Uncertainty quantification on industrial high pressure die casting process

High pressure die casting (HPDC) is a famous manufacturing technology in industry. This manufacturing process is simulated by commercial code to shed the light on the quality of casting product. The casting product quality might be affected by the uncertainty in the simulation parameter settings. Thus, the uncertainty quantification on HPDC process is significant to improve the casting quality and the manufacturing efficiency. In this work, three uncertainty quantifications and sensitivity analyses on the A380 aluminum alloy HPDC process of intermediate speed plate are performed. The material thermophysical properties, boundary conditions of the model, and operational as well as artificial parameters with their uncertainties, are considered as the inputs of interest. Uncertainty quantification and sensitivity analyses are investigated for the outputs of interest including percent volume of porosity result, percent volume of fraction solid less than 1, and the percent volume that solidified during multiple solidification times. The most influential input parameter for predicting the outputs of interest is the boundary condition of metal-die interfacial air gap

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Thermooxidative stability of PMMA composites

Tato práce se zabývá studiem termooxidační stability kompozitů polymethylmethakrylátu (PMMA) plněného mikro a nanočásticemi siliky. V připravených vzorcích byly použity různé objemové zlomky a různé velikosti částic siliky. Studium stability bylo prováděno pomocí termogravimetrie, která umožňuje simulovat podmínky termooxidační degradace. Indukční perioda byla stanovena za použití různých rychlostí ohřevu a aplikací izokonverzních metod. Závislosti teplot degradací na rychlostech ohřevu sloužily pro určení parametrů odvozených ze čtyř různých teplotních funkcí, které dovolují předpověď stability materiálu (indukční periody) při zvoleném rozsahu teplot. Zjištěné výsledky ukazují, že větší částice siliky snižuji stabilitu PMMA, zatímco nanočástice v nízkých koncentracích ji nijak neovlivňují.In this work the thermooxidative stability of poly(methyl metacrylate) (PMMA) composites reinforced with silica micro and nanoparticles was studied. Different volume fractions and particles sizes of silica particles were used. PMMA/silica composites were analysed by thermogravimetry which simulated the conditions of thermooxidative degradation. The induction periods were determined using different heating rates and applying the isoconversional methods. The dependence of degradation temperatures on heating rates were used for the determination of adjustable parameters derived for four different temperature functions allowing the prediction of material stability (induction periods) at chosen temperatures. Results showed that the larger silica particles destabilized the PMMA structure while smallest nanoparticles at low concentration had no effect on the stability.

Block 2 SRM conceptual design studies. Volume 1, Book 1: Conceptual design package

The conceptual design studies of a Block 2 Solid Rocket Motor (SRM) require the elimination of asbestos-filled insulation and was open to alternate designs, such as case changes, different propellants, modified burn rate - to improve reliability and performance. Limitations were placed on SRM changes such that the outside geometry should not impact the physical interfaces with other Space Shuttle elements and should have minimum changes to the aerodynamic and dynamic characteristics of the Space Shuttle vehicle. Previous Space Shuttle SRM experience was assessed and new design concepts combined to define a valid approach to assured flight success and economic operation of the STS. Trade studies, preliminary designs, analyses, plans, and cost estimates are documented

Towards A Computational Intelligence Framework in Steel Product Quality and Cost Control

Steel is a fundamental raw material for all industries. It can be widely used in vari-ous fields, including construction, bridges, ships, containers, medical devices and cars. However, the production process of iron and steel is very perplexing, which consists of four processes: ironmaking, steelmaking, continuous casting and rolling. It is also extremely complicated to control the quality of steel during the full manufacturing pro-cess. Therefore, the quality control of steel is considered as a huge challenge for the whole steel industry. This thesis studies the quality control, taking the case of Nanjing Iron and Steel Group, and then provides new approaches for quality analysis, manage-ment and control of the industry. At present, Nanjing Iron and Steel Group has established a quality management and control system, which oversees many systems involved in the steel manufacturing. It poses a high statistical requirement for business professionals, resulting in a limited use of the system. A lot of data of quality has been collected in each system. At present, all systems mainly pay attention to the processing and analysis of the data after the manufacturing process, and the quality problems of the products are mainly tested by sampling-experimental method. This method cannot detect product quality or predict in advance the hidden quality issues in a timely manner. In the quality control system, the responsibilities and functions of different information systems involved are intricate. Each information system is merely responsible for storing the data of its corresponding functions. Hence, the data in each information system is relatively isolated, forming a data island. The iron and steel production process belongs to the process industry. The data in multiple information systems can be combined to analyze and predict the quality of products in depth and provide an early warning alert. Therefore, it is necessary to introduce new product quality control methods in the steel industry. With the waves of industry 4.0 and intelligent manufacturing, intelligent technology has also been in-troduced in the field of quality control to improve the competitiveness of the iron and steel enterprises in the industry. Applying intelligent technology can generate accurate quality analysis and optimal prediction results based on the data distributed in the fac-tory and determine the online adjustment of the production process. This not only gives rise to the product quality control, but is also beneficial to in the reduction of product costs. Inspired from this, this paper provide in-depth discussion in three chapters: (1) For scrap steel to be used as raw material, how to use artificial intelligence algorithms to evaluate its quality grade is studied in chapter 3; (2) the probability that the longi-tudinal crack occurs on the surface of continuous casting slab is studied in chapter 4;(3) The prediction of mechanical properties of finished steel plate in chapter 5. All these 3 chapters will serve as the technical support of quality control in iron and steel production

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security