Applied Soft Computing 65 (2018) 272–279 Contents lists available at ScienceDirect Applied Soft Computing j ourna l ho me page: www.elsevier.com/locate /asoc Design of patient specific dental implant using FE analysis and computational intelligence techniques Sandipan Roy a , Swati Dey b , Niloy Khutia b , Amit Roy Chowdhury b,∗ , Shubhabrata Datta a a Department of Mechanical Engineering, SRM University, Kattankulathur, Chennai 603203, India b Department of Aerospace Engineering & Applied Mechanics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, India a r t i c l e i n f o Article history: Received 20 November 2016 Received in revised form 6 July 2017 Accepted 19 January 2018 Keywords: Implant design Optimization Micro strain Implant stress Finite element analysis Artificial neural network Desirability function Genetic algorithm a b s t r a c t Genetic algorithm is employed for optimum designing of patient specific dental implants with varying dimension and porosity. It is generally recommended that, the micro strain at the bone implant interface should be around 1500–3000. The porous dental implant needs to be designed in such a way that the micro stain remains within the above range, and a value close to 2500 micro strain is most desired. In this design problem, the most important constraint is that the implant stress should be limited within 350 MPa as titanium alloy was considered as implant material. The above attributes are to be achieved per the varying bone conditions of the patients to design a patient specific prosthesis. This design problem is expressed as an optimization problem using the desirability function, where the data generated by finite element analysis is converted to an artificial neural network model. The output of the neural network model is converted within a range of 0–1 using desirability function, where the maximum value is reached at the most desired micro strain of 2500. This hybrid model of neural network and desirability function is used as the objective function for the optimization problem using genetic algorithm. Another neural network model describing the implant stress is used as the constraint. The optimum solutions achieved from ANN and GA are validated again through finite element method. Without doing stress analysis by FEM, the ANN models are used for measuring the fitness of the members of the population during optimization. This would predict the optimum dimension of dental implant made of Titanium alloy with most favorable porosity percentage for better ossiointegration for a patient per bone condition. © 2018 Elsevier B.V. All rights reserved. 1. Introduction A dental implant system involves a transplant, which can be surgically implanted in the maxilla or mandible, along with an abutment that mates with the implant after it successfully inte- grates into the bone. The applications of mechanical components as dental implants are branded as one of the most popular and common treatment for dental restoration with reported high rate of success. The success of the prosthesis is based on several factors which affect the bone and implant interface, implant and abutment interface and the abutment–prosthesis interfaces as studied [1]. The works of several researchers show that the applied loads, qual- ity and quantity of the bone, material and shape of the prosthesis also play an important role in determining the stress level in the bone surrounding the implant. Optimization of the shape and mate- rial of the dental implants has been the focus of research recently ∗ Corresponding author. E-mail address: amit@aero.iiests.ac.in (A. Roy Chowdhury). [2–6]. Various works have been performed in order to reduce the stress concentration, by optimizing the shape and size of the pros- thesis since the bone quality or the applied loads cannot be changed easily [3,7]. Numerous investigations in this domain focused on increasing the diameter and/or the length of the prosthesis, thus, increasing the contact area between the bone and implant, reducing the stress level in the bone [6,7]. Per a previous work reducing the effect of shear forces on the bone-implant interface for better preservation of the marginal bone can be an effective method to enhance osseointegration process of the dental implants [8]. One of the significant complications and difficulties need to be addressed in design being fracture and crack- ing of the implant components, and it is confirmed that mechanical fractures of the prosthesis is the most common cause of these prob- lems [9,10]. Numerous investigations claimed that fatigue is one of the key causes of such implant damage [11–13]. Due to these complications, it is important to study the material behavior of prosthesis that may help us to attain long term strength. Application of finite element method (FEM) has been hugely popular among researchers to analyze the stress distribution of https://doi.org/10.1016/j.asoc.2018.01.025 1568-4946/© 2018 Elsevier B.V. All rights reserved.