articles+ search results

Mechanical Engineering, Part Accuracy, Finite Element Analysis, Modeling, Pillow Forming, Small batch production, and Single Point Incremental Forming

Single point incremental forming (SPIF) is a new sheet forming process with much potential for application in industry and can be used in combination or in place of standard sheet forming processes. However, part accuracy is a major drawback in this process. Pillow formation in particular is a major defect in SPIF because it leads to early fracture and part inaccuracy. This study aims at understanding the effects of sheet thickness, tool geometry and radius on pillowing in SPIF. The study was carried out on aluminum 1060 alloy (commercial aluminum).The findings are determined based on practical work and simulations using Finite Element Analysis (FEA) on Abaqus 6.12. The temperature, machining conditions and material property are assumed to be same in FEA. Parameters that influence the formation of pillow were studied. It is observed that an increase in tool radius decreases pillow height due lower stresses and strains as revealed by FEA. Flats tools tend to form lower pillows than round tools due to lower bending angles and also the fact that flat tools may bend the sheet at two points along the profile. Pillow height was found to increase with sheet thickness. The ratio of the tool size to sheet thickness was proven to be a very important parameter as this determines the amount of bend severity. FEA was used to determine of the stress and strain states in SPIF and hence an explanation for pillowing mechanism. The results are expected to be a useful reference to designers and manufacturing enterprises. Keywords: Single Point Incremental Forming, Pillow Forming, Small batch production, Finite Element Analysis, Modeling, Part Accuracy. ÖZ: Tek noktalı artan şekillendirme, endüstride kullanım potansiyeli çok ve kombinasyonlarla veya standart levha şekillendirmenin yerine kullanılan bir işlemdir. Parça doğrultu bu işlemin en büyük dezavantajıdır. Yastık oluşumu bu işlemin en büyük hatasıdır çünkü erken kırılma ve parça eğrilmesine sebep olur. Bu çalışma, levha kalınlığının, olet geometrisinin ve yastiklama işlemindeki yarı çapın, tek noktalı artan şekillendirmeninü zerindeki etkisini ortaya koyar. Bu deney, 1060 alüminyum alaşım üstünde gerçekleşti. Buluşumlar, pratik iş ve sonlu elemar analizini kullarak ortaya akmıştır. Sıcaklık, işleme şartları ve material özellikleri, sonlu elemar analizi ile aynı olduğu var sayıldı. Yastık üretimindeki parametrelerin etkileri araştırıldı. Sonu elemar analizinde ortaya çıkan gerilim ve burkulmaya bağlı olarak, alet yarı çapı azaldığında yastık yüksekliğinin de azaldiği gürülmşütür. Düz aletin levhayi bükmesi ve düşük bükme açılarına bağlı olarak düz aletler, yuvarlak aletlere oranla daha alçak yastıklar üretilir. Levha kalınliğına bağlı olarak yastık yüksekliğinin artması gözlemlendi. Bükülme derecesini saptamak için kullanırlar, alet boyutunun levha kalınlığına oranının, çok önemli bir parameter olduğu kanıtlarımıştır. Sonlu eleman analizi, tek noktalı artan şekillendirme de belirtilen bükülme ve gerilmeyi bulmak için kullanılır ve ayrıca yastıklama mekanizması için bir açıklamadır. Çıkan sonuçlar, tasarım ve üreticiler için kullanışlı referanslar olması beklenir. Anahtar kelimeler: Tek noktalı artan şekillendirme, Yastık oluşumu, küçük ölçekli üretim, sonlu eleman analizi, modelleme, parça doğruluğu. Master of Science in Mechanical Engineering. Thesis (M.S.)--Eastern Mediterranean University, Faculty of Engineering, Dept. of Mechanical Engineering, 2014. Supervisor: Assist. Prof. Dr. Ghulam Hussain and Co-Supervisor: Assist. Prof. Dr. Khalid A. Al-Ghamdi.

Artificial intelligence, Engineering, Artificial intelligence--Industrial applications, business.product_category, Intelligent manufacturing, Control intel·ligent, Guidelines, Intelligent control systems, computer.software_genre, Machine learning, Fuzzy logic, Industrial and Manufacturing Engineering, Field (computer science), Neural networks (Computer science), Surface roughness, AI selection, Xarxes neuronals (Informàtica), Accuracy prediction, Part accuracy, Adaptive neuro fuzzy inference system, Artificial neural networks, Artificial neural network, business.industry, Intel·ligència artificial, Mechanical Engineering, Bayesian network, Fuzzy control system, ANNs, Trial and error, Intel·ligència artificial--Aplicacions industrials, Machine tool, Bayesian networks, Adaptative network based fuzzy inference systems, Mechanics of Materials, Data mining, business, Dimensional derivations, and computer

Nowadays, despite the large volume of worldwide academic research on various aspects of metal cutting the control of workpiece precision still relies on machine-tool operator's experience and trial and error runs. In order to increase the efficiency of machining systems, many empirical models based on artificial intelligent (AI) approaches have been proposed in the past, where important process improvements were reported. This paper overviews the AI approaches applied in machining operations to predict part accuracy in terms of dimensional deviations and surface roughness. Successful techniques applied in this field such as artificial neural networks, fuzzy logic, adaptive-network-based fuzzy inference systems and Bayesian networks are briefly reviewed and compared to facilitate its use. For each AI approach, the most relevant research works are described and based on those works some guidelines are proposed for its implementation. In addition, advantages and drawbacks of each approach are summarised and a generic guideline for AI approaches selection is proposed.