© March 2016 | IJIRT | Volume 2 Issue 10 | ISSN: 2349-6002 IJIRT 143344 INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 165 SUSPENSION RATING IN KINEMATIC CHAIN Ashok Kumar Sharma 1 , Amit Kumar Dewangan 2 , Abhishek Kumar 3 , Ashish Sahu 4 1 Asst. Professor, Department of Mechanical Engineering 2,3,4 Students, Department of Mechanical engineering Shri Shankaracharya Technical Campus Bhilai, Chhattisgarh,India Abstract— The present work deals with the problem of detection of suspension rating which is frequently encountered in structural synthesis of kinematic chains. A new method which incorporates all essential feature of matrix method easy to compute & reliable is suggested in this paper. It is capable of detecting rating in all types of planar kinematic chains. Many methods are available to the kinematic chain to detect suspension rating among chains & inversions but each has own shortcoming. Most of the study to detect suspension Rating is based on hamming matrix & multiple equation method. This method is based on joint value & jerk absorbing capacity of kinematic chain. This method is proposed up to six bar kinematic chains & its inversion. C.N.Rao and A.C.Rao have been reported methods to predict the performance and rate the kinematic chains and mechanism among several configurations. In the present work a methodology is used which is based on the influence of type of links, type of joints present in a kinematic chain to predict the performance of kinematic chains without carrying out the dimensional synthesis. Index Terms— Kinematic Chain, Joint, Degree of Freedom, Kinematic Inversion I. INTRODUCTION The problem of detection of suspension rating between two kinematic chains has been the subject matter of investigation for a long time. This method is proposed that if the link has more jerk absorbing capacity & to produces more kinetic energy then this kinematic chain is more suspension. Nikunj Yagnik and Anurag Verma[1] proposed a method to rate kinematic chain. A.C. Rao, Raju D. Varada[2] used hamming matrix to detect isomorphism. In this method forming the joint value matrix by the help of two directly connected link.S. Shende & A. C. Rao[8] proposed Mach Theory in this field. After forming this matrix any one link is to be fixed & give a motion or jerk to another link. Calculate the motion of another connected link except that fixed or grounded link. Comparison of characteristic coefficient of the adjacency matrix of the corresponding inversion of kinematic chain is considered to be suitable for this purpose. Application of the joint value & link assortment method for structural synthesis & analysis of planer kinematic chain has been proven to be an effective & systematic approach in the suspension rating. Many of the method which is to be used for detecting the suspension rating are very lengthy & calculation is more complicated. . One of the most important and challenging problem in structural synthesis of kinematic chain is to identify the possible structural suspension between given chains. Kinematic synthesis is an essential step at the first stage of designing of a machine, as it represents creation of mechanism to achieve a desired set of motion characteristics. Ali Hasan , Khan R.A[9] used Mach theory in this field. Many researchers have directed their efforts to study various aspects of mechanisms, Machine designer have been synthesizing kinematic chains unconsciously. A lot of literature related to suspension rating detection and detection of distinct mechanism (DM) is available but still there is scope for an efficient simple and reliable method and this paper is an attempt in this direction vary important problem involved structure analysis of chains is the determination of distinct mechanism of a chain and to detect suspension rating among kinematic chains. Attempts have been made in the past to solve this problem, and a number of algorithms proposed by crossly, mainly based on the Graph Theory, are available in the literature. Most of these methods are based on link adjacency matrices or Distance matrices which was first introduced by Freudenstein and Dobrajansky. In this paper we generate a