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Sustainable Manufacturing: Optimization of single pass Turning machining operation using Meta-heuristic algorithm

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Main aim is to optimize a manufacturing process by using different Meta-heuristic algorithm. i had selected turning process here. Firstly i found the most influential parameters in turning process by introducing "Local-centrality Method". Optimization of these most influential parameters will lead to the optimization of whole process using "Genetic algorithm and Taguchi Method". Genetic algorithm has been used to optimize production rate & production cost and Taguchi method has been used to optimize cutting quality, which has been described in presentation.

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Sustainable Manufacturing: Optimization of single pass Turning machining operation using Meta-heuristic algorithm

  1. 1. Project Guide :- Submitted by:- Mr. Amit Kumar Sinha Sajal Dixit (2012EME05) (Asst. Professor, DME) Senior undergraduate student Department of Mechanical Engineering SMVDU, J&K, India e-mail : sajaldixit07@gmail.com
  2. 2. Sustainable Manufacturing Manufacturing Sustainable manufacturing Mechanical, physical and chemical process to modify the geometry, properties and appearance of a given starting material in the making of new finished part of product The creation of manufactured products that use processes that are non-polluting, conserve energy and natural resources, and are economically sound and safe for employees, communities, and consumers.”
  3. 3. Turning Process • A very important machining process, in which a single point cutting tool removes unwanted material from the surface of a rotating cylindrical work piece
  4. 4. Need of optimization and decision making in Manufacturing Necessary for efficient and optimal use of manufacturing industries To increase productivity and reduce operational cost Manufactures can reduce downtime and can increase the value of products in the Global market. To increase Quality of Manufactured Products
  5. 5. Parameters which effects sustainable manufacturing in turning operation A. Machining parameters – C. Environmental parameters- (1) Cutting Speed (CS) (1) Water Intensity (WI) (2) Tool Life (TL) (2) Energy Intensity (EI) (3) Feed (F) (3) Materials (M) (4) Depth of cut (DOC) (5) Cutting force (CF) B. Economical Parameters- (1) Cutting Quality (CQ) (2) Production Cost (PC) (3) Production Rate (PR)
  6. 6. Graph Theory and Matrix Approach as a Decision making Method A graph G = (V, E) consists of a set of objects V = {v1, v2, ….} called vertices or nodes, and another set E = {e1, e2, ….}, of which the elements are called edges, such that each edge ek is identified with a pair of vertices. The most common representation of a graph is by means of a diagram, in which the vertices are represented by small points or circles, and each edge represent the inter-relation between two end vertices. Fig 1
  7. 7. Graphical relationship and complex structure of different parameters
  8. 8. Table 1 . inter-relationship table between different parameters aNeeraj Bhanota*, P. Venkateswara Raoa, S.G. Deshmukha, Sustainable Manufacturing: An Interaction Analysis for Machining Parameters Parameter CS TL F DOC CF PC C.Q. PR WI EI ML Cutting speed(CS) 0 1 1 1 0 1 0 1 0 0 1 Tool Life (TL) 1 0 1 1 0 1 1 1 1 0 1 Feed rate(F) 1 0 0 0 1 1 1 1 0 0 1 Depth of cut(DOC) 1 1 0 0 1 1 1 1 0 0 0 Cutting force(CF) 1 0 1 1 0 0 0 1 0 1 1 Production cost(PC)a 1 1 1 1 1 0 1 1 0 1 1 Cutting Quality (C.Q.) 1 1 1 1 0 1 0 1 1 1 1 Production Rate (PR) 1 1 1 1 1 1 1 0 1 1 1 Water intensity(WI) 1 1 0 0 0 0 1 0 0 1 1 Energy intensity(EI)a 1 0 0 0 1 1 1 1 0 0 1 Materials (ML)a 1 1 0 0 1 1 1 1 1 1 0
  9. 9. Finding of most Influential parameters Method :- Local Centrality Method local centrality measure as a tradeoff between low-relevant degree centrality and other time-consuming measures. It is a part of graph theory. It considers nearest neighbors only. The local centrality CL(v) of node v is defined as– 𝑄 𝑢 = 𝑤∈Γ𝑢 𝑁(𝑤) …eq.(1) 𝐶 𝐿 𝑣 = 𝑢∈𝜇(𝑣) 𝑄(𝑢) …eq.(2) [where Γ (u) and µ(v) is the set of the nearest neighbors of node u and v respectively . N(w) is the number of the nearest neighbor of node w.]
  10. 10. As example, feed(Node 1) has six nearest neighbors and thus N(1) = 6. The values of N(w) for the nodes in Fig. 2 are presented in the second column of Table 2. According to Eq. (1), Q(1) = N(2) + N(3) + N(4) + N(5) + N(6) + N(7) + N(8) + N(9)+N(10)+N(11) = 48. Similarly, we can obtain the values of Q for the rest nodes which are shown in the second column of Table 2. Finally, according to Eq. (2), the local centrality of node 1 is equal to the sum of Q over all the nearest neighbors of node 1, namely CL(1) = Q(2)+Q(3)+Q(4)+Q(5)+Q(6)+Q(7)+Q(8)+Q(9)+Q(10)+Q(11) = 346.
  11. 11. S.NO PARAMETER N Q CL Contribution (%) 1 Feed (F) 6 48 346 7.98 2 Depth of Cut (DOC) 6 48 346 7.98 3 Cutting Force (CF) 6 42 319 7.36 4 Cutting Quality (CQ) 9 64 480 11.08 5 Production Cost (PC) 9 65 484 11.17 6 Production Rate (PR) 10 69 517 11.94 7 Water Intensity (WI) 5 37 277 6.39 8 Energy Intensity (EI) 6 48 346 7.98 9 Cutting Speed (CS) 6 47 348 8.03 10 Tool Life (T.L.) 8 59 437 10.09 11 Material (ML) 8 59 431 9.95 Table 2. Centrality measures for node influences
  12. 12. Meta-heuristic algorithm • In mathematical optimization, a metaheuristic is a higher-level procedure or algorithm which is use to provide a sufficiently good solution of an optimization problem • Metaheuristics are strategies that guide the search process. • In Meta-heuristic algorithms search space is very high to find an optimum solution that’s why it is very efficient. • Genetic Algorithm, Ant colony algorithm (ACO), Taguchi Method are some of the examples of Meta-heuristic procedure.
  13. 13. Optimization of Parameters on the basis of their higher contribution Cutting Quality Product- ion rate Product- ion cost Making of Objective function Design Optimization by TAGUCHI method Optimization by Genetic Algorithm tool box using MATLAB 11.08 % 11.94 % 11.17 %
  14. 14. Taguchi Method :- Optimization of Cutting Quality • Taguchi method is a powerful tool for the design of high quality system. • It provides a system efficient and systematic approach to optimize designs for performance, quality and cost. • Here we are using Taguchi method to optimize cutting quality in the single pass turning operation. •The experimental details of using Taguchi method to determine and analyze of optimum cutting parameter with regard to performance index such as Tool Life which is described below. Process Variable Response Variable Cutting speed Feed Tool Life Depth of cut
  15. 15. Single pass turning operation is performed on mild steel work piece material using high speed steel (HSS) cutting tool. Table-3 : Cutting Parameter and their levels a selected initial cutting parameters. Symbol Cutting Parameter Unit Level 1 Level 2 Level 3 A Cutting Speed m/min 100 125a 150 B Feed mm/rev 0.2 0.3a 0.4 C Depth of cut mm 1.0 1.5a 2.0
  16. 16. Table 4: Experimental Layout using an L27 orthogonal array. Experiment No. Cutting Parameter Level A B C Cutting speed Feed rate Depth of cut 1 1 1 1 2 1 1 2 3 1 1 3 4 1 2 1 5 1 2 2 6 1 2 3 7 1 3 1 8 1 3 2 9 1 3 3 10 2 1 1 11 2 1 2 12 2 1 3 13 2 2 1 14 2 2 2 15 2 2 3 16 2 3 1 17 2 3 2 18 2 3 3 19 3 1 1 20 3 1 2 21 3 1 3 22 3 2 1 23 3 2 2 24 3 2 3 25 3 3 1 26 3 3 2 27 3 3 3
  17. 17. Table 5: Results for Tool life and S/N ratio S. NO. Cutting Speed(m/min) Feed(mm/rev) Depth of cut(mm) Tool life(min) S/N ratio (dB) 1. 100 0.2 1.0 758 57.60 2. 100 0.2 1.5 560 53.07 3. 100 0.2 2.0 451 50.06 4. 100 0.3 1.0 506 48.60 5. 100 0.3 1.5 372 46.78 6. 100 0.3 2.0 301 44.94 7. 100 0.4 1.0 379 44.09 8. 100 0.4 1.5 280 42.86 9. 100 0.4 2.0 225 41.45 10. 125 0.2 1.0 248 40.56 11. 125 0.2 1.5 183 39.29 12. 125 0.2 2.0 147 37.85 13. 125 0.3 1.0 166 36.98 14. 125 0.3 1.5 122 35.73 15. 125 0.3 2.0 99 34.33 16. 125 0.4 1.0 125 33.03 17. 125 0.4 1.5 92 32.10 18. 125 0.4 2.0 74 30.97 19. 150 0.2 1.0 100 30.46 20. 150 0.2 1.5 73 29.64 21. 150 0.2 2.0 59 28.60 22. 150 0.3 1.0 67 27.90 23. 150 0.3 1.5 49 26.90 24. 150 0.3 2.0 40 25.78 25. 150 0.4 1.0 49 24.34 26. 150 0.4 1.5 37 23.55 27. 150 0.4 2.0 30 22.58
  18. 18. SYMBOL PARAMETER Mean S/N Ratio Level 1 Level 2 Level 3 Max.-Min. A Cutting Speed 47.71 35.68 26.65 21.05 B feed 40.79 36.45 32.81 7.98 C Depth of Cut 38.18 36.69 35.17 3.005 Total Mean S/N Ratio = 36.68 dB Table 6 : S/N response table for tool life Extended Taylor tool life Equation :- 𝑉𝑇 𝑛 𝑓 𝑎 𝑑 𝑏 = 𝐶 n = 0.20 C = 273 for Mild Steel work piece material a = 0.20 - For HSS tool material b = 0.15 Optimal Cutting Parameter – A1B1C1
  19. 19. SYMBOL Cutting Parameter Degree of freedom Sum of Squares Mean Square Contribution(%) A Cutting Speed 2 669.78 334.89 85.88% B Feed rate 2 95.76 47.88 12.27% C Depth of cut 2 14.34 7.17 1.83% Table 7 :Result Of the Analysis Of Variance For Tool Life Sum of Squares SSTR = 𝑖=1 3 𝑟𝑖(𝑒𝑖 − ē) ri –Number of levels ē – Mean S/N ratio of parameter ei - S/N ratio of particular level Mean Square deviations MSD = SSTR/(N-1)
  20. 20. Initial Cutting parameters Optimal cutting parameters level A2B2C2 A1B1C1 S/N Ratio 36.27 42.22 Improvement in S/N Ratio 5.95 dB Table 8 :Result of Configuration on Experiment of Tool Life S/N ratio – Signal to Noise ratio (λ) Signal represent the desirable value for output characteristics and noise represent the undesirable value for output characteristics. It is the ratio of the mean to the standard deviation (SD). λ = −10 log 𝑀. 𝑆. 𝐷 Here 𝑀. 𝑆. 𝐷 = 1 𝑚 ⅀ 1 𝑇𝑖 2 M.S.D = Mean Square Deviation m= number of test, Ti is the value of tool life at ith test
  21. 21. Optimization of Production rate and Production cost using Genetic algorithm MAKING OF OBJECTIVE FUNCTION 1. Max. Production rate or min. Production time: aims to maximize number of parts produced in a unit time interval or minimizes the time per unit part. Neglects cost and/or profit. . Unit production time, t(min/pt): time to manufacture a unit of the product. t = ts + tm+ tr(tm/T)
  22. 22. Where, ts= setup time (min/pt); includes time needed to load/unload parts into machine, tool setup time, etc. tm= machining time (min/pt) . tr= total tool replacement time (min/pt) . T= tool life (min). • Production rate= (1/t) = 1/ {ts+ tm+ tr(tm/T)}
  23. 23. • Unit production cost, u(Rs/pt): cost to manufacture a unit of the product. c = c1+ c3+ c4= kots+ kotm + [kt + kotr] (tm/T). Where, c1= capacity utilization cost (Rs/pt); includes machine cost, labor cost, overhead etc. ko= machine utilization rate (Rs/min) c3= machining cost (associated with actual machining time); includes cost of electricity, cutting fluids etc. km= machining overhead (Rs/min). c4= tool utilization cost; includes cost of cutting tool, tool re sharpening, etc. kt= cost per cutting edge (Rs/edge).
  24. 24. Machining time (tm)= (3.14*D*L)/(1000*v*f) where, D : Diameter of Workpiece(mm) L : Length of turning(mm) v : Cutting Speed(m/min.) f : feed(mm/rev) Taylor’s Tool Life Equation: v*Tp*fq*dr = Ct where, T : Tool life (min.) d: depth of cut (mm.) Ct, p, q, r: Emperical constants
  25. 25. Objective function with constraints 1. 𝑐 = 𝑐1 + 𝑐2 ∗ 𝑣 − 1 ∗ 𝑓 − 1 + π𝐷𝐿 ∗ 𝑑 𝑟 𝑝 ∗ 𝑣 1 − 𝑃 𝑝 ∗ 𝑓 𝑞 − 𝑝 𝑝 1000∗𝑐𝑡 1 𝑝 Where, C1 = K0*ts C2 = π*D*L*K0/1000 2. 𝑃𝑅 = 1/{𝑡𝑠 + 𝜋𝐷𝐿 1000∗𝑣∗𝑓 + 𝑡𝑟[(𝜋𝐷𝐿 ∗ 𝑣 1 − 𝑝 𝑝 ∗ 𝑓 𝑞 − 𝑝 𝑝 ∗ 𝑑 𝑟 𝑝 ]/ 1000 ∗ 𝑐𝑡 1 𝑝 Following to:- 1. vmin ≤ v ≤ vmax 2. fmin ≤ f ≤ fmax 3. Pm ≥ Cn*v*d*fx 4. Qu ≥ k2*vw*fy*dz
  26. 26. Practical Data for HSS tool material for 0 degree rake angle :- a) k0=30 Rs./min. J) K2=132 b) ts = 2 min. k) w = 0.4 c) tr = 3 min. l) y = 0.2 d) Kt = 120 Rs m) z = 0.105 e) Taylor’s Tool life constant: i) Ct = 273 ii) p = 0.20 iii) q = 0.20 iv) r =0.15 f) D = 50 mm g) L = 120 mm h) d = 6 mm i) Qu = 873 K j) Pm = 20 KW k) Cn =9772.3722 l) x = 1.15
  27. 27. • After putting all the values given above, final objective function with constant values and constraints :- Production cost C = 60 + 565.487/(v*f) + 9.3786*10-9*v4 Production rate: PR = 1/(2 + (18.84/v*f) + 1.34*10-10*v4) Constraints :- 1. Minimal Cutting Speed 2. Maximum Cutting Speed V ≥ 50 m/min V ≤ 500 m/min 3 Minimum feed 4. Maximum feed f ≥0.1 mm/rev. f ≤ 2 mm/rev 5. Power force constraint 6. Chip tool interface temperature const. v*f1.15 ≤ 341.097 v0.4*f0.2 ≤ 11.317
  28. 28. Solution of objective function by genetic algorithm tool box using MATLAB Code for the Maximization of Production Rate: function P = production_rate(x) P = -(1/(2+18.84/(x(1)*x(2))+1.34*10^-10*x(1)^4.0)); end Code for the Minimization of Production Cost: function C = Production_cost(x) C = 60+565.487/(x(1)*x(2))+9.3786*10^-9*x(1)^4.0; end
  29. 29. Code for the Inequality Constraints: function [T, ceq] = nonlinear_constraints(x) T = [x(1)*x(2)^1.15-341.097; x(1)^0.4*x(2)^0.2-11.317]; ceq = []; end [The above code will be applied on both the basic optimization function] Main code to find Optimum points for Production rate and Production cost ObjFcn = @production_rate/@production_cost; nvars = 2; LB = [50,0.1]; UB = [500,2.0]; ConsFcn = @nonlinear_constraints [x, fval] = ga(ObjFcn, nvars, [], [], [], [], LB, UB, ConsFcn);
  30. 30. Result of optimization using Genetic algorithm After running main code and Genetic algorithm tool box in MATLAB for 5 iterations, it gives following result : For Production rate – Optimized value of production rate = 0.4750160018531908 pt/time Optimum points where production rate is maximum : x(1)= 111.9379 m/min, x(2)= 2.0 mm/rev For Production cost – Optimized value of production cost = 63.739932123648494 Rs./pt Optimum points where production cost is minimum : x(1)= 94.5015 m/min, x(2)= 2.0 mm/rev
  31. 31. Screen-shots of Genetic algorithm tool box and main code in MATLAB

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