This research focuses on the working and development of wireless robotic hand system. 

In this research previously developed models have been studied. After analysis of those 

models, a better approach has been presented in this research. The objective of this 

research is to design and develop a tele-operated robotic hand system. The robotic hand 

is intended for providing solutions to industrial problems like robot reprogramming, 

industrial automation and safety of the workers working in hostile environments. The 

robotic hand system works in the master slave configuration where Bluetooth is being 

used as the communication channel for the tele-operation. The master is a glove, 

embedded with sensors to detect the movement of every joint present in the hand, which 

a human operator can wear. This joint movement is transferred to the slave robotic hand 

which will mimic the movement of human operator. The robotic hand is a multi 

fingered dexterous and anthropomorphic hand. All the fingers are capable of performing 

flexion, extension, abduction, adduction and hence circumduction. A new combination 

of pneumatic muscles and springs has been used for the actuation purpose. As a result, 

this combination reduces the size of the robotic hand by decreasing the number of 

pneumatic muscles used. The pneumatic muscles are controlled by the opening and 

closing of solenoid valves. A novel technique has been used in the robotic hand for 

tendon routing, which gives the ability of independence to all finger joints. The heart of 

all the control mechanism of the system is mbed microcontroller. The designed system 

was tested at different module levels. The results show the successful establishment of 

communication between master and slave at a rate of 10 packets per second, which was 

sufficient for smooth motion of the system. The amount of torque produced at all the 

joints in the robotic hand has been presented in this research. The posture tests have 

been performed in which two fingers were actuated which followed the master. This 

system has achieved motion of fingers without any tendon coupling problem. The 

system is able to replace the human industrial workers performing dexterous tasks

Yaqub, Muhammad Atif

English

UTHM Institutional Repository

Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management Kuala Lumpur, Malaysia, January 22 – 24, 2011  Development of Mathematical Cost Model for Preheated End-milling of AISI D2 Tool SteelMohamed Elhadie1, A. N. Mustafizul Karim1, A. K. M. Nurul Amin1Department of Manufacturing and Materials EngineeringInternational Islamic University Malaysia, Gombak, Kuala Lumpur 53100, MalaysiaM. A. Lajis2Faculty of Mechanical and Manufacturing EngineeringUniversiti Tun Hussein Onn Malaysia, Batu Pahat, Johor Bahru 86400, MalaysiaAbstractIn this research paper, reliable mathematical model for estimating the cost of preheated end-milling of AISI D2 tool steel using TiAlN coated carbide tool inserts is developed. Initially, the different components of machining cost were identified, followed by establishment of equations to determine their values. Then, the required experimental and non-experimental data were collected and the bottom-up approach was adopted for evaluating the cost of machining corresponding to each of fifteen experimental runs. The Response Surface Methodology (RSM) was used to develop the model in which the cost of machining is given as a function of the machining parameters; cutting speed, feed per tooth, and preheating temperature, and expressed in RM per cm3. ANOVA output was utilized to check the adequacy of the developed model. The developed model was found to be statistically adequate and this was confirmed by the small prediction errors made by the model.Keywords: machining cost, cost modeling, preheated end-milling, RSM, AISI D2 tool steel.1.0 IntroductionAISI D hardened steel group is widely used in making moulds and dies for fabrication of automotive and aerospace components [1]. Cost estimation, cost analysis and cost control are highly critical factors for the continued success of a manufacturing enterprise [2].Development of reliable cost models to estimate the cost of preheated machining of AISI D2 tool steel at different levels of machining parameters; cutting speed, feed per tooth, and preheating temperature, is a useful endeavor. Having cost models enables determining which cost elements contribute most to the cost. With cost model, it is possible to determine the conditions that minimize cost (cost optimization).In this research paper, the bottom-up and parametric cost estimation techniques were merged to develop a rather new technique that is free from the limitations of the parent techniques and inherits their advantages. The bottom-up and parametric cost estimation techniques are the most common in practice. They are the two main techniques from which several other techniques branch out [4].The cost models found in the literature that can be used for estimating the cost of a machining run are generally less use-friendly, and do not combine between accuracy and user-friendliness. These problems, through merging the bottom-up and parametric techniques, and modeling the cost of machining as a function of a small number of parameters for which data can be obtained rather easily, are efficiently solved. 2.0 Overview of Past Machining Cost ModelsThe past mathematical models of machining cost are generally descriptive; that is, they describe the cost components found in machining operations. This characteristic causes two problems: firstly, the model will be consisting of parameters for some of which data is not easy to obtain. Secondly, it will be consisting of many input 576Elhadie, Karim, Amin, and Lajisparameters. Thus, it is not user-friendly. For instance, George E. D. [5] presented the following cost model which can be used to calculate the cost of an end-milling operation:)(1                                               TmttC0t)Ttoolt(1mt100)opOHW(1100)mOHM(1601uC  where Cu = total unit cost, $          M = machine cost (profit, depreciation, and maintenance), $/hOHm = machine overhead (power, proportional share of building, taxes, insurance, etc), %    W = labor rate for operator, $/h      OHop = operator overhead rate, %tm = machining timettool = tool changing timeT = tool lifet0 = time elements that are independent of tool life               Ct = tool cost, $Obviously, this model is not user-friendly for finding the cost of a particular machining run. It contains around ten input parameters for which the user has to find data. Beside this, data for some of these input parameters are not easily obtainable. The model developed in this paper contains only three input parameters; cutting speed, feed per tooth, and preheating temperature. The values for these parameters are quite easy to be obtained. Besides being a user-friendly model, it gives accurate estimations (under the specific conditions considered).Similar models (to the one presented by George E. D.) were proposed by Robert C. C. et al. [3], Gavriel S. [6], Geoffrey B. and Winston A. K. [7], and others.3.0 Research MethodologyThe methodology of this research can be outlined in form of the following activities:  Establishment of equations for evaluating the cost of removing a unit volume of material (RM per cm3). Collection of all the data (experimental and non-experimental) required for evaluation of machining cost. Evaluation of machining cost considering 25% utilization level. Use of RSM to model the cost of machining, and ANOVA output to check the adequacy of the model.3.1 Establishment of Equations for Evaluating the Cost of MachiningIn this research paper, the cost of machining is made up by the following cost components: operator cost, VMC depreciation cost, heating device depreciation cost, VMC maintenance cost, heating device maintenance cost, cost of electricity consumed by the VMC, cost of electricity consumed by the heating device, tool edge cost, tool edge changing cost, and setup, loading, unloading, and teardown (SLUT) cost [3, 5, 6, 7].Table 1 presents the equations that were established to determine the values of the machining cost components. Machining cost was expressed in RM per cm3; this is more useful than evaluating the cost per component. The cost was evaluated considering 25% utilization level. This level of utilization can be found in process-based facilities (job-shops, etc). To reduce the truncation error, a long period (a span of one year) of production has been chosen for the calculation of machining cost.During production time, the following activities are carried out: machine setup, work-piece loading, material removing, tool changing, work-piece unloading, and machine teardown. At 25% utilization, the production time per 8-hours working day is 120 minutes (8 * 60 * 0.25). Out of these 120 minutes, 24 are used for setup, loading, unloading, and teardown (SLUT). These 24 minutes are equivalent to 5% ((24 / (8 * 60)) * 100) of the working day. The remaining production time in the day at 25% utilization level is (120 - 24) = 96 minutes. These 96 minutes are equivalent to 20% (25% - 5%) of the 8-hours working day. These 96 minutes are used for material removing and tool changing only.577Elhadie, Karim, Amin, and LajisTable 1: Components of machining cost and the equations established to determine their valuesCost ComponentEquationOperatorCost yr3cmper Year VMR/yrRMper Year Salary sOperator'3cmRM3cmperCostOperatorVMC DepreciationCostyr3cmper Year VMR/yrRMAnnuity VMC3cmRM3cmperCostonDepreciatiVMCVMC MaintenanceCost yr3cmper Year VMR / yrRMper Year Cost ce MaintenanVMC 3cmRM3cmperCosteMaintenancVMCCost of Electricity Consumed by the VMChr3cmMRR / hrRMper Hour VMCby theConsumedy Electricit3cmRM3cmperCostyElectricitVMCHeating Device (HD)DepreciationCostyr3cmper Year VMR/yrRMAnnuity HD3cmRM3cmperCostonDepreciatiHDHDMaintenanceCost yr3cmper Year VMR / yrRMper Year Cost ce MaintenanHD 3cmRM3cmperCosteMaintenancHDCost of Electricity Consumed by the HDhr3cmMRR / hrRMper Hour  HDby theConsumedy Electricit3cmRM3cmperCostyElectricitHDTool Edge Cost   min3cmMRR*min LifeTool / (RM) EdgeToolper Cost 3cmRM3cmperCostEdgeToolTool Edge Changing Cost   min3cmMRR*min LifeToolminRMminper Cost  MachineminRMminper Cost Operator *minTimeChanging EdgeTool3cmRM                            3cmperCostChangingEdgeToolSetup, Loading, Unloading, and Teardown (SLUT) Cost       min3cm MRR*min LifeTool*(min)TimeChanging EdgeTool LifeToolmin K*60*8minRMminper Cost  MachineminRMminper Cost Operator *minTimeSLUT3cmRM       3cmperCostTeardownandUnloading&LoadingSetup,The volume of material removed per year (VMR per year) considered in some of the equations that are presented in Table 1 is calculated as follows:578Elhadie, Karim, Amin, and Lajis      min3cmMRR*min LifeTool*minTimeChangingTool LifeTool / )yrminK *60*8*(250yr3cmper YearVMRwhere, K = 0.20 (the level of utilization available for material removing and tool changing only).The Annuity is calculated as follows:1))ni)/((1ni)(1(i*PyrRMAnnuity where, P = initial cost of the equipment          i = cost of capital (%)          n = useful life of the equipmentOperator cost per minute is given by the following equation:   yrminon Utilizati*60*8*250/yrRMper Year Salary sOperator'minRMper MinuteCost Opertaor The machine cost per minute is given by the following equation:minRMper Minute HDby theConsumedity   ElectricminRMper MinuteVMCby theConsumedicity     Electr                              yrminon Utilizati60*8*250yrRMper Year Cost ce MaintenanHDyrRMper Year Cost ce MaintenanVMCyrminon Utilizati60*8*250yrRMAnnuity HDyrRMAnnuity VMC                                                                                                 minRMminutepercostMachine3.2 Data used for Evaluating the Cost of Machining The data that were used to evaluate the cost of machining fall into two categories; experimental data [8], and            non-experimental data. The non-experimental data were obtained based on realistic assumptions or estimations. These data are shown in Tables 2 and 3.Table 2: The non-experimental data used for evaluating the cost of machiningFactor SpecificationWorking days per year 250Working hours per day 8; one shift per dayUtilization level 25%Operator’s salary per year RM 33600 (RM 2800 * 12)Initial expense of the VMC RM 300000Initial expense of the heating device RM 6700Useful life of the VMC 15 yearsUseful life of the heating device 7 yearsCost of capital (%) 5Depreciation method Sinking fundYearly expense on VMC maintenance RM 5000Yearly expense on heating device maintenance RM 500Electricity tariff RM 0.4 per kWhPrice per edge of cutting tool RM 15Tool changing time 5 minutesSetup, loading, unloading, and teardown time 24 minutes579Elhadie, Karim, Amin, and LajisTable 3: The experimental data used in evaluation of machining costRunNo.Cutting Speed (v) (m/min)Feed (f)(mm/tooth)1 72.28 0.0252 72.28 0.0793 44.27 0.0794 44.27 0.0255 56.57 0.0446 56.57 0.0447 56.57 0.0448 56.57 0.0449 56.57 0.04410 40.00 0.04411 80.00 0.04412 56.57 0.04413 56.57 0.04414 56.57 0.02015 56.57 0.1003.3 Machining Cost Evaluated at Machining cost was evaluated considering 25% Table 4The machining parameters and their values that are presented in Table machining cost, while the machining cost values that a4.0 Results and DiscussionThe Response Surface Methodology (RSM) was utilized for this purpose. In the developed modelparameters; cutting speed (v), feed per tooth Analysis of variance (ANOVA) was used to test the 95% confidence interval. The “Prob > F” “Lack-of-fit”. “Prob > F” value that is less than 0.05 generally indicates significance at 95% confidence interval. If it is greater than 0.05, this generally indicates insignificance. PreheatingTemp.(θ)(◦C)Tool Life(min)MRR(cm3/min)VMC Electricity Cost per Hour(RM/hr)413 38.26 0.0719 0.0260273 11.67 0.2272 0.0932413 56.25 0.1391 0.0548273 73.82 0.0440 0.0166335 95.95 0.0990 0.0398335 80.8 0.0990 0.0398335 78.28 0.0990 0.0398335 85.85 0.0990 0.0398335 84.34 0.0990 0.0398335 121.41 0.0700 0.0282335 22.85 0.1400 0.0563250 85.85 0.0990 0.0406450 119.43 0.0990 0.0382335 99.99 0.0450 0.0166335 18.82 0.2251 0.090525% Utilization Levelutilization level. The results are shown in Table 4: Machining cost evaluated at 25% utilization level3 are the factors re presented in the last column of Table 4 are the responsewas used for developing the model. The software Design, machining cost is expressed in terms of the machining (f), and preheating temperature (θ). adequacy of the developed model. The avalue was used to examine the significance of the model, Various types of R2 were used to examine the HD Electricity Cost per Hour(RM/hr)0.11720.10720.13040.0840.1040.1040.1040.1040.1040.10080.11320.08760.12680.09720.1228.(variables) in modeling the .-Expert 6.0.8 dequacy was verified at its terms and prediction 580Elhadie, Karim, Amin, and Lajiscapability of the developed model. Higher values of R2 indicate that the model is capable of explaining higher percentages of variability in the response. The adequacy of the developed model was confirmed by comparing the actual and predicted values of cost.4.1 Formulation of the Mathematical ModelModel 1 was developed for estimating the cost of machining (RM per cm3) in preheated end-milling of AISI D2 tool steel at 25% utilization level using TiAlN coated carbide inserts. Log10(Machining Cost) = + 10.45787 - 0.38378 * v - 48.20697 * f -1.05058E-003 * θ + 6.50456E-003 * v2      + 132.10388 * f2+ 0.16574 * v * f - 2.10239E-005 * v * θ + 0.048919 * f * θ - 3.57881E-005 * v3Model 1The ANOVA output of Model 1 that is shown in Table 5 indicates that this Model is statistically significant and fitting for exploring the design space at 95% confidence interval.Table 5: ANOVA output for Model 1Source P-value (Prob > F) RemarkModel < 0.0001 SignificantTerm All the terms have P-values less than 0.05 SignificantLack of fit 0.1046 Not SignificantR-Squared 0.9995Adj. R-Squared 0.9987Pred. R-Squared N/AThe “Prob > F” values of the Model and its Lack-of-Fit, which are “< 0.0001” and 0.1046, respectively, prove that Model 1 is statistically significant and fitting for navigating the design space at 95% confidence interval. All the terms of the Model are significant at the 95% confidence interval as indicated by their “Prob > F” values which are all less than 0.05. The “R-squared” value of 0.9995 indicates that the model reasonably explains 99.95% of the variability of the machining cost.4.2 Adequacy ConfirmationThe adequacy of the developed model was confirmed by comparing the actual costs that have been obtained using the cost components equations with the predicted costs that have been obtained using the developed model. The results are shown in Table 6.Table 6: Adequacy confirmation for the developed modelRun No.Cutting Speed(m/min)Feed(mm/tooth)Preheating Temp.(◦C)Actual CM(RM/cm3)Predicted CM(RM/cm3)Error(%)1 72.28 0.025 413 64.0701 64.1008 0.052 72.28 0.079 273 31.7723 31.7877 0.053 44.27 0.079 413 30.4903 30.4998 0.034 44.27 0.025 273 92.1325 92.1592 0.035 56.57 0.044 335 39.5742 40.0080 1.106 56.57 0.044 335 40.4394 40.0080 1.077 56.57 0.044 335 40.6158 40.0080 1.508 56.57 0.044 335 40.1171 40.0080 0.279 56.57 0.044 335 40.2094 40.0080 0.5010 40.00 0.044 335 54.5961 54.6107 0.0311 80.00 0.044 335 38.4280 38.4485 0.0512 56.57 0.044 250 40.1136 40.6988 1.4613 56.57 0.044 450 38.6717 39.0920 1.0914 56.57 0.02 335 86.6384 86.6706 0.0415 56.57 0.1 335 25.7302 25.7403 0.04581Elhadie, Karim, Amin, and LajisAll the prediction errors made by the model, as shown in Table 6, are significantly less than 5%. This reasonably confirms the adequacy of the developed model as indicated by the ANOVA output.5.0 ConclusionIn this research paper, reliable mathematical model to estimate the cost of preheated end-milling of AISI D2 tool steel using TiAlN coated carbide inserts was developed. This model was developed based on 25% utilization level. The ANOVA outputs indicated that the developed model is statistically adequate and this was confirmed by the small prediction errors (less than 5%) that are made by the model. This model is quite reliable, however, it has to be used within the conditions that have been considered in developing it, such as the level of utilization, VMC initial expenses, operator’s salary, and so on. This model can be used successfully in cost reduction programs, process selection, and establishment of selling prices.References1. Lajis, M. A.; Mustafizul Karim, A. N.; Nurul Amin, A. K. M.; Hafiz, A. M. K.; & Turnad, L. G. (2008). Prediction of Tool Life in End Milling of Hardened Steel AISI D2. European Journal of Scientific Research, 21 (4), 592-602.2. Malstrom, E. M. (1984). Manufacturing Cost Engineering Handbook. Marcel Dekker, Inc. New York & Basel.3. Crease, R. C., Adithan, M., & Pabla, B. S. (1992). Estimating and Costing for the   Metal Manufacturing Industries. New York: MARCEL DEKKER, INC. 4. Toth, C. A. (2006). A Bottoms-up Approach to Cost Estimation using Parametric Inputs. Master Dissertation, College of Engineering and Technology of Ohio University.5. Dieter, G. E. (2000). Engineering Design: A Materials and Processing Approach (3rd edn.). Singapore: McGraw-Hill.6. Salvendy G. (2001). Handbook of Industrial Engineering. Canada: John Wiley & Sons, Inc.7. Boothroyd, G., & Knight, W. A. (1989). Fundamentals of Machining and Machine Tools (2nd edn.). New York: MARCEL DEKKER, INC.8. Lajis, M. A. (2009). Preheated Machining of Hardened Steel AISI D2 and Optimization of Parameters. Doctoral Dissertation, IIUM, Kuala Lumpur.9. Esawi, A. M. K. & Ashby, M. F. (1998, May). Cost-Based Ranking for Manufacturing Process Selection. Proceedings of the Second International Conference on Integrated Design and Manufacturing in Mechanical Engineering, 4, 1001-1008.10. Isakov, E. (2004). Engineering Formulas for Metal Cutting. New York: Industrial Press, Inc.11. JR., E. R. S. (1995). Precision Manufacturing Costing. New York: MARCEL DEKKER, INC.12. Sullivan, W. G., Bontadelli, J. A., & Wicks, E. M. (2000). Engineering Economy (11th edn.). New Jersy: Prentice-Hall, Inc.582

Multi fingered robot hand in industrial robot application using tele-operation

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