The Cyber Physical Implementation of Cloud Manufactuirng Monitoring Systems

AbstractThe rise of the industrial internet has been envisaged as a key catalyst for creating the intelligent manufacturing plant of the future through enabling open data distribution for cloud manufacturing. The context supporting these systems has been defined by Service Oriented Architectures (SOA) that facilitate data resource and computational functions as services available on a network. SOA has been at the forefront EU research over the past decade and several industrially implemented SOA technologies exist on the manufacturing floor. However it is still unclear whether SOA can meet the multi-layered requirements present within state-of-the-art manufacturing Cyber Physical Systems (CPS). The focus of this research is to identify the capability of SOA to be implemented at different execution layers present in a manufacturing CPS. The state-of-the-art for manufacturing CPS is represented by the ISA-95 standard and is correlated with different temporal analysis scales, and manufacturing computational requirements. Manufacturing computational requirements are identified through a review of open and closed loop machine control orientations, and continuous and discrete control methods. Finally the Acquire Recognise Cluster (ARC) SOA for reconfigurable manufacturing process monitoring systems is reviewed, to provide a topological view of data flow within a field level manufacturing SOA

Integration Framework of MES Toward Data Security Interoperation

© 2020, Springer Nature Switzerland AG. The core problem of the application of MES (Manufacturing Execution System) in intelligent manufacturing systems is integration, which solves the problem of the data interoperation between the distributed manufacturing systems. The previous researches on MES integration rarely considered the problem of system data security access. A three-level data security access mechanism based on the independence of the system administrators, security administrators, and security auditors is proposed which integrated into the MES integration framework to guarantee the business and engineering data security access for the related distributed clients. The principle is using the domain to make the logical isolation for different clients and data sources and applying the pre-defined data sharing rules for safe access. In the proposed MES integration framework model, the data interoperation between MES and the engineering software systems is discussed which includes ERP (Enterprise Resource Management), CAPP (Computer Aided Process Planning), DNC (Distribution Numerical Control), WMS (Warehouse Management System), and SCADA (Supervisory Control and Data Acquisition), etc., the implementation method of personalized data display GUI is discussed as well. The study is based on the KMMES developed by Wuhan KM-Software of China, and it has been deployed in over forty companies from the sections of aerospace, automotive, shipbuilding and other industries

Predicting pharmaceutical particle size distributions using kernel mean embedding

In the pharmaceutical industry, the transition to continuous manufacturing of solid dosage forms is adopted by more and more companies. For these continuous processes, high-quality process models are needed. In pharmaceutical wet granulation, a unit operation in the ConsiGmaTM-25 continuous powder-to-tablet system (GEA Pharma systems, Collette, Wommelgem, Belgium), the product under study presents itself as a collection of particles that differ in shape and size. The measurement of this collection results in a particle size distribution. However, the theoretical basis to describe the physical phenomena leading to changes in this particle size distribution is lacking. It is essential to understand how the particle size distribution changes as a function of the unit operation's process settings, as it has a profound effect on the behavior of the fluid bed dryer. Therefore, we suggest a data-driven modeling framework that links the machine settings of the wet granulation unit operation and the output distribution of granules. We do this without making any assumptions on the nature of the distributions under study. A simulation of the granule size distribution could act as a soft sensor when in-line measurements are challenging to perform. The method of this work is a two-step procedure: first, the measured distributions are transformed into a high-dimensional feature space, where the relation between the machine settings and the distributions can be learnt. Second, the inverse transformation is performed, allowing an interpretation of the results in the original measurement space. Further, a comparison is made with previous work, which employs a more mechanistic framework for describing the granules. A reliable prediction of the granule size is vital in the assurance of quality in the production line, and is needed in the assessment of upstream (feeding) and downstream (drying, milling, and tableting) issues. Now that a validated data-driven framework for predicting pharmaceutical particle size distributions is available, it can be applied in settings such as model-based experimental design and, due to its fast computation, there is potential in real-time model predictive control

A flexible architecture for manufacturing planning software maintenance

Computer software systems took on a new role in manufacturing planning with the introduction of Material Requirement Planning (MRP) system in 1965. The MRP system generates material requirement lists in response to given production requirements. In this way, inventory management, purchasing, and shipping activities are linked to manufacturing. In 1979, Manufacturing Resource Planning (MRP II) systems were introduced [VerDuin 1995]. MRP II typically includes planning applications, customer order entry, finished goods inventory, forecasting, sales analysis, production control, purchasing, inventory control, product data management, cost accounting, general ledger processing, payables, receivables, and payroll [Turbide 1995]. An emerging market is developing for software systems that expand the scope of\u27MRP II farther to encompass activities for the entire organization. Among these systems are Enterprise Resource Planning (ERP), Customer-Oriented Manufacturing Management System (COMMS), and Manufacturing Execution Systems (MES). These systems integrate marketing, manufacturing, sales, finance, and distribution to move beyond optimizing production alone, to optimizing the organization\u27s multiple objectives of low cost, rapid delivery, high quality, and customer satisfaction [VerDuin 1995]. MRP II is still the dominant solution for manufacturing in tens of thousands of companies. These companies range in size from less than a million dollars in sales right up to the top of Fortune 500 companies. However, this is a market penetration of only 11% which clearly shows the size and potential of the opportunity for MRP II development. Yet, despite the commonality of needs across the scope of manufacturing, there are distinct differences when comparing plant to plant, company to company, and industry to industry. Often MRP II has to be modified to adapt to a particular industry [Turbide 1993]. This modification often pushes the cost even higher and makes MRP II more out-of-reach for many companies. Therefore, it would be highly beneficial for the overall scope of manufacturing if a highly flexible low-cost MRP II system can be developed. This research presents a flexible architecture for development and maintenance of manufacturing planning software, especially MRP II. The architecture uses the concept of software reuse and is built on top of run-time object-oriented framework

MFRL-BI: Design of a Model-free Reinforcement Learning Process Control Scheme by Using Bayesian Inference

Design of process control scheme is critical for quality assurance to reduce variations in manufacturing systems. Taking semiconductor manufacturing as an example, extensive literature focuses on control optimization based on certain process models (usually linear models), which are obtained by experiments before a manufacturing process starts. However, in real applications, pre-defined models may not be accurate, especially for a complex manufacturing system. To tackle model inaccuracy, we propose a model-free reinforcement learning (MFRL) approach to conduct experiments and optimize control simultaneously according to real-time data. Specifically, we design a novel MFRL control scheme by updating the distribution of disturbances using Bayesian inference to reduce their large variations during manufacturing processes. As a result, the proposed MFRL controller is demonstrated to perform well in a nonlinear chemical mechanical planarization (CMP) process when the process model is unknown. Theoretical properties are also guaranteed when disturbances are additive. The numerical studies also demonstrate the effectiveness and efficiency of our methodology.Comment: 31 pages, 7 figures, and 3 table

Architectur System Design Logistik Management PT. Sinjaraga Santika Sport

Distribution channel is a channel marketing intermediaries both transport and storage of goods and services from producer to consumer hands. So the main function of the distribution channel is to deliver the goods / products from manufactures kekonsumen, the company in implementing and determining the distribution channels should do good judgment, so that the production cost is comparable with the business profits. The process of software development is divided into several stages, where each stage is performed several stages of work done repeatedly. The research process is done by designing information systems architecture proposed by PT. Sinjaraga Santika Sport then this system has the ability to monitor the existing raw material warehouse. Information systems PT. Sinjaraga Santika Sport is one of the utilization of technology that uses an application that is used for the data pengeleloaan materials distribution company. Architectural Design Process System is intended to help any part in managing goods data, sales data and preparing reports. Architectural Design Method of Information System used is the method Supply Chain Management where this method is used to emphasize the integrated pattern concerning the flow of products from suppliers, manufacturing, distributors, retailers to consumers

Smart manufacturing for industry 4.0 using Radio Frequency Identification (RFID) technology

Industry 4.0 (I4.0) presents a unique challenge of efficiently transforming traditional manufacturing to smart and autonomous systems.Integrating manufacturing systems, materials, machinery, operators, products and consumers, improve interconnectivity and traceability across the entire product life cycle in order to ensure the horizontal and vertical integration of networked Smart Manufacturing (SM) systems. Manufacturing functions of Material Handling (MH)-control, storage, protection and transport of raw materials, work in process (WIP) and finished products- throughout a manufacturing and distribution process will need a revamp in ways they are currently being carried in order to transition them into the SM era. Radio Frequency Identification (RFID), an Automated Identification Data Capture (AIDC) technology increasingly being used to enhance MH functions in the (SM) industry, due to opportunities it presents for item tracking, out of sight data capturing, navigation and space mapping abilities. The technology readiness level of RFID has presented many implementation challenges as progress is being made to fully integrate the technology into the preexisting MH functions. Recently, many researchers in academia and industry have described various methods of using RFID for improving and efficiently carrying out MH functions as a gradual transition is being made into I4.0 era. This paper reviews and categorize research finding regarding RFID application developments according to various MH functions in SM, tabulates how various I4.0 enablers are needed to transform various traditional manufacturing functions into SM. It aims to let more experts know the current research status of RFID technology and provide some guidance for future research

Industry 4.0 technologies for the manufacturing and distribution of COVID-19 vaccines

Background: The evolutionary stages of manufacturing have led us to conceptualize the use of Industry 4.0 for COVID-19 (coronavirus disease 2019), powered by Industry 4.0 technologies. Using applications of integrated process optimizations reliant on digitized data, we propose novel intelligent networks along the vaccine value chain. Vaccine 4.0 may enable maintenance processes, streamline logistics, and enable optimal production of COVID-19 vaccines.Vaccine 4.0 framework: The challenge in applying Vaccine 4.0 includes the requirement of large-scale technologies for digitally transforming manufacturing, producing, rolling-out, and distributing vaccines. With our framework, Vaccine 4.0 analytics will target process performance, process development, process stability, compliance, quality assessment, and optimized maintenance. The benefits of digitization during and post the COVID-19 pandemic include first, the continual assurance of process control, and second, the efficacy of big-data analytics in streamlining set parameter limits. Digitization including big data-analytics may potentially improve the quality of large-scale vaccine production, profitability, and manufacturing processes. The path to Vaccine 4.0 will enhance vaccine quality, improve efficacy, and compliance with data-regulated requirements.Discussion: Fiscal and logistical barriers are prevalent across resource-limited countries worldwide. The Vaccine 4.0 framework accounts for expected barriers of manufacturing and equitably distributing COVID-19 vaccines. With amalgamating big data analytics and biometrics, we enable the identification of vulnerable populations who are at higher risk of disease transmission. Artificial intelligence powered sensors and robotics support thermostable vaccine distribution in limited capacity regions, globally. Biosensors isolate COVID-19 vaccinations with low or limited efficacy. Finally, Vaccine 4.0 blockchain systems address low- and middle-income countries with limited distribution capacities.Conclusion: Vaccine 4.0 is a viable framework to optimize manufacturing of vaccines during and post the COVID-19 pandemic