global original equipment manufacturer (OEM) of machinery. The mathematical models proposed in this dissertation are developed based on a multinational hydraulic-press manufacturing company. The company owns a local machine shop (one of the sister companies in North America) for building hydraulic presses meant to be delivered to companies producing engineered wood products (such as OSB (oriented Strand Board), PB (Particle Board), and MDF Board (Medium-Density Fibre) …etc.). The sister company in North America occupies a footprint of 5,000 meters squared with a number of capabilities such as machining (turning and machining centres, welding, assembly, material handling…etc.). Several aspects of the model proposed in this dissertation had been implemented in the company such as the bi-directional relationship between total hours and hourly rates which assisted the company in gaining more jobs and projects. In addition, connectivity between strategic suppliers and company branched has been established (enabler of Industry 4.0). The proposed model\u27s novelty incorporates the bi-directional relationship between hourly rates and annual hours in each workcentre. It provides a managerial decision-making tool for the investment level required to pursue new business and gaining a competitive advantage over rivals. Furthermore, a cost-benefit analysis is performed on the implementation of Industry 4.0. The primary aspect considered in industry 4.0 is Information Communication Technology (ICT) infrastructure with strategic suppliers to intensify interconnection between the manufacturing firm and the strategic suppliers. This research\u27s significance is focused on cost analysis and provides managers in manufacturing facilities with the required decision-making tools to decide on orders to accept or decline, as well as investing in additional production equipment, facility expansion, as well as Industry 4.0. In addition, this research will also help manufacturing companies achieve a competitive edge among rivals by reducing hourly rates within their facility. Furthermore, the implementation of the model reduced hourly rates for workcentres by up to 25% as a result of accepting more jobs (and accordingly, machining hours) on the available workcentres, and hence, reducing the hourly rates. This implementation has helped the company gain a competitive advantage among rivals since pricing of products submitted to customer was reduced. Additional benefits and significance are (1) providing manufacturing companies with a method to quantify the decision-making process for right-sizing their manufacturing space, (2) the ability to justify growing a scalable system (machine level, system-level and factory level) using costing (not customer demand), (3) expanding market share and, (4) reducing operational cost and allowing companies a numerical model to justify scaling the manufacturing system

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation

Tool wear classification using time series imaging and deep learning

Abstract: Tool condition monitoring (TCM) has become essential to achieve high-quality machining as well as cost-effective production. Identification of the cutting tool state during machining before it reaches its failure stage is critical. This paper presents a novel big data approach for tool wear classification based on signal imaging and deep learning. By combining these two techniques, the approach is able to work with the raw data directly, avoiding the use of statistical pre-processing or filter methods. This aspect is fundamental when dealing with large amounts of data that hold complex evolving features. The imaging process serves as an encoding procedure of the sensor data, meaning that the original time series can be re-created from the image without loss of information. By using an off-the-shelf deep learning implementation, the manual selection of features is avoided, thus making this novel approach more general and suitable when dealing with large datasets. The experimental results have revealed that deep learning is able to identify intrinsic features of sensory raw data, achieving in some cases a classification accuracy above 90%