Document Type : Research Paper


Department of Industrial and Production Engineering, Military Institute of Science and Technology, Mirpur-1216, Dhaka, Bangladesh.


The development of a product demands numbers of consideration and customer-based product dominates the present market. This study aims to formulate a customer-oriented product and investigate the optimum design parameters level for this formulation. The customer-oriented product named 'CNC PCB Plotter' – is proposed as a handy tool to make a single PCB within a short time and cost. In the sophisticated art of product design, the desires of the customer should be the only constraint. With this in mind, an organized approach is conducted to formulate the product. Suitable design parameters with their optimum ranges provide the sustainability of the product. Response Surface Methodology (RSM) is applied to determine the optimum level of design parameters. A 2-level 3 factorial Central Composite Design (CCD) provides the experimental trails. This research involves the customer demand and specifies the design parameter, such as cutting speed, feed rate, and depth of cut. The average dimensional accuracy is taken as a response and found 0.027 µm with a combination of cutting speed 53.676 m/min, feed rate 253.272 mm/min, and depth of cut, which is found to be the optimum value.


Main Subjects

[1]     Kroes, P. (2009). Foundational issues of engineering design. In philosophy of technology and engineering sciences (pp. 513-541). North-Holland.
[2]     Huang, K., Guo, J., & Xu, Z. (2009). Recycling of waste printed circuit boards: a review of current technologies and treatment status in China. Journal of hazardous materials164(2-3), 399-408. DOI: 10.1016/j.jhazmat.2008.08.051
[3]     Keen, C., & Etemad, H. (2012). Rapid growth and rapid internationalization: the case of smaller enterprises from Canada. Management decision, 50(4), 569–590. DOI: 10.1108/00251741211220138
[4]     Bezerra, M. A., Santelli, R. E., Oliveira, E. P., Villar, L. S., & Escaleira, L. A. (2008). Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta76(5), 965-977. DOI: 10.1016/j.talanta.2008.05.019
[5]     Asghar, A., Abdul Raman, A. A., & Daud, W. M. A. W. (2014). A comparison of central composite design and Taguchi method for optimizing Fenton process. The scientific world journal.
[6]     Ozcelik, B., & Erzurumlu, T. (2006). Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm. Journal of materials processing technology171(3), 437-445. DOI: 10.1016/j.jmatprotec.2005.04.120
[7]     Lin, M. C., Wang, C. C., & Chen, T. C. (2006). A strategy for managing customer-oriented product design. Concurrent engineering14(3), 231-244.
[8]     Joung, J., Jung, K., Ko, S., & Kim, K. (2018). Customer complaints analysis using text mining and outcome-driven innovation method for market-oriented product development. Sustainability11(1), 1-14.
[9]     Sajjanit, C., & Rompho, N. (2019). Measuring customer-oriented product returns service performance. The international journal of logistics management. Int. J. Logist. Manag, 30(3), 772–796. DOI: 10.1108/IJLM-06-2018-0157
[10] Zheng, P., Xu, X., & Xie, S. Q. (2019). Correction to: a weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning. Journal of intelligent manufacturing30(1), 459-459.
[11] Guan, H., Alix, T., & Bourrieres, J. P. (2019). An integrated design framework for virtual enterprise-based customer-oriented product-service systems. Procedia CIRP83, 198-203. DOI: 10.1016/j.procir.2019.03.143
[12] Dou, R., Li, W., & Nan, G. (2019). An integrated approach for dynamic customer requirement identification for product development. Enterprise information systems, 13(4), 448-466. DOI: 10.1080/17517575.2018.1526321
[13] Zhai, L. Y., Khoo, L. P., & Zhong, Z. W. (2009). A dominance-based rough set approach to Kansei Engineering in product development. Expert systems with applications36(1), 393-402. DOI: 10.1016/j.eswa.2007.09.041
[14] Delgado‐Hernandez, D. J., Bampton, K. E., & Aspinwall, E. (2007). Quality function deployment in construction. Construction management and economics25(6), 597-609. DOI: 10.1080/01446190601139917
[15] Karsak, E. E., Sozer, S., & Alptekin, S. E. (2003). Product planning in quality function deployment using a combined analytic network process and goal programming approach. Computers and industrial engineering44(1), 171-190. DOI: 10.1016/S0360-8352(02)00191-2
[16] Hauser, J. R., & Clausing, D. (1996). The house of quality. IEEE engineering management review24(1), 24-32.
[17] Sivasamy, K., Arumugam, C., Devadasan, S. R., Murugesh, R., & Thilak, V. M. M. (2016). Advanced models of quality function deployment: a literature review. Quality and quantity50(3), 1399-1414. DOI: 10.1007/s11135-015-0212-2
[18] Rahman, M., Tahiduzzaman, M., & Dey, S. K. (2018). QFD based product design and development of weight measuring chair for the benefits of physically challenged person. American journal of industrial engineering, 5(1), 12-16.
[19] Kroll, E. (2013). Design theory and conceptual design: contrasting functional decomposition and morphology with parameter analysis. Research in engineering design, 24(2), 165-183. DOI: 10.1007/s00163-012-0149-6
[20] Kant, G., & Sangwan, K. S. (2014). Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. Journal of cleaner production83, 151-164. DOI: 10.1016/j.jclepro.2014.07.073
[21] Box, G. E., & Draper, N. R. (1987). Empirical model-building and response surfaces. John Wiley & Sons.
[22] Box, G. E., & Hunter, J. S. (1957). Multi-factor experimental designs for exploring response surfaces. The annals of mathematical statistics28(1), 195-241. DOI: 10.1214/aoms/1177707047
[23] Ginta, T. L., Amin, A. N., Radzi, H. M., & Lajis, M. A. (2009). Development of surface roughness models in end milling titanium alloy Ti-6Al-4V using uncoated tungsten carbide inserts. European journal of scientific research28(4), 542-551.
[24] Al Hazza, M. H. F., Adesta, E. Y., & Seder, A. M. (2015, December). Using soft computing methods as an effective tool in predicting surface roughness. 2015 4th international conference on advanced computer science applications and technologies (ACSAT) (pp. 9-13). IEEE. DOI: 10.1109/ACSAT.2015.17
[25] Krishnaraj, V., Samsudeensadham, S., Sindhumathi, R., & Kuppan, P. (2014). A study on high speed end milling of titanium alloy. Procedia engineering97, 251-257. DOI: 10.1016/j.proeng.2014.12.248