895 resultados para Machine shops -- Automation


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El GREP (grup de recerca de producte, procés i producció) de la UdG actualment disposa d’una eina informàtica desenvolupada en un PFC del 2003 que li permet fer la seqüenciació de la producció d’un taller mecànic amb un màxim de cinc productes, un nombre definit de possibles rutes de fabricació per a cada producte i tres màquines. Aquesta eina és molt resolutiva per a aquests casos, ja que estudia totes les possibilitats i les comprova una per una. No obstant aquest fet presenta una sèrie de limitacions com són el temps d’execució doncs al comprovar totes les seqüències té un elevat cost computacional i la rigidesa del sistema doncs no ens permet seqüenciar més productes ni més màquines. Per tal de donar solució a aquest problema es planteja generar una nova eina informàtica a partir de l’actual però que permeti seqüenciar més peces sense ocupar tanta memòria per així implementar-hi futures millores com el temps de preparació etc... Per a desenvolupar l’eina informàtica s’han utilitzat mètodes heurístics, concretament dos que són: algoritmes genètics i cerca TABU. Aquests mètodes destaquen perquè no busquen totes les combinacions possibles sinó que estudien una sèrie de combinacions i utilitzant mètodes de creuament i generació d’entorns busquen una solució

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When tracking resources in large-scale, congested, outdoor construction sites, the cost and time for purchasing, installing and maintaining the position sensors needed to track thousands of materials, and hundreds of equipment and personnel can be significant. To alleviate this problem a novel vision based tracking method that allows each sensor (camera) to monitor the position of multiple entities simultaneously has been proposed. This paper presents the full-scale validation experiments for this method. The validation included testing the method under harsh conditions at a variety of mega-project construction sites. The procedure for collecting data from the sites, the testing procedure, metrics, and results are reported. Full-scale validation demonstrates that the novel vision tracking provides a good solution to track different entities on a large, congested construction site.

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Työssä on pyritty löytämään ratkaisuvaihtoehtoja pesulaitteen horisontaalisen harjan nostomekanismin parantamiseksi. Nostomekanismia pyrittiin kehittämään vähemmän huoltoa vaativaksi, toimintavarmemmaksi sekä kustannuksiltaan halvemmaksi. Uusia vaihtoehtoja mekanismille pyrittiin löytämään järjestelmällisen ideoinnin avulla, käyttäen hyödyksi alan kirjallisuutta, käytössä olevia pesulaitteita sekä jokapäiväisessä elämässä eteen tulleita havaintoja. Korroosiota aiheuttavat olosuhteet, horisontaalisen harjakokoonpanon pitkä liikerata sekä paino asettavat erityisvaatimuksia uudelle mekanismille. Lisäksi mekanismin täytyy liikuttaa harjaa vertikaalisessa suunnassa vakiona pysyvällä nopeudella. Valittuja ratkaisuvaihtoehtoja tarkasteltiin pistearvioinnin avulla ennen jatkokehittelyn aloittamista. Alustavassa komponenttien mitoituksessa pyrittiin mekanismin komponenteille löytämään oikea valmistusmateriaali sekä komponenttien geometriset mitat. Mekanismin valmistuskustannuksia eri valmistusmenetelmien kesken kartoitettiin yhteydenotoilla eri konepajojen kanssa. Alustavien kustannuslaskelmien perusteella jatkokehittelyyn otetun mekanismin kustannukset on 70 prosenttia pienemmät kuin nykyisen mekanismin kustannukset. Osien vähyys sekä mekanismin yksinkertaisuus lisää nostomekanismin luotettavuutta sekä vähentää tarvittavia huoltotoimenpiteitä.

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In this paper, we study a problem of scheduling and batching on two machines in a flow-shop and open-shop environment. Each machine processes operations in batches, and the processing time of a batch is the sum of the processing times of the operations in that batch. A setup time, which depends only on the machine, is required before a batch is processed on a machine, and all jobs in a batch remain at the machine until the entire batch is processed. The aim is to make batching and sequencing decisions, which specify a partition of the jobs into batches on each machine, and a processing order of the batches on each machine, respectively, so that the makespan is minimized. The flow-shop problem is shown to be strongly NP-hard. We demonstrate that there is an optimal solution with the same batches on the two machines; we refer to these as consistent batches. A heuristic is developed that selects the best schedule among several with one, two, or three consistent batches, and is shown to have a worst-case performance ratio of 4/3. For the open-shop, we show that the problem is NP-hard in the ordinary sense. By proving the existence of an optimal solution with one, two or three consistent batches, a close relationship is established with the problem of scheduling two or three identical parallel machines to minimize the makespan. This allows a pseudo-polynomial algorithm to be derived, and various heuristic methods to be suggested.

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Modern machines are complex and often required to operate long hours to achieve production targets. The ability to detect symptoms of failure, hence, forecasting the remaining useful life of the machine is vital to prevent catastrophic failures. This is essential to reducing maintenance cost, operation downtime and safety hazard. Recent advances in condition monitoring technologies have given rise to a number of prognosis models that attempt to forecast machinery health based on either condition data or reliability data. In practice, failure condition trending data are seldom kept by industries and data that ended with a suspension are sometimes treated as failure data. This paper presents a novel approach of incorporating historical failure data and suspended condition trending data in the prognostic model. The proposed model consists of a FFNN whose training targets are asset survival probabilities estimated using a variation of Kaplan-Meier estimator and degradation-based failure PDF estimator. The output survival probabilities collectively form an estimated survival curve. The viability of the model was tested using a set of industry vibration data.

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The research described in this paper is directed toward increasing productivity of draglines through automation. In particular, it focuses on the swing-to-dump, dump, and return-to-dig phases of the dragline operational cycle by developing a swing automation system. In typical operation the dragline boom can be in motion for up to 80% of the total cycle time. This provides considerable scope for improving cycle time through automated or partially automated boom motion control. This paper describes machine vision based sensor technology and control algorithms under development to solve the problem of continuous real time bucket location and control. Incorporation of this capability into existing dragline control systems will then enable true automation of dragline swing and dump operations.