Browsing by Author "Sadigh,B.L."

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Citation - WoS: 13
Citation - Scopus: 17
Cutting Force Prediction in Ultrasonic-Assisted Milling of Ti-6al With Different Machining Conditions Using Artificial Neural Network
(Cambridge University Press, 2021) Namlu,R.H.; Turhan,C.; Sadigh,B.L.; Kiliç,S.E.
Ti-6Al-4V alloy has superior material properties such as high strength-to-weight ratio, good corrosion resistance, and excellent fracture toughness. Therefore, it is widely used in aerospace, medical, and automotive industries where machining is an essential process for these industries. However, machining of Ti-6Al-4V is a material with extremely low machinability characteristics; thus, conventional machining methods are not appropriate to machine such materials. Ultrasonic-assisted machining (UAM) is a novel hybrid machining method which has numerous advantages over conventional machining processes. In addition, minimum quantity lubrication (MQL) is an alternative type of metal cutting fluid application that is being used instead of conventional lubrication in machining. One of the parameters which could be used to measure the performance of the machining process is the amount of cutting force. Nevertheless, there is a number of limited studies to compare the changes in cutting forces by using UAM and MQL together which are time-consuming and not cost-effective. Artificial neural network (ANN) is an alternative method that may eliminate the limitations mentioned above by estimating the outputs with the limited number of data. In this study, a model was developed and coded in Python programming environment in order to predict cutting forces using ANN. The results showed that experimental cutting forces were estimated with a successful prediction rate of 0.99 with mean absolute percentage error and mean squared error of 1.85% and 13.1, respectively. Moreover, considering too limited experimental data, ANN provided acceptable results in a cost-and time-effective way. Copyright © The Author(s), 2020. Published by Cambridge University Press.
Citation - Scopus: 1
An Ontology Based Semantic Machine Tool Selection for Multi Scale Wire Edm Processes
(Trans Tech Publications Ltd, 2017) Sadigh,B.L.; Oliaei,S.N.B.; Dadvandipour,S.
Manufacturing high-tech complex products which contain multi-scale complex features without outsourcing considering company's capabilities is quite difficult. Outsourcing some processes to other independent companies is a crucial step toward fabricating a product. To find the most suitable partner company several critical parameters should be considered including company machine-park, skilled personnel, infrastructure etc. Having comprehensive information about necessary machine tool(s) to outsourcing related manufacturing process is essential. Focusing on Wire Electro Discharge Machining (WEDM) process, the objective of this paper is to introduce a platform to store and analyze information and data about part(s) and machine tools and show out coming results as a list of capable machine tools to produce the desired parts with multi-scale features.
Partner Selection in Formation of Virtual Enterprises Using Fuzzy Logic
(SciTePress, 2015) Nikghadam,S.; Sadigh,B.L.; Ozbayoglu,A.M.; Unver,H.O.; Kilic,S.E.
Virtual Enterprise (VE) is a temporary cooperation among independent enterprises to build up a dynamic collaboration framework for manufacturing. One of the most important steps to construct a successful VE is to select the most qualified partners to take role in the project. This paper is a survey of ranking the volunteer companies with respect to four evaluation criteria, proposed unit price, delivery time, quality and enterprises' past performance. Fuzzy logic method is proposed to deal with these four conflicting criteria, considered as input variables of the model. As each criterion is different in nature with the other criterion, various membership functions are used to fuzzify the input values. The next step is to construct the logical fuzzy rules combining the inputs to conclude the output. Mamdani's approach is adopted to evaluate the output in this Fuzzy Inference System. The result of the model is the partnership chance of each partner to participate in VE. A partner with highest partnership chance will be the winner of the negotiation. Implementation of this model to the illustrative example of a partner selection problem in virtual enterprise and comparing it with fuzzy-TOPSIS approach verifies the feasibility of the proposed approach and the computational results are satisfactory. Copyright © 2015 SCITEPRESS - Science and Technology Publications All rights reserve.