A Smart Manufacturing Execution System

Quality monitoring in infrastructural design-build projects : the analysis of an audit-based monitoring system

Modern data loggers record and store large amounts of data from many different types of sensors. This enables their use in various applications to capture different measurands. However wireless access to the recorded data opens countless possibilities for novel and innovative applications, for example in the health monitoring of humans or livestock. In many of these applications a central device connects simultaneously to several data loggers. This paper discusses the experiences made during the design, implementation and test of an energy efficient wireless interface to a data logger. The implemented system serves as an application example for Bluetooth Smart. It allows a user to monitor ongoing measurements of several data loggers through a smartphone. Additionally the recorded data of several loggers can be simultaneously uploaded to a central gateway. The available Bluetooth Smart chips and software stacks place limitations on usable bandwidth as well as on the number of simultaneous connections. Although most modern smartphones include hardware for Bluetooth Smart, implementations differ widely across platforms, thus creating additional effort for the app programmer. This paper presents measurement results and introduces the implemented approaches to optimize these parameters.

Manufacturing Execution Systems (MES) optimize production and business processes at the same time. However, the engineering and specification of MES is a challenging, interdisciplinary process. Especially IT and production experts with different views and background have to cooperate. For successful and efficient MES software projects, misunderstandings in the specification process have to be avoided. Therefore, textual specifications need to be complemented by unambiguous graphical models, reducing the complexity by integrating interdisciplinary views and domain specific terms based on different background knowledge. Today's modeling notations focus on the detailed modeling of a certain domain specific problem area. They do not support interdisciplinary discussion adequately. To bridge this gap a novel MES Modeling Language (MES-ML) integrating all necessary views important for MES and pointing out their interdependencies has been developed. Due to its formal basis, comparable and consistent MES-models can be created for specification, standardization, testing, and documentation of MES software. In this paper, the authors present the formal basis of the modeling language and its core notation. The application of MES-ML is demonstrated taking a yogurt production as an example. Finally, the authors give some evaluation results that underline the effectiveness and efficiency of this new modeling approach with reference to four applications in industrial MES-projects in the domain of discrete and hybrid manufacturing.

Within the framework of Industry 4.0, the transition from stand-alone systems to an interconnected digital enterprise plays a crucial role. Increasing demands for flexibility, quality, and shorter product development cycles drive the adoption of advanced digital solutions such as product data management (PDM) systems, manufacturing execution systems (MES), advanced planning and scheduling (APS) tools, and the digital twin (DT) concept. At the same time, digitalisation trends (e.g., Smart Factory) reveal critical weaknesses of manufacturing companies, with insufficient planning and scheduling capabilities being one of the most common issues. While medium- and long-term planning is typically handled within ERP systems, operational scheduling is often still managed through inflexible spreadsheet-based solutions. APS systems are therefore gaining importance as effective tools with relatively fast return on investment, although their deployment requires alignment between ERP and MES layers as well as a thorough understanding of business and production processes. The combined implementation of PDM, APS, MES, and DT enhances decision-making, enables rapid adaptability to change, and supports the development of learning organisations. The article focuses on key principles and technologies of digital transformation with emphasis on data integration using selected tools.

Manufacturing is facing difficulty in evolving and meeting reconfigurability needs because of the prevalence of legacy systems that are heterogeneous and inflexible. This PhD project addresses that challenge by developing design principles that are relevant to the real-world industrial context for manufacturing operations management (MOM). The core system of MOM is manufacturing execution system (MES), which is a factory information system, and its principles aim to enable a smart factory. The smart factory is an information technology (IT)-driven enabler for meeting future manufacturing requirements, such as reconfigurability. It has the capacity to solve the customer responsiveness problem by reducing the time to market and supporting product variety.

Violation of the technical condition of the transmission and running systems leads to operational and economic costs, violation of the technical indicators of mobile power tools laid down by the manufacturer. It also has a negative impact on the environment. At present, only a few domestic and foreign companies have equipped the equipment they produce with digital systems for monitoring the technical condition of power transmissions and running systems, and even then by one or several parameters. These are systems that measure the level of wheel slippage of the tractor, the temperature and pressure in the tires, the angle of inclination of the tractor, as well as the systems that control the optimality of the conversion of engine power to tractive force. The main mass of tractors has no control system during operation and diagnostics of components and assemblies of the transmission and chassis systems is carried out by the tractor operator «by eye», based on his experience and fairness. This often leads to serious violations and equipment operates in modes and loads close to failure. Therefore, it is necessary to introduce into the design of modern technology integrated system for monitoring the state of power transmission and running systems, based on the use of digital technologies. As an experiment, a prototype model of such a control system was developed, including various sensors, both embedded and external, the hardware of the Arduino microcontroller platform, as well as widely available applications for the smartphone, allowing to measure and display on the screen the necessary information about the current state of the units and components. The smartphone is supposed to be mounted in the cab of the tractor. The prototype was tested and can be considered successful. Therefore, in the future it is planned to expand its functionality and add elements.

Characteristic of Auto electronic parts production industry are introduced at first. And with the background of the practical demands of Auto electronic parts production industry, with model of Browser/Server (B/S) and program as Microsoft.NET, Visual C++, SQLSever2000 was introduced. Meanwhile, this architecture of Manufacturing Execution System (MES) design theory and database structural charts of the whole system are presented. Information integration and production scheduling optimization function of MES implement information fusion and combination between PCS and ERP, realization the optimization control and management of the Auto electronic parts enterprise production process, which improves enterprise's synthesis competition ability. The two layers integrated automation system production indexes and scheduling as the design core realization optimization of production scheduling indexes. The MES that adopting this method is achieved the integration of management and control. This system has been applied to the Auto electronic parts plant and the production efficiency is increased. The successful implementation of this system has explored a new method for the overall optimization of the large systems in discrete manufacturing enterprise.

In view of the production quality management of filter rods in the manufacturing and execution process of cigarette enterprises, this paper analyzes the necessity of implementing the manufacturing execution system (MES) in the production process of filter rods. In this paper, the filter rod quality system of cigarette enterprise based on MES is fully studied, and the constructive information management system demand analysis, cigarette quality control process, system function module design, implementation and test effect are given. This paper utilizes the Fuzzy analytic hierarchy process to find the optimal system for processing the manufacturing of cigarette. The implementation of MSE based filter rod quality information management system for a cigarette enterprise ensures the quality control in the cigarette production process. Through visualization, real-time and dynamic way, the information management of cigarette production is completed, which greatly improves the quality of cigarette enterprise manufacturing process.

This paper briefly discusses the ALICE machine background concerns and the background monitoring system. ALICE RUNNING STRATEGY ALICE (A Large Ion Collider Experiment) [1] is a general purpose detector designed to address the physics of strongly interacting matter and the quark-gluon plasma in nucleus-nucleus collisions at the LHC. It will allow a comprehensive study of particles produced in Pb–Pb collisions, up to the highest multiplicities anticipated at the LHC. The physics program also includes collisions with lighter ions as well as dedicated proton-nucleus runs. Regular data taking during pp runs will provide reference data for the heavy ion program and address a number of specific pp topics. The pp runs will be in parallel with the other experiments but at a reduced luminosity in IP2. In order to keep the pile-up in the Time Projection Chamber (TPC) and Silicon Drift Detectors (SDD) at an acceptable level, the luminosity during pp runs has to be limited to 3 × 10 cm−2s−1, corresponding to an interaction rate of 200 kHz. At this rate we record on average 20 overlapping events. The optimal detector operation and physics performance with the TPC, i.e. no pile-up, is at 10 cm−2s−1. IMPACT OF MACHINE BACKGROUND General considerations Due to the running at reduced luminosity ALICE has the most unfavorable interaction rate over background rate ratio (at least a factor of 10 less than the high luminosity experiments). Machine background effects are alleviated by the fact that ALICE has been designed to perform tracking for up to 1000 times the pp multiplicity and the trigger reduction factors are relatively small (typically 10). So far the expected effects of the background are mainly of cumulative nature, such as radiation damage (integral dose and neutron fluences). Also the increase of the data volume has obvious negative consequences in terms of data storage and offline computing requirements. To simulate these effects ALICE has so far considered beam gas events in the experimental region IP ±20 m and beam-halo from beam-gas scattering outside the experimental regions. Input for the quartiary background caused by tertiary collimators (TCT) close to the experimental region is not yet available for IP2. In case the collimators are at the nominal settings this contribution could well be the dominant source of machine background. However, since at full beam intensity ALICE will run at high β∗ (10 m), the inner triplet will not limit the aperture of the machine. ALICE requires that for stable beams the TCTs will be put at a position at which they protect the inner triplets against accidental losses but do not produce extra losses for stable beams. Dose in central detectors The radiation environment in the experimental cavern has been simulated for the planned running scenario of the ALICE experiment (Table 1) [2]. Running with p-p, low and high mass ion–ion collisions over a ten year period has been assumed. Beam-beam and beam-gas interactions have been considered as potential radiation sources. The highest doses, up to 2.8 kGy, are expected at the location of the inner tracking system (ITS) (Table 2). The contribution from beam halo [3] amounts to ≈ 20% of the total dose. The contribution from beam-gas collisions within the experimental region has been calculated assuming a very conservative residual gas pressure of 2× 10 molecules/m. Only under these conditions a sizeable contribution of about 10% of the total dose is expected. Charged particle rates on RPCs Among the ALICE detectors, the muon trigger system is one of the most sensitive to the machine induced background. As a matter of fact, the Resistive Plate Chambers (RPC) rate capability (50 − 100 Hz/cm) might be saturated by a too high background level, which might also have an impact on the detector lifetime. The fluxes of secondary charged particles through muon trigger system originating from machine induced background has been simulated [4]. The trigger background consists mainly of electrons from hadronic showers resulting in a hot spot of ≈ 60 Hz/cm located at x = 1.5 m and |y| < 1.5 m. BACKGROUND MONITORING For machine background monitoring during injection ALICE will use the beam condition monitor (BCM) and the V0 forward scintillator detectors at safe photomultiplier settings. Due to the different acceptance of the two detectors an OR of the two signals will be used. With circulating stable beams a combination of signals from BCM, V0, SPD, TPC and forward muon spectrometer will be used to obtain a normalized machine background signal. Beam condition monitors The purpose of the Beam Condition Monitor (BCM) is to detect adverse beam conditions within the ALICE experLHC Workshop on Experimental Conditions and Beam-Induced Detector Backgrounds

The discharge of industrial wastewater into surface water requires constant monitoring of the water quality to comply with specifications and quality requirements. Furthermore, it must be ensured that individual approvals for the production plants are obtained when industrial wastewater is discharged into the wastewater treatment plant (WWTP). For this reason, the influent of the WWTP is continuously monitored for trace organic compounds (TrOCs). These compounds are usually determined using liquid (LC) or gas chromatographic (GC) target methods coupled to low resolution mass spectrometers. However, these methods monitor only a small part of TrOCs in wastewater. Only a limited number of compounds can be detected in a single run and many compounds are ignored in the analysis as they are not part of the target list. Thus, unknown TrOCs neither can be detected nor identified in wastewater samples using these methods, even if they are present in high concentrations. Therefore, high resolution mass spectrometers (HRMS) have become more and more common in water analysis to carry out more extensive monitoring by detecting both known and unknown compounds. Besides, in combination with a non target screening (NTS), the generated HRMS data additionally enable the identification of unknown compounds. The application of LC HRMS in NTS related to industrial wastewater data is described in this work. Sample treatment procedures and an analytical LC HRMS method are developed which enable the sensitive and reliable monitoring of TrOCs in a broad polarity range. Additionally, the development of a reliable data processing algorithm for NTS is part of this work. A large amount of data is produced in LC HRMS that cannot completely be evaluated. Thus, prioritisation methods are required, enabling data reduction. As a result, three prioritisation strategies were developed, which make it possible to extract relevant features (a combination of a particular mass to charge ratio, the associated retention time and intensity) from the data for identification. The relevance of each feature depended on the prioritisation strategy. The first prioritisation method selects these features, whose intensities followed rising or falling trends over time series measurements. As a result, influences on industrial wastewater through the different production processes in an industrial park were recognised. This method was carried out by principal component analysis (PCA) and group wise PCA (GPCA). 130 of initially 3303 detected features were prioritised in the WWTP influent samples. In addition to prioritisation, the introduced method enabled componentisation (grouping of several features into one TrOC). As proof of concept, one feature with an increasing trend over five months was identified as N methylpyrrolidone. In the second trend related prioritisation method, the time series investigations were linked to spatial trends. For this purpose, several sampling sites before and after the WWTP were sampled and analysed over five months. This allows evaluating the treatment procedure of industrial wastewater over time. Besides, site specific features were detected. In future studies, these features could serve as a fingerprint in the monitoring of the wastewater streams. In contrast to the first two, the latter prioritisation method shows a more technical approach. TrOCs, which were repeatedly detected in the influent of the WWTP by the routine monitoring, but which were initially not identified (‘known unknowns’) are prioritised and identified by an (offline) two dimensional LC coupled to two kinds of detection techniques (ultra violet detection and MS). The identification of these ‘known unknowns’ is of high interest for the operators of the WWTP. LC UV peaks from wastewater samples were fractionated manually in the first dimension and elucidated in the second dimension, the LC HRMS, by NTS. By applying this method, the analysis of only one sample fraction led to sampling purification and therefore, data reduction. As an example, the ‘known unknown’ with the retention time of 41.1 minutes and the maximum UV absorption of 240 nm in the first dimension was successfully identified as a dichlorodinitrophenol isomer. All in all, this work shows that the use of HRMS data, in combination with NTS and the application of the presented prioritisation methods, extends the monitoring of industrial wastewater and permits an evaluation of the WWTP processes. It indicates great potential for future establishment in routine monitoring.

The research paper ventures through the control of projector wirelessly with smart phone. Present remotes work on IR based communication. The device to be controlled is considered as a receiver and the remote behaves as a transmitter. In case the remote is lost, the controlling becomes impossible, thus making it difficult for the user to operate the device . In this paper, authors have tried to remove this hardware dependency by controlling the infrared device using the Bluetooth technology. A prototype of wireless remote has been developed using Arduino microcontroller, Bluetooth module and IR LED. An Android based app is also developed to control IR LEDs wirelessly through smart phones. This device takes care of security issues encountered with Bluetooth based communication also. The app is designed to satisfy this need. It asks for an authenticated user id and password to be operated and name of the Bluetooth devices are also encrypted, so that intruders will not get access to the developed device. There are several IR based remotes but the developed product stands apart because of its user friendly user interface, authentication, speed of operation and cost. Keywords : Bluetooth, infrared, IR LED, IR remote, microcontroller Cite this Article Kulwant Singh, Ashwin Dixon, Chandra Mukherjee. Smart Infer Hopper: An Infrared Bluetooth Controlled App Based Remote Control for Projector. Journal of Instrumentation Technology & Innovations . 2018; 8(1): 13–20p.

Background: New oral anticoagulants (NOACs) are predicted to become the new standard treatment for stroke prevention in patients with atrial fibrillation, and may replace vitamin K antagonists (VKAs). NOACs are prescribed less than expected, even though they do not require international normalised ratio (INR) monitoring. In this study we assessed methods for INR monitoring after the introduction of NOACs a in heterogeneous sample of countries. Methods: We asked representatives of the Vasco da Gama Movement, a network of junior and future gen- eral practitioners (GPs) in Europe, and WONCA, the World Organization of Family Doctors, to describe the way INR is monitored in their respective countries. Results: Representatives of 14 countries responded. In most countries, the INR is monitored by GPs; in some countries, these patients are treated by other specialists or in specialised anticoagulation centres. In only a few countries, anticoagulated patients monitor the INR themselves. Conclusion: Our study showed several strategies for managing anticoagulation in different countries. In most countries, the INR is monitored by GPs. These consultations offer opportunities to address other is- sues, such as blood pressure control or medication adherence. These factors may be considered when de- ciding to switch patients from VKAs to NOACs.

Smart grid relay protection device is the key equipment to ensure the security and stability of power grid operation, and it is the basis to realize the secure and stable supply of energy and the sustainable, healthy development of national economy. At present, the production of relay protection device is discrete island mode which slows down the production process, the automation degree of device assembly and testing process is not high, the production process is opaque, which reduce the output of the product. This paper designs an automatic production line control system which is based on model of Cyber-Physical Systems (CPS), and meets the requirements of production process parameters, and realizes the automatic production of equipment assembly, testing, logistics etc. This system uses auto machines replacing human labor and designs logic timing control, and uses active Radio Frequency Identification (RFID) tags to track device and detect relevant status. In this line system, each process station adopts modular design idea and uses PLC for independent control and data acquisition. Furthermore, the system has been integrated and tested in conjunction with Manufacturing Execution System (MES), the dispatching system of Automated Guided Vehicle (AGV), device product testing system. The production line based on CPS model is of great significance for improving shop floor’s environment, saving labor costs, and improving the quality and production efficiency of relay protection products.

In an environment of constantly growing market dynamics and associated increasing complexity of company structures and their production processes, manufacturing companies are forced to adapt to this environment. Information technology is thereby a key for manufacturing companies to regain sovereignty over their own production processes. Digital networking via their own company and as well, the overall supply chain, can only succeed if digital planning reflects reality as accurately as possible and if production control can react to deviations in real time. In essence, this leads to a development of process control towards process regulation. While long-term production and resource planning is usually mapped by Enterprise Resource Planning (ERP) systems, detailed planning, including short-term deviations and real-time data at the production level, is increasingly supported by Manufacturing Execution Systems (MES) at the production control level. However, in order to bring the underlying system concepts into line with Industry 4.0 efforts in a standardized manner, mutual functional integration within the framework of interoperable production planning and control is of crucial importance. For this purpose, studies were carried out in particular into cause-effect relationships. Thus, the overarching research objective is a valid design model to increase the controllability of production planning and control systems (PPC) in the context of Industry 4.0.

The article substantiates the impact of applying lean manufacturing tools on the digital company’s transformation. The study distinguishes the main lean manufacturing tools that contribute to the optimisation of production processes, reduce losses and improve staff efficiency. Special attention is paid to the synergy of lean methods and digital technologies, which allows companies to adapt to changing market conditions and remain competitive by accelerating and replicating production processes in their digital transformation. The case study of implementing lean manufacturing tools at the Russian food industry enterprises, participants in the national project “Labour Productivity,” demonstrates the feasibility of implementing the key 5 lean manufacturing principles (sorting, organising, standardising, keeping clean and improving), along with the Manufacturing Execution System (MES). The author concludes that companies need to combine lean manufacturing and digital technologies to improve efficiency. Given the fact that lean manufacturing and information technology can conflict, providing their integration will increase the success of the digital company’s transformation. A lean manufacturing approach simplifies production processes, improves the efficiency of digitalisation and increases the likelihood of successful adoption of Industry 4.0 technologies.