Search

The implementation of “Vision Industrie 4.0” focuses on the networking of intelligent production systems.

Using the simple formula “assistant + data glasses = skilled worker”, a German news magazine described possible effects on work and assistance systems in Industrie 4.0, quoting one manager as follows: “With data glasses, a new worker can achieve the same results after a short briefing as another employee with many years of experience” and “When Amazon hires 800 new people for the Christmas business, they won’t need 20 trainers in the future, but can put on the Smartglass and get started immediately” [NN14].

In addition to the work described above on the functional areas of the current research funding for Industrie 4.0, the two projects Speedfactory and Storefactory, funded by the Federal Ministry of Economics and Energy, should be mentioned in particular. In addition, there are fields of action, especially for the Industrie 4.0 solution transfer to the industry, as well as for future actions, which will be presented in the following chapter.

As deduced in Sect. 3.3, self-organisation or autonomy is a relevant research topic for the textile industry. In the field of textile machines and self-organisation, self-optimising systems are of particular importance.

In the following section, relevant research topics in the area of Industrie 4.0 are derived for the textile industry. This derivation of topics is based on an industry survey, the analysis of current projects, the strategy papers on Industrie 4.0 and the current situation of the textile industry in Germany.

The production of reclaimed fibers from used textiles and their processing into textile products has been an effective recycling solution for centuries and thus one of the oldest material cycles in the world. It has arisen historically from the scarcity of naturally occurring vegetable and animal fiber, the economy and the relative lack of need of people. The aim has always been to recover the fibers contained in unneeded textile structures as gently as possible as a raw material for new products.

This article delineates the work of an interdisciplinary research group concerning the implementation of a digital assistance system in the German textile industry. Using a holistic approach, researchers from different disciplines contribute to the design of an integrated socio-technical method that guides industrial actors in developing and implementing digital assistance systems that are applicable on the shop-floor level and at the same time take into account various social and organizational demands. Following this approach, the development of new technologies is coordinated with innovative social practices, for example, learning techniques or organizational changes. Furthermore, aspired users of the assistance system participate in the project by contributing their expertise of the working progress as well as by defining requirements essential towards addressing the various challenges at hand. The conceptual outline and early findings of the project, including the development of a prototype of the assistance system, are presented in this article.

The warp tension is a critical variable of the weaving process. If the warp tension is too high or too low, the weaving process will be interrupted. In order to find a suitable setting for the weaving machine, the experience of the operator is needed. Self-optimization routines can support the operator in finding optimal settings. Within this paper, the model-based self-optimization of the weaving process developed at Institut fur Textiltechnik der RWTH Aachen University is presented. The self-optimization routine uses an automatic design of experiment to generate data for a full quadratic regression model of the characteristic values of the warp tension. Three weighted quality criteria are used to optimize the machine settings within given boundaries. An improvement is proposed by integrating the vertical warp stop motion position as a factor with high impact on the warp tension. The vertical warp stop motion position is automated and integrated into the optimization process. The adjusted routine is validated on an air jet weaving machine. The test results show that the integration of the warp stop motion position into a self-optimization routine leads to a 35% reduction of tension in the warp yarns. Compared to the existing routine, the integration of the warp stop motion position leads to a 23% higher effect on the warp tension as the target value of the optimization. The statistical validation shows that the quality of the used regression model is high. The described system also reduces the setup time of a weaving machine. Economically, the improvements mean a reduction of production costs by 22%, when producing small lot sizes. The system therefore contributes to the competitiveness of weaving mills in high-wage countries.

Purpose – The purpose of this paper is to deal with the energy efficiency of textile weaving machines. Increasing energy costs and environmental impact are a challenge for textile manufacturers as well as for the developers of textile production machines. As example, air jet weaving is the most productive but also most energy consuming weaving method. Design/methodology/approach – A method based on energy efficiency considered as the main requirements in the design phase has been developed at the Institut für Textiltechnik der RWTH Aachen University (ITA), Aachen, Germany, in order to improve energy efficiency of air-jet weaving machines. Technological developments are always concerned about low energy costs, low environmental impact, high productivity and constant product quality. The high degree of energy consumption of the method can be ascribed to the high need of compressed air required by the relay nozzles during the weft insertion process. Findings – The relay nozzles of the air-jet weaving technology consume up to 80 percent of the air required by the weft insertion process. At ITA a new nozzle concept was developed. The developed geometry is a so called high-volume-low-pressure nozzle, based on convergent nozzle aerodynamic theory. Originality/value – By employing such new concept of relay nozzles within the weft insertion process, energy savings are possible up to 30 percent.

In the textile and clothing industry, global value-added networks are widespread for textile and clothing production. As a result of global networks, the value chain is fragmented, and a great deal of effort is required to coordinate the production processes. In addition, the planning effort on the quantity and design of the goods is high and risky. Today the fashion industry is facing an increasing customer demand for individual and customizable products in addition to short delivery times. These challenges are passed down to the textile and clothing industry decreasing batch sizes and production times. Conventional clothing production cannot fulfill those demands especially when combined with more individual designs. Hence new sustainable and economical production concepts have to be developed. Together with the adidas AG, Herzogenaurach, a flexible and automated in-store production concept for knitted customized merino wool sweaters has been developed. With “Industrie 4.0” technologies, an urban and customer close production system has been developed. The analysis of the economical key performance indicators shows how such a new production system performs against a conventional production in Asia and where potentials are hidden.

Assistance systems for production machines have to fulfill several functions depending on specific tasks and users. The overall purpose of the use of an assistance system in production is to raise the efficiency of the production. In this text, one approach for an assistance system for a weaving machine is presented. Based on a systematic approach, a system is designed in order to facilitate the repair of broken weft yarns. Using this approach an assistance system with a high general user acceptance can be achieved. Furthermore, the use of assistance systems will have an impact on the necessary qualification of operators in the weaving mills.

Warp tension is a critical variable of the weaving process. If the warp tension is too high or too low the weaving process will be interrupted. In order to find suitable setting for the weaving machine, the experience of the operator or data base systems are used. Within this paper an automatic setup routine following model based self-optimization strategies is proposed. Within the routine, data for a regression model are collected by the weaving machine. For given quality criteria the weaving machine is able to calculate an optimal setting point. Validation of the routine shows that the chosen regression model is suitable; stress on the warp yarns is reduced. In addition, a statistical validation proves the usability of the regression models.

A vision-based measurement system to quantify the yarn density of woven fabrics during production is presented. As an extension to an earlier developed fabric flaw detection system, the proposed framework consists of a combination of basic and custom-made image-processing techniques that allow to precisely track single wefts and warps within fabric images—in real-time. Several adaptations facilitate the measurement of density changes for plain, satin, and twill weaves. In this paper, the algorithmic framework has been evaluated in several comprehensive on-line experiments on a real-world air-jet loom and is additionally compared with three alternative methods for fabric density measurement. It proved to be precise, robust, and applicable for industrial use as it overcomes many of the existing shortcomings of current methods.

Actuators 4(1), 2-16 (2015). doi:10.3390/act4010002, Published by MDPI, Basel

17th World Textile Conference AUTEX 2017 - Shaping the Future of Textiles : 29-31 May 2017, Corfu, Greece 17th World Textile Conference AUTEX 2017 : Textiles - Shaping the Future, AUTEX 2017, Corfu, Greece, 29 May 2017 - 31 May 2017; London [u.a.] : Institute of Physics, IOP conference series / Materials science and engineering, 254, 092004, 1-6 (2017). doi:10.1088/1757-899X/254/9/092004, Published by Institute of Physics, London [u.a.]

Germany's Textile Industry with its hybrid and highly fragmented value chains is seen as both a future key supplier and adopter of digital operations technologies and Industry 4.0 solutions. Yet, companies hesitate to start their digital transformation process due to severe implementation barriers including uncertainties about financial benefits and the lack of specialist knowledge. In order to overcome these challenges and to help textile manufacturersto kick-start their digital transformation, we are setting up the Textile Learning Factory 4.0 at the Institut fur Textiltechnik der RWTH Aachen University in Aachen, Germany. The factory will become a central location to deliver capability building in a real-life demonstration and learning environment as well as a test base for piloting and scaling-up new digital solutions. Therefore, an end-to-end value chain from order to delivery for the manufacturing of smart, customer-specific textile products will be set up within the factory. The line will feature two development stages (Lean and Industry 4.0). In the Lean stage participants are going to learn how to systematically conduct a digital transformation while the Industry 4.0 stage will serve as a demonstrator featuring state-of-the-art digital solutions. In this paper, we want to present the initial motivation, the current status and the future prospects of the factory.

17th World Textile Conference AUTEX 2017 - Shaping the Future of Textiles : 29-31 May 2017, Corfu, Greece 17th World Textile Conference AUTEX 2017 : Textiles - Shaping the Future, AUTEX 2017, Corfu, Greece, 29 May 2017 - 31 May 2017; London [u.a.] : Institute of Physics, IOP conference series / Materials science and engineering, 254, 202001, 1-5 (2017). doi:10.1088/1757-899X/254/20/202001, Published by Institute of Physics, London [u.a.]

Customer demands have become more individual and complex, requiring a highly flexible production. In high-wage countries, efficient and robust manufacturing processes are vital to ensure global competitiveness. One approach to solve the conflict between individualized products and high automation is Model-based Self-optimization (MBSO). It uses surrogate models to combine process measures and expert knowledge, enabling the technical system to determine its current operating point and thus optimize it accordingly. The objective is an autonomous and reliable process at its productivity limit. The MBSO concept is implemented in eight demonstrators of different production technologies such as metal cutting, plastics processing, textile processing and inspection. They all have a different focus according to their specific production process, but share in common the use of models for optimization. Different approaches to generate suitable models are developed. With respect to implementation of MBSO, the challenge is the broad range of technologies, materials, scales and optimization variables. The results encourage further examination regarding industry applications.

Multiple Family Therapy (MFT) has gained increasing popularity in the treatment of eating disorders and many programs have been developed over the past decade. Still, there is little evidence in the literature on the effectiveness on MFT for treating eating disorders. The present study examines the effects of a particular model of Multiple Family Therapy on eating disorder symptoms, quality of life, and percentage of Expected Body Weight (%EBW) in adolescents with eating disorders (ED). Eighty-two adolescents with ED, aged between 11 and 19 years, were assessed before and after treatment using the Eating Disorders Inventory 2 (EDI-2), the Outcome Questionnaire 45 (OQ-45) and %EBW. Results showed a significant increase in %EBW between the beginning and end of treatment, with a large effect size. 52.4% of patients achieved an EBW above 85%. Symptoms relative to all EDI dimensions (except for bulimia) significantly decreased during treatment. The three dimensions related to quality of life assessment also improved over the course of MFT. At the end of treatment, 70.7% of patients had a total OQ-45 score below clinical significance. This study suggests that Multiple Family Therapy may benefit adolescents with eating disorders, with improvement on several outcome measures (%EBW, ED symptoms, and quality of life). However, the lack of a comparison group entails caution when drawing conclusions. La terapia familiar multiple ha adquirido gran popularidad en el tratamiento de trastornos alimentarios, es por eso que en la ultima decada se han desarrollado numerosos programas para tal fin. Aun asi, la bibliografia existente contiene escasa evidencia de la eficacia de la terapia familiar multiple para el tratamiento de trastornos alimentarios. El presente estudio analiza los efectos que produce un modelo particular de terapia familiar multiple en los sintomas de trastornos alimentarios, en la calidad de vida y en el porcentaje de peso corporal esperado en adolescentes con trastornos alimentarios. Se evaluo a ochenta y dos adolescentes con trastornos alimentarios de entre 11 y 19 anos antes y despues del tratamiento utilizando el test de trastornos alimentarios 2 (Eating Disorders Inventory 2, EDI-2), el cuestionario de resultados 45 (Outcome Questionnaire 45, OQ-45) y el porcentaje de peso corporal esperado (%EBW). Los resultados indicaron un aumento significativo en el porcentaje de peso corporal esperado entre el comienzo y el final del tratamiento, con un gran tamano del efecto. El 52,4% de los pacientes obtuvo un peso corporal esperado superior al 85%. Los sintomas relativos a todos los aspectos del test de trastornos alimentarios (salvo la bulimia) disminuyeron considerablemente durante el tratamiento. Los tres aspectos relacionados con la evaluacion de la calidad de vida tambien mejoraron durante el transcurso de la terapia familiar multiple. Al final del tratamiento, el 70,7% de los pacientes obtuvo un puntaje total en el cuestionario de resultados que carece de importancia clinica. Este estudio sugiere que la terapia familiar multiple puede beneficiar a adolescentes que padecen trastornos alimentarios produciendo mejoras en varios indicadores de resultados (porcentaje de peso corporal esperado, sintomas de trastornos alimentarios y calidad de vida). Sin embargo, la falta de un grupo comparativo implica cautela a la hora de sacar conclusiones. 多重家庭心理治疗(MFT)日益普遍的被用于治疗饮食失调,在过去的十年中,已经发展起了很多项目。然而,文献中很少有关于多重家庭心理治疗对于饮食失调疗效的证据。本文研究了某一特定多重家庭心理治疗模式对治疗饮食失调症状的疗效,及其对饮食失调(ED)的青少年生活质量和预期体重百分比(%EBW)的影响。82名年龄在11到19岁之间患有饮食失调症的青少年在接受治疗前后通过饮食失调清单2 (EDI-2), 结果调查问卷45(OQ-45) 和 %EBW进行评估。结果显示,治疗前后%EBW有明显增高,疗效尺度很大。52.4%的患者实现了预期体重百分比超过85%。与EDI 指标有关的所有症状(除了暴食症)在治疗期间明显减少。三项关于生活质量评估的指标在家庭心理治疗的过程中也有所改善。疗程最后,70.7%的患者OQ-45总值在临床意义之下。本项研究表明多重家庭心理治疗对饮食失调的青少年有帮助,使其多项结果衡量指标(%EBW, ED 症状和生活质量)有所改善。然而,由于没有对照组,我们下结论时仍需谨慎。

Al2O3 films have been deposited at 85 °C by atomic layer deposition onto single 100 nm thick tris-(8-hydroxyquinoline) aluminium (AlQ3) films made onto silicon wafers. It has been found that a thick ALD-deposited Al2O3 layer (> 11 nm) greatly prevents the photo-oxidation of AlQ3 films when exposed to continuous UV irradiation (350 mW/cm2). Thin Al2O3 thicknesses (< 11 nm) on the contrary yield lower barrier performances. Defects in the Al2O3 layer have been easily observed as non-fluorescent AlQ3 singularities, or black spots, under UV light on the system Si/AlQ3/Al2O3 stored into laboratory conditions (22 °C/50% Relative Humidity (RH)) for long time scale (~ 2000 h). Accelerated aging conditions in a climatic chamber (85 °C/85% RH) also allow faster visualization of the same defects (168 h). The black spot density grows upon time and the black spot density occurrence rates have been calculated to be 0.024 h− 1·cm− 2 and 0.243 h− 1·cm− 2 respectively for the two testing conditions. A detailed investigation of these defects did show that they cannot be ascribed to the presence of a detectable particle. In that sense they are presumably the consequence of the existence of nanometre-scaled defects which cannot be detected onto fresh samples. Interestingly, an additional overcoating of ebeam-deposited SiO2 onto the Si/AlQ3/Al2O3 sample helps to decrease drastically the black spot density occurrence rates down to 0.004 h− 1·cm− 2 and 0.04 h− 1·cm− 2 respectively for 22 °C/50% RH and 85 °C/85% RH testing conditions. These observations highlight the moisture sensitivity of low temperature ALD-deposited Al2O3 films and confirm the general idea that a single Al2O3 ALD film performs as an ultra-high barrier but needs to be overprotected from water condensation by an additional moisture-stable layer.

Real (physical) objects melt together with information-processing (virtual) objects to create Cyber-Physical Production Systems (CPPS). Through embedding of intelligent, self-optimizing CPPS in process chains, productivity of manufacturing companies and quality of goods can be increased. Textile producers especially in high-wage countries have to cope with the trend towards smaller lot sizes in combination with the demand for increasing product variations. One possibility to cope with these changing market trends consists in manufacturing with CPPS and cognitive machinery. This chapter presents a method for multi-objective self-optimization of the weaving process. Multi-objective self-optimization assists the operator in setting weaving machine parameters according to objective functions. The implementation of a self-optimization routine in a software-based Programmable Logic Controller (soft-PLC) is presented. The routine enables a weaving machine to calculate the optimal parameter settings autonomously. Set-up time is reduced by 75 % and objective functions are improved by at least 14 % compared to manual machine settings.

Smart manufacturing concepts merge modern production machinery and digital technologies in Cyber-Physical Production Systems (CPPS). CPPS consist of intelligent, real-time-capable, networked sensors and actuators. Operation of such advanced machinery requires substantial employees' skills in various qualification phases. This holds true for different industries, such as the German textile industry. An important measure that is intertwined with the implementation of advanced manufacturing concepts in textile production is supporting employees in their development of skills concerning smart manufacturing concepts. For this purpose, the development of Augmented reality (AR)-based assistance systems in connection with up-to-date textile machinery is regarded as a promising step towards the successful implementation of adequate, user-adaptable digital working environments in the textile industry. As the implementation of such assisted digital working environments in industrial organizations poses challenges, they might most adequately be addressed by adhering to standardized implementation procedures.

Real (physical) objects melt together with information-processing (virtual) objects. These blends are called Cyber-Physical Production Systems (CPPS). The German government identifies this technological revolution as the fourth step of industrialization (Industry 4.0). Through embedding of intelligent, self-optimizing CPPS in process chains, productivity of manufacturing companies and quality of goods can be increased. Textile producers especially in high-wage countries have to cope with the trend towards smaller lot sizes in combination with the demand for increasing product variations. One possibility to cope with these changing market trends consists in manufacturing with CPPS and cognitive machinery. This paper focuses on woven fabric production and presents a method for multi-objective self-optimization of the weaving process. Multi-objective self-optimization assists the operator in setting weaving machine parameters according to the objective functions warp tension, energy consumption and fabric quality. Individual preferences of customers and plant management are integrated into the optimization routine. The implementation of desirability functions together with Nelder/Mead algorithm in a software-based Programmable Logic Controller (soft-PLC) is presented. The self-optimization routine enables a weaving machine to calculate the optimal parameter settings autonomously. Set-up time is reduced by 75 % and objective functions are improved by at least 14 % compared to manual machine settings.

48th Conference of the International Federation of Knitting Technologists (IFKT) : 8-11 June 2016, Moenchengladbach, Germany / editors: Susanne Aumann (Niederrhein University of Applied Sciences, Germany), Andrea Ehrmann (Bielefeld University of Applied Sciences, Germany), Marcus O. Weber (Niederrhein University of Applied Sciences, Germany) 48th Conference of the International Federation of Knitting Technologists, IFKT, Moenchengladbach, Germany, 8 Jun 2016 - 11 Jun 2016; [Bristol] : IOP Publishing, IOP conference series / Materials science and engineering, 141,1, 012014, 10 Seiten (2016). doi:10.1088/1757-899X/141/1/012014, Published by IOP Publishing, [Bristol]

Tekstilec : glasilo slovenskih tekstilcev 59(2), 182-185 (2016). doi:10.14502/Tekstilec2016.59.182-185, Published by University of Ljubljana, Faculty of Natural Sciences and Engineering, Department of Textiles, Graphic Arts and Design, Ljubljana

Smart manufacturing concepts merge modern production machinery and digital technologies in Cyber-Physical Systems (CPS). CPS consist of intelligent real-time-capable and networked sensors and actuators. The operation of such advanced machinery requires new and substantial skills in employees in various qualification phases. Successful implementation strategies take these varying skills of the workforce, which result from diverse cultural, educational, age-or gender-related socio-demographic variables, into account. Thus, the need for a differential-dynamic job design in textile production seems more relevant than ever, especially regarding the increasing number of older employees due to demographic changes in high-wage countries,. An important measure that is intertwined with the implementation of advanced manufacturing concepts in textile production is supporting employees in their development of skills concerning these new production methods. For this purpose, the development of Augmented reality-based assistance systems in connection with up-to-date textile machinery is regarded as a promising step towards the successful implementation of adequate, user-adaptable Cyber-Physical Systems.

This chapter starts with the history of denim fabric and talks about the fibres and yarns used in the manufacture of denim. Challenges and opportunities in engineering and preparing yarns for denim manufacture are explained. Furthermore, the technologies used for denim weaving and different weaving machines are detailed. Sections follow detailing the possibility of manufacturing denim efficiently using intelligent machines, and technological developments for manufacturing high quality denim. Finally, future trends in the weaving of denim are included.

The recycling of textiles is heavily based on the material type and treatment of the fibre material. Especially materials that do not allow a recovery of the raw material in virgin material quality limit the possibilities of textile recycling significantly. This circumstance leads to great amounts of accumulating worldwide textile waste that is not yet processed into new valuable products. In this section, the authors will show two strategies for the re-use and recycling of high valuable fibre materials like polyamide and carbon used in luxury applications. Afterwards, two case studies are presented to validate the presented concepts.

High-wage countries are on the brink of change, due to social and technological effects. In this paper, we will first give an outlook on both these effects concerning the German textile industry. Second, we will shortly describe the interdisciplinary build-up of our research group which influences the way how we address our research issues. Finally, we will outline two prototypical applications that serve as demonstrators for further user tests and subsequent developments.

This chapter gives an overview of three-dimensional (3D) textiles in sports and leisure clothing with respect to manufacturing processes and product requirements. The chapter first reviews key manufacturing methods of 3D textiles. It then discusses the requirements of 3D textiles for sports and recreational clothing. Finally applications of 3D textiles for sports and recreational clothing are presented.

A multilayer thin-film encapsulation process based on the use of Atomic Layer Deposition of Al2O3 for organic light-emitting diodes (OLED) has been presented at the last SID Display Week 2012 by LETI. It is based on the use of a generic stack of the kind Al2O3/organic layer/inorganic layer. When comes the time to evaluate the performances of this stack in severe climatic storage conditions, it turns out that the intrinsic stability of the organic decoupling layer becomes of outmost importance. This work details the behavior of the thin film barrier stack when exposed to 85 °C / 85 %RH climatic conditions.

A multilayer thin-film encapsulation process based on the use of Atomic Layer Deposition of Al2O3 for organic light-emitting diodes (OLED) has been developed at LETI. Its impact onto OLED characteristics and onto the lifetime degradation of devices has been studied. The degradation model has been compared to the usual model of glass-encapsulated OLED and the performances of the thin-film encapsulation has been evaluated upon storage in 65°C/85%RH conditions.

17th World Textile Conference AUTEX 2017 - Shaping the Future of Textiles : 29-31 May 2017, Corfu, Greece 17th World Textile Conference AUTEX 2017 : Textiles - Shaping the Future, AUTEX 2017, Corfu, Greece, 29 May 2017 - 31 May 2017; London [u.a.] : Institute of Physics, IOP conference series / Materials science and engineering, 254, 132003, 1-6 (2017). doi:10.1088/1757-899X/254/13/132003, Published by Institute of Physics, London [u.a.]

17th World Textile Conference AUTEX 2017 - Shaping the Future of Textiles : 29-31 May 2017, Corfu, Greece 17th World Textile Conference AUTEX 2017 : Textiles - Shaping the Future, AUTEX 2017, Corfu, Greece, 29 May 2017 - 31 May 2017; London [u.a.] : Institute of Physics, IOP conference series / Materials science and engineering, 254, 132001, 1-4 (2017). doi:10.1088/1757-899X/254/13/132001, Published by Institute of Physics, London [u.a.]

The crochet knitting machine is a warp knitting machine with a weft insertion system placed on a weft guide bar. On standard machines, the weft guide bar is made from aluminum and weighs about 570 g. The single-drive motors, which power the bar, account for 15–20% of the machines total power consumption. The aim of this research was to reduce power consumption by decreasing the mass of the weft guide bar. This was done by constructing the bar from carbon fiber reinforced plastics rather than aluminum, resulting in a mass saving of 260 g.

The human knee is one of the most frequently injured joints. More than half of these injuries are related to a failure of the anterior cruciate ligament. Current treatments (allogeneic and autologous) bear several disadvantages which can be overcome through the use of synthetic structures. Within the scope of this paper the potential of tubular woven fabrics for the use as artificial ligaments has been evaluated. Twelve fabrics made of polyethylene terephthalate and polytetrafluoroethylene were produced using shuttle weaving technology. Mechanical and biological properties of the fabrics were assessed using static tensile testing and cytotoxicity assays. The results obtained within this study show that woven tubular fabrics can be potentially used as artificial ligament structures as they can provide the desired medical and mechanical properties for cruciate ligament replacements. Through the choice of material and weaving parameters the fabrics’ tensile properties can imitate the stress–strain characteristic of the human cruciate ligament. Further assessments in terms of cyclic loading behavior and abrasion resistance of the material are needed to evaluate the success in long term implantation.

This paper describes a comprehensive workflow that incorporates geologic, seismic, and production data in an effort to understand and predict the performance of gas wells in the Fayetteville Shale play. A reservoir model was developed by integration of all available well, log, petrophysical, sonic, image, core, stimulation, production, microseismic and processed surface seismic data. This static reservoir model was used to history match the short- and long-term production performance and its variations across the exploration area. History matching production profile from multiple wells is a critical step toward understanding the key production drivers in unconventional shale gas formations. This workflow involves extensive use of surface seismic to develop a velocity model and to distribute reservoir properties. This paper highlights the multidisciplinary knowledge that was applied in the project and the lessons learned. The reservoir characterization and dual-porosity simulation model developed was used to predict production performance of future wells in the Fayetteville Shale formation. Key production drivers—such as matrix quality, natural fractures, net gas porosity—and ways to identify and exploit these parameters during the development phase of the reservoir are discussed. The ability of the models to predict production performance is also discussed. This workflow is useful in selecting drilling locations, identifying best landing points, optimizing well spacing, and guiding completion practices.

Meta-modeling for manufacturing processes describes a procedure to create reduced numeric surrogates that describe cause-effect relationships between setting parameters as input and product quality variables as output for manufacturing processes. Within this method, expert knowledge, empiric data and physical process models are transformed such that machine readable, reduced models describe the behavior of the process with sufficient precision. Three phases comprising definition, generation of data and creation of the model are suggested and used iteratively to improve the model until a required model quality is reached. In manufacturing systems, such models allow the generation of starting values for setting parameters based on the manufacturing task and the requested product quality. In-process, such reduced models can be used to determine the operating point and to search for alternative setting parameters in order to optimize the objectives of the manufacturing process, the product quality. This opens up the path to self-optimization of manufacturing processes. The method is explained exemplarily at the gas metal arc welding process.

For several years, the number of energy derivatives markets has been increasing at a tremendous rate. The same is true for the prices and the transactions volumes of energy futures contracts, with the noticeable exception of the recent crisis, in 2008–9. Such sustained growths naturally give rise to questions. What are the real dangers of such a development? Are derivatives markets always characterised by a high leverage effect, by opacity and liquidity problems? Do all these markets and transactions really respond to a need? Should the regulating authorities restrain further the transactions of speculators in such markets, before they introduce excess volatility, capable of destabilising the underlying physical markets? This chapter proposes answers to these questions, or at least part of an answer, whenever it is possible. It focuses on derivatives markets,1 and more specifically on energy derivatives markets.