An extensible product structure model for product lifecycle management in the make-to-order environment

This paper presents a product structure model with a semantic representation technique that make the product structure extensible for developing product lifecycle management (PLM) systems that is flexible for make-to-order environment. In the make-to-order business context, each product could have a number of variants with slightly different constitutions to fulfill different customer requirements. All the variants of a family have common characteristics and each variant has its specific features. A master-variant pattern is proposed for building the product structure model to explicitly represent common characteristics and specific features of individual variants. The model is capable of enforcing the consistency of a family structure and its variant structure, supporting multiple product views, and facilitating the business processes. A semantic representation technique is developed that enables entity attributes to be defined and entities to be categorized in a neutral and semantic format. As a result, entity attributes and entity categorization can be redefined easily with its configurable capability for different requirements of the PLM systems. An XML-based language is developed for semantically representing entities and entity categories. A prototype as a proof-of-concept system is presented to illustrate the capability of the proposed extensible product structure model

Difficult Denture Birds: An Easy Psychological Assessment

Individuals who present uncommon and abnormal denture problems require unusual and resourceful treatment. Because of the extreme complexity of their systemic illness, physical,psychogenic abnormalities, anatomic abnormalities and neurological disorders, these are the difficult denture birds. This article mainly describes about difficult denture birds and theirmanagement.&nbsp

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

Curing Epoxy Resins by Microwave Radiation

Rapid prototyping is a generic process for making rapidly and accurately three-dimensional physical objects of almost any shape. Existing rapid prototyping techniques rely on laser and printing techniques. The use of microwave technology in rapid prototyping has not been explored as yet. In this work, curing process of thin layers of epoxy resins using microwave radiation was investigated as an alternative technique that can be implemented to develop a new rapid prototyping technique. Curing temperature and curing time have been determined for several epoxy mixtures. The mixtures were made up of three commercially available epoxy resins, hardener, aluminium and flydust powde

Behaviour Of Metal Transfer Modes In Pulse Gas Metal Arc Welding Of Aluminum

The knowledge of metal transfer mode is important for achieving good quality in thin aluminium sheets welded by pulse gas metal arc welding (GMAW-P). In this study, the effects of various pulsing parameters on metal transfer mode in thin aluminium sheets welded by GMAW-P have been investigated. The pulsing variables namely peak current, base current, peak time, and base time were chosen as variable parameters. The metal transfer mode investigation was based on the synchronization of welding signals and high speed camera to characterize and identify conditions under which different types of metal transfer modes are observed in GMAW-P system. Further investigation involved understanding the effects of the pulsing parameters on different transition region involved in GMAW-P

Prediction of die casting process parameters by using an artificial neural network model for zinc alloys

Pressure die casting is an important production process. In pressure die casting,\ud the ® rst setting of process parameters is established through guess work. Experts\ud use their previous experience and knowledge to develop a solution for a new\ud application. Due to rapid expansion in the die casting process to produce\ud better quality products in a short period of time, there is ever increasing\ud demand to replace the time-consuming and expert-reliant traditional trial and\ud error methods of establishing process parameters. A neural network system is\ud developed to generate the process parameters for the pressure die casting process.\ud The system aims to replace the existing high-cost, time-consuming and expertdependent\ud trial and error approach for determining the process parameters. The\ud scope of this work includes analysing a physical model of the pressure die casting\ud ® lling stage based on governing equations of die cavity ® lling and the collection of\ud feasible casting data for the training of the network. The training data were\ud generated by using ZN-DA3 material on a hot chamber die casting machine\ud with a plunger diameter of 60 mm. The present network was developed using\ud the MATLAB application toolbox. In this work, the neural network was developed\ud by comparing three di€ erent training algorithms: i.e. error backpropagation\ud algorithm; momentum and adaptive learning algorithm; and Levenberg±\ud Marquardt approximation algorithm. It was found that the Levenberg±\ud Marquardt approximation algorithm was the preferred method for this application\ud as it reduced the sum-squared error to a small value. The accuracy of the\ud developed network was tested by comparing the data generated fromthe network\ud with those of an expert froma local die casting industry. It was established that by\ud using this network the selection of process parameters becomes much easier, so\ud that it can be used by a novice user without prior knowledge of the die casting\ud process or optimization techniques.\ud 1. Introduction\ud Pressure die casting is an important production process that is extensively used to\ud produce castings for the electrical, electronic and automobile industries. The process\ud has its origins in type casting machines developed in 1822. The process showed its\ud production potential as early as the mid 1800s when it had reached a high level of\ud automation and mechanical efficiency.\ud In 1894, the ® rst die casting machine was developed, in which molten metal was\ud forced through an inclined port and out of the nozzle into the die by the central ram\ud actuated by a lever. During the past two decades, the pressure die casting process has\ud become an essential casting production process for the engineering industry. High\ud production rate, excellent surface ® nish and good mechanical properties of the ® n-\ud Revision received March 1999

INFOMECHATRONICS: Design and Development of First Undergraduate Inter-Disciplinary Engineering Course in Pacific Region

This paper presents an educational view on the concept of Infomechatronics as a logical development of rationalisation and integration across the traditional disciplines of mechanical engineering, electrical and electronic engineering and information technology. As a synergy of core technologies, the combined application of computers and electronic instrumentation is becoming an important component of modern machinery and processes. In order to gain a competitive edge in the modern manufacturing era where the products and the processes are becoming highly integrated in functionalities, it is essential to have workforce with inter-disciplinary knowledge. The development of Infomechatronics will therefore be essential in order to maintain the continued competitiveness among various industries such as manufacturing, construction, maintenance, mining, food processing and other service industries. This paper also discusses the growing trend towards inter-faculty knowledge requirement of engineering graduates in current industry and the development of four-year undergraduate degree in Infomechatronics Engineering at Queensland University of Technology, Australi

A set of heuristic algorithms for optimal nesting of two-dimensional irregularly shaped sheet-metal blanks

This paper deals with the development of intelligent nesting algorithms for nesting of irregularly shaped sheet-metal blanks with varying blank geometries. A set of nesting algorithms has been developed to find all the feasible arrangements in such a manner that two arbitrary blanks do not overlap or intersect by considering the constraints of sheet-metal stamping operations, such as bridge width and grain orientation, and to satisfy the design requirements, such as maximizing the strength of the blank when bending is involved as a subsequent operation. The solutions generated by this nesting algorithm are compared favorably with the manual procedures adopted in industry, and also with some of the reviewed algorithms in terms of utilization ratio

Development of an integrated neural network system for prediction of process parameters in metal injection moulding

In this present work attempts have been made to develop an integrated neural network system for prediction of process parameters\ud such as injection pressure and injection time in metal injection moulding (MIM) process. The current system has been developed by\ud integrating the different aspects of MIM process. The aspects that are addressed in this system are the physical model of MIM filling\ud stage based on governing equations of mould filling, and process parameters for debinding and sintering stages generated by\ud experimentation. In this work the feed forward type of neural network has been used, which was initially trained with the analytical\ud data before incorporating as part of an integrated system. In this work Gauss training method has been incorporated for the usage of\ud function approximation. This integrated system has been implemented in MatLAB environment by using neural networks toolbox. This\ud integrated system was successfully tested to solve the real world problems ofMIM process. The analytical algorithm based on governing\ud equations of mould filling process first produces a feasible injection time for the MIM process. Injection time data is then used to train\ud the neural network system. In order to validate the results generated by the neural network system are checked with the simulation\ud results of the ‘‘Moldflow’’ software and found that the results generated by integrated neural network system are not different from the\ud simulated results