In the present work, the minimization of total manufacturing cost of an assembly considering tolerance allocation using various optimization algorithms have been addressed. Complex assembly which consists of more than one critical dimension with any one dimension of the part involved in more than one dimensional chain has been considered in this work. The manufacturing cost of the various parts of an assembly with tolerance allocation has been minimized using the cost tolerance model. The benchmark solution to the problem has been derived using Lagrange Multiplier (LM) method. The results are improved using Genetic Algorithm (GA) and Artificial Bee Colony (ABC) algorithm by considering the Alternate Nominal Dimension Selection (ANDS). The results have been compared with LM method and found GA outperformed ABC algorithm. Further the research has been extended by considering ANDS and Alternate Process Selection (APS) using Teaching-Learning Based Optimization (TLBO) algorithm and the results have been compared with benchmark solutions. The proposed method is suitable for any type of complex assembly. An Improved Knuckle Joint (IKJ) assembly has been chosen for the illustration of proposed methodology with an implementation strategy. From the results, it was noted that TLBO yielded better results in the aspect of minimizing the total manufacturing cost of IKJ assemblies.
ChaseKWGreenwoodWHLoosliBG, et al. Least cost tolerance for mechanical assemblies with automated process selection. Manuf Rev1990; 3(1): 49–59.
2.
LiuSJinQDongY, et al. A closed-form method for statistical tolerance allocation considering quality loss and different kinds of manufacturing cost functions. Int J Adv Manuf Technol2017; 93(5–8): 2801–2811.
3.
MarouaGMehdiTNizarA. Optimal tolerance allocation based on difficulty matrix using FMECA tool. Procedia CIRP2018; 70: 132–137.
4.
ChaseKW. Minimum cost tolerance allocation. Brigham Young University. ADCATS Report no 99–5, 1999.
5.
GreenwoodWHChaseKW. Root sum squares tolerance analysis with nonlinear problems. Trans ASME J. Eng Ind1990; 112: 382–384.
6.
ZhouZHuangWZhangL. Sequential algorithm based on number theoretic method for statistical tolerance analysis and synthesis. Trans ASME J Manuf Sci Eng2001: 123: 490–493.
7.
TeeravaraprugJ. Deterministic tolerance synthesis with a consideration of nominal values. Thammasat Int J Sci Technol2002; 7(l): 50–55.
8.
Siva KumarMKannanSMJayabalanV. Construction of closed-form equations and graphical representation for optimal tolerance allocation. Int J Prod Res2007; 45(6): 1449–1468.
9.
SinghPKJainPKJainSC. Important issues in tolerance design of mechanical assemblies. Part 1: tolerance analysis. Proc IMechE, Part B: J Engineering Manufacture2009; 223(10): 1225–1247.
10.
SinghPKJainPKJainSC. Important issues in tolerance design of mechanical assemblies. Part 2: Tolerance analysis. Proc IMechE, Part B: J Engineering Manufacture2009; 223(10): 1249–1287.
11.
PrabhaharanGAsokanPRajendranS. Sensitivity-based conceptual design and tolerance allocation using the continuous ant’s colony algorithm (CACO). Int J Adv Manuf Technol2005; 25(5): 516–526.
12.
ForouraghiB. Optimal tolerance allocation using a multi objective particle swarm optimizer. Int J Adv Manuf Technol2009; 44(7): 710–724.
13.
Siva KumarMKannanSMJayabalanV. A new algorithm for optimum tolerance allocation of complex assemblies with alternative processes selection. Int J Adv Manuf Technol2009; 40: 819–836.
14.
Sha XuSXingYChenW. Multi-objective optimization based on improved non-dominated sorting genetic algorithm II for tolerance allocation of auto-body parts. Adv Mech Eng2017; 9(9): 1–9.
15.
KhodayganSHemati-NikJ. Multi-objective optimal tolerance allocation of the mechanical systems under the thermal gradients. SAE technical paper 2018-01-1031, 2018.
16.
SinghPKJainSCJainPK. A GA-based solution to optimum tolerance synthesis of mechanical assemblies with alternative manufacturing processes bench marking with the exhaustive search method using Lagrange multiplier. Proc IMechE, Part B: J Engineering Manufucture2004; 218: 765–778.
17.
PrabhaharanGAsokanPRameshPRajendranS. Genetic-algorithm-based optimal tolerance allocation using a least-cost model. Int J Adv Manuf Technol2004; 24(9): 647–660.
18.
Vignesh KumarDRavindranDSiva KumarM, et al. Optimum tolerance synthesis of simple assemblies with nominal dimension selection using genetic algorithm. Proc IMechE, Part C: J Mechanical Engineering Science2016; 230(19): 3488–3508.
19.
Siva KumarMKannanSM. Optimum manufacturing tolerance to selective assembly technique for different assembly specifications by using genetic algorithm. Int J Adv Manuf Technol2007; 32(5): 591–598.
20.
KarabogaDAkayB. A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Appl Soft Comput2011; 11(3): 3021–3031.
LiuJZhuHMaQ, et al. An artificial bee colony algorithm with guide of global & local optimal and asynchronous scaling factors for numerical optimization. Appl Soft Comput2015; 37: 608–618.
23.
Vignesh KumarDRavindranDLeninN, et al. Alternative nominal dimension selection of complex assembly using artificial bee colony (ABC) algorithm. Proc IMechE, Part C: J Mechanical Engineering Science2019; 233(1): 18–38.
24.
RaoRV. Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decis Sci Lett2016; 5(1): 1–30.
25.
BaykasogluAHamzadayiAKoseSY. Testing the performance of teaching learning based optimization (TLBO) algorithm on combinatorial problems: flow shop and job shop scheduling cases. Inf Sci2014; 276: 204–218.
26.
RaoRVSavsaniVJVakhariaDP. Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Int J Comput Aided Des2011; 43(3): 303–315.
27.
RaoRVMoreKC. Advanced optimal tolerance design of machine elements using teaching-learning-based optimization algorithm. Int J Prod Manuf Res2014; 2(1): 71–94.
28.
CrepinsekaMLiubS-HMernikcL. A note on teaching–learning-based optimization algorithm. Inf Sci2012; 212: 79–93.
29.
Venkata RaoRKalyankarVD. Multi-pass turning process parameter optimization using teaching–learning-based optimization algorithm. Sci Iran, Trans E: Ind Eng2013; 20(3): 967–974.
30.
GhaderiAHassaniHKhodayganS. A Bayesian-reliability based multi-objective optimization for tolerance design of mechanical assemblies. Reliab Eng Syst Saf2021; 213: 107748.
31.
Venkata RaoRMoreKC. Simultaneous optimal selection of design and manufacturing tolerances with different stack-up conditions using TLBO algorithm. In: International conference on innovations in engineering and technology (ICIET), Bangkok, Thailand, 2013, pp.25–26.
32.
SatapathySCNaikAParvathiK. A teaching learning based optimization based on orthogonal design for solving global optimization problems. Springer Plus2013; 2(1): 130.
33.
Venkata RaoRPatelV. An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Sci Iran2013; 20(3): 710–720.
34.
Venkata RaoRWaghmareGG. A comparative study of a teaching–learning-based optimization algorithm on multi-objective unconstrained and constrained functions. J King Saud Univ – Comput Inf Sci2014; 26(3): 332–346.
35.
Alper SofuogluMHayati CakirFGurgenS. An efficient approach by adjusting bounds for heuristic optimization algorithms. Soft Comput2019; 23: 5199–5212.
36.
GurgenSHayati CakirFAlper SofuogluM, et al. Multi-criteria decision-making analysis of different non-traditional machining operations of Ti6Al4V. Soft Comput2019; 23: 5259–5272.
37.
GurgenSAlper SofuogluMHayati CakirF, et al. Multi response optimization of turning operation with self-propelled rotary tool. Procedia – Social Behav Sci2015; 195: 2592–2600.
38.
ZhangCDongHWangD, et al. Tolerance synthesis for lost motion requirement of planetary gear train based on a mechanism model. Mech Mach Theory2021; 164(1): 104405.
