ANALYSIS AND MODELLING OF
BIODYNAMIC RESPONSE TO HAND
ARM VIBRATION SYSTEM
Mohod Chandrashekhar D.
Research Scholar
Department of Mechanical Engineering, Laxminarayan Institute of Technology, Nagpur, Maharashtra State, 440033, India
email – cdmohod@gmail.com
Mahalle Ashish M
Associate Professor
Department of Mechanical Engineering, Laxminarayan Institute of Technology, Nagpur, Maharashtra State, 440033, India
email – cdmohod@gmail.com
Abstract:
Hand operated tools are widely used in industrial and commercial sector. These tools generate vibrations which have impact on health of an operator. Hence study of Hand Vibration Syndrome is one of the key areas where major researchers are attracted. This study considers the literature review for hand operated vibration measurement and analysis that are extensively used. Objective of this review was to understand results and effects of hand vibration transmission on health. The review could be used to develop a prediction model with use of Adaptive Neuro Fuzzy Inference System hence another objective is to represent the applicability of ANFIS in development of the model
Keywords: Tools; Vibrations; Health Effect; ANFIS.
1. Introduction
Industrial growth and practices in developing countries of Asia are working with the machines and hand operated tools that are now subjected with vibrations. Hand operated tools are not only used in mechanical industries but they are widely used in every sector of society. These vibratory tools are operated by variety of labours as well as different age groups. In this working environment operators are subjected to continuous vibrations. Vibrations have many medical effects on different body parts with different operating body postures. This is why the main aim of this literatures review to systematically represent the analysis and modeling requirement for hand arm vibration system. The review is divided in three sections. Section 1 represents medical importance of vibrations and few tools that are being employed in Asian industrial sector. Section 2 represents the methodologies that are adapted in obtaining the experimental results for different medical parameters. Section 3 represents innovative way of obtaining the prediction of medical effects with different postures, age group, gender, working surface properties like hardness. Such a prediction model can be effectively utilized by manufacturers of machine equipment’s to predict the medical effects with the use of these machines. Such model is useful to medical practitioners also for predicting that what the actual medical effects on the patient are during his operative environment.
2. Clinical Observations
Table 01 – Stages of Vibration Syndrome (Enrico and Michael, 2006)
Stage Condition of Fingers Work and Social Interface
01 No observations No Complaints
02 Tingling (intermittent) No Interface with Activities
03 Numbness (intermittent) No Interface with Activities
04 Tingling and Numbness (intermittent) No Interface with Activities
05 Blanching of Fingertips with or without numbness or tingling
No Interface with Activities
06 Blanching of one or more fingers (specially in winter) Possible interface with non work activities
07 Extensive blanching of fingertips (specially in summer and winter)
Definite interface at work
08 Extensive blanching of most fingers Change in occupation duto syndrome effects
3. Sub-headings
Varieties of tools are used in different industries. Particularly power tools used in construction and automotive industries assembly line. These tools produce vibrations and the tools are sanders, rotary and reciprocating power tools. Different tools are used with number of working surface and produces vibrations. Operator may have different working postures based on the service requirements, type of tool and may be the work environment. Out of these the most common tool that is observed in all types of sector is hand drill machine. Drill machine are used on variety of surfaces that includes concrete, wood, metals etc. Drill machines are also widely used in the fields of mining and commercial also. Hence their weights and speeds are also varying. The vibration is one of the important parameter in mechanical engineering. Many hand held power tools in the industries produce vibration. The operation of powered hand held tools such as grinders, drills etc exposes workers to hand arm vibration. The transmission of vibration generated by hand held tools to the operator hand arm system (HAS) are prone to develop the various vibration induced disorders of the hand and arm which are collectively called as hand arm vibration (HAV) syndromes. Lage et. al. (1998) and Ren et. al. (2006) observed that the vibrations transmitted can originate the several types of illness with associated symptoms of blood supply, nerves, muscles of HAS and leads neurological, vascular and oesteoarticular disorders. Vibration induced white finger is one of the vibration induced disorders in the fingers and the hand because of blanching along with tingling and numbness in the fingers and the hand (Dong and Shopper, 2002). The determination of HAV syndrome and vibration exposure has to be a very complex problem. The vibration transmission from a tool handle to the HAS depends on mechanical impedance (MI) of system Dong et. al. (2005). The use of hand held vibrating tools is common in many different professions and the tools vary in size, weight, acceleration amplitude and frequency Gerhardsson and Balogh (2005). The risk of developing HAV system depends on magnitude of vibration transmitted to the tool handle, on the mechanical coupling between the hand and the handle, on the duration of vibration exposure and on the user sensitivity to HAV. The driving point mechanical impedance (DPMI) can be effectively applied to estimate the amount of mechanical energy dissipated by the hand arm structure under a specified hand tool vibration spectrum Marcotte et. al. (2005).
Aldien et. al. (2006) observed that the transmission of vibration is dependent upon the various factors such as hand arm posture, vibration direction, grip and push force, handle diameter, magnitude of vibration, type of gender etc. A tight hand tool coupling not only imposes higher stresses on the anatomical structures of the HAS and impedes peripheral circulation, but it also increases the transmissibility of vibration to the hand and arm Mc.Dowel et.al. (2006). Operators assume considerable variations in the hand arm posture while operating hand held power tools. Such variations coupled with variations in the hand forces imparted on the tool handle could considerably different biodynamic responses of the HAS Aldien et. al. (2006).
assessing vibration isolation methods, and developing hand arm simulators for analysis and test of powered hand tools (Dong and Dong, 2007).
In the current studies the position of extended forearm with elbow angle 180° and flexed forearm with elbow angle 90° is discussed by Aldien et. al. (2006). In actual practice the hand tools are not necessarily in the said position only and hence there is need to analyze the other different postures with extended and flexed both in high and low positions Aldien et. al. (2006) and (Antonio and Volero, 2007).
4. Literature Review
The vibration transmitted to the hand arm system due to hand held tools is one of the major aspects in designing the hand held tools. The vibration transmitted are measured in terms of biodynamic response viz. by driving point mechanical impedance (DPMI), absorbed power, apparent mass, force recall method, subjective ratings of intensity and discomfort etc. The characteristics of the hand driving point mechanical impedance distributed at the fingers and the palm are very different. The characteristics suggests that the vibration power absorption measured at the fingers may be a better measure than the total vibration power absorption of the entire HAS for studying the vibration induced finger disorders; and the palm MI may have a better association with the injuries in the palm wrist arm structures Dong et. al. (2005).
The results suggest that the hand-handle contact force is strongly dependent upon not only the grip and push forces but also the handle diameter. The contact force for a given handle size can be expressed as a linear combination of grip and push forces, where the contribution of the grip force is considerably larger than that of the push force. The results further suggest that a linear relation can characterize the dependence of the contact force on the handle diameter Welcome et. al. (2004). The precise dose estimation of the vibration exposure are difficult to obtain as many factors are involved in the vibration transmission from the tool to the HAS. Self reporting of the vibration exposure may be due to individual estimation difficulties but may also depend on the psychological disposition of the worker as regards the tendency to overestimate or underestimate such an exposure (Gerhardsson and Balogh, 2005). The handle size was found to have a considerable influence on DPMI, particularly near the frequency of peak magnitude and at frequencies above 100Hz, where the effect was observed to be quite considerable. The biodynamic response of the human hand arm is slightly nonlinear; increasing the excitation amplitude has the effect of reducing the DPMI peak amplitude and the corresponding frequency Marcotte et. al. (2005). There is strong influence of hand arm posture on biodynamic measure of HAS exposed to vibration along Zh axis and gives higher power absorption and impedance magnitude under extended forearm posture than the flexed forearm posture Aldien et. al. (2006). The research shows that the gender difference gives different responses to HAV. On average rating of perceived intensity and discomfort were higher for female than male at few investigated frequencies (Neely and Burstorm, 2006).
5. ANFIS – An Overview of Application
Asian countries are populated with number of operators using vibratory equipment’s. Effects of these vibrations are slow but may be a permanent if not being cared in time. Hence it will be of greater value if dieses were diagnosed in their early stages. Varieties of clinical observations were made by practitioners to correlate the dieses. Hence neuro fuzzy inference system is mostly being use in medical studies. Observed and reported researches were study and Neagoe et. al. (2003) reported methodology of recognizing the signal for heart dieses. He uses Discrete Cosine Transform and Component Analysis for predicting the dieses. He used Gaussian Fuzzy Inference System. Guler et. al. (2005) proposed an ANFIS model for clinical investigations on brain dieses. (Polat and Gunes, 2007) used ANFIS for diabetic patients. With the application diagnostic accuracy was reached to a satisfactory level. (Ephzibah and Sun-darapandian, 2012) reported use of fuzzy inference system for clinical observation on heart dieses. Khameneh et. al. (2012) reported methodology of detection of abnormality of red blood cell using ANFIS. These reported study forces the author to develop the experimental setup for measurement of vibration effect on blood pressure with different operation position, weight, age group and gender. Results of this experimentation will then be used to develop the prediction model giving the output on blood pressure. This section represents the ANFIS and Its application for the same.
Ability of self-training of Neural network sets the various rules which can be then used for prediction. It is a multilayer feed forward that uses the neural algorithms for predicting the output factor which depends on number of input parameters. ANFIS is one of the most promising tools for various applications like detection of faults, wind speed, cyclone conditions, fore casting, expenses, etc. It basically extracts the fuzzy rules from numerical data that avoids the complications by human intelligence.
Let z is one of the output parameter that depends on two different input parameter x and y. If then rule as per first-order Sugeno fuzzy model is then expressed as
Rule − If x is A and y is B then z = p × x + q × y + R Rule − If x is A and y is B then z = p × x + q × y + R Where p , q and r i = or are linear parameters
The ANFIS have five different layers Figure 1 represents architecture for two inputs as discussed above.
Fig. 1. ANFIS Architecture for two input parameter and one output parameter
Input Nodes Node from this layer generates the membership function from which they belong. These Nodes then forms appropriate fuzzy set using these membership functions.
, = = ,
, = = ,
Where x, y are the crispy inputs to node i, and Ai, Bi. These are characterized membership function. Gaussian and bell-shaped membership functions are widely accepted due to their concise notations.
Rule nodes. AND operator used in this layer obtains one output that represents the result of the ancestor for that rule.
= = × = , … ,
Average nodes In this layer ratio of each ith layer is calculated. Wi is then considered for normalizing the firing strength.
= = ∑
= , … . ,
Consequent Nodes This is the layer where the contribution of each I th layer is computed. This function is defined as
= = + + = , … ,
Where wi is the ith node output from the previous layer. pi, qi, ri, is the coefficients of this linear combination and is also the parameter set in the consequent part of the Sugeno fuzzy model.
Output nodes All incoming signals are then added together and the single node computes the output. The defuzzification transforms each rules fuzzy result into a crisp output in this layer
= = ∑∑
Thus ANFIS uses an approach of hybrid learning algorithm. Least square method and the gradient descent method are used to update the parameters. Least square method identifies resulting parameters and gradient descent tune up the nonlinear parameters. As shown in Fig. 1 the circular nodes are not adaptive. These are fixed one. The square nodes have parameter variables. These parameters changed during the training procedure. The learning procedure is divided into the two important steps. In the first step least square method is applied to identify the consequent parameters, while the antecedent parameters (membership functions) are assumed to be fixed for the current cycle through the training set. Backward propagation of error is then observed and gradient descent method is used to update the premise parameters, through minimizing the overall quadratic cost function, while the consequent parameters remain fixed.
6. Conclusions
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