Concurrent multi-response optimization of austenitic stainless steel surface roughness driven by embedded lean and green indicators

Abstract International ecodesign initiatives encourage drilling process improvements that are also attentive to energy and peripheral materials consumption. A stainless-steel heat-exchanger component is optimized for surface quality during drilling while taking into account lean-and-green improvements. A non-linear four-factor fractional factorial scheme has been utilized to investigate the surface roughness performance due to: 1) the feed rate, 2) the spindle speed, 3) the twist drill type and 4) the coolant concentration. We address simultaneously the cumulative effect of multiple consecutive drillings on the surface quality of the last machined hole of a tube-sheet test plate. The concurrent optimization effort regulated the weighted surface quality performance of the drilling process by suppressing consumptions for: 1) energy – monitored at the final drilling and 2) coolant fluid. The weighted distribution-free optimization scheme was repeated to compound information from a key lean process indicator, the drilling processing time. It was found that the feed rate (at 70 mm/min) and the spindle speed (at 350 rpm) commonly influence non-linearly the surface roughness response (predicted at 1.05 μm) in both multi-response scenarios. The drill type (Guhring No. 8520) was also a statistically important effect when excluding optimality with respect to improved lean and green process performance. In such a case, the spindle speed needed to be decreased to 300 rpm for a predicted surface roughness of 0.86 μm (confirmed within 3%).

[1]  George J. Besseris,et al.  Non‐linear nonparametric quality screening in low sampling testing , 2010 .

[2]  Genichi Taguchi,et al.  Taguchi's Quality Engineering Handbook , 2004 .

[3]  Lin Li,et al.  Multi-objective optimization of milling parameters – the trade-offs between energy, production rate and cutting quality , 2013 .

[4]  Ahmed M. Deif,et al.  A system model for green manufacturing , 2011 .

[5]  Gil Friend.  The Truth About Green Business , 2009 .

[6]  Peter Hines,et al.  Creating a Lean and Green Business System: Techniques for Improving Profits and Sustainability , 2013 .

[7]  George J. Besseris,et al.  Design of experiments and environmental improvement: Applying a six sigma toolset , 2010 .

[8]  Nadya Zhexembayeva,et al.  Embedded Sustainability: The Next Big Competitive Advantage , 2011 .

[9]  Hari Singh,et al.  Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi's technique—A comparative analysis , 2008 .

[10]  Lonnie Wilson,et al.  How to Implement Lean Manufacturing , 2009 .

[11]  R. F. Ávila,et al.  Environmentally friendly manufacturing: Behavior analysis of minimum quantity of lubricant - MQL in grinding process , 2013 .

[12]  B. Tabachnick,et al.  Using Multivariate Statistics , 1983 .

[13]  Sujit Das,et al.  Making progress towards more sustainable societies through lean and green initiatives , 2012 .

[14]  Lieh-Dai Yang,et al.  Machining characteristic study of friction drilling on AISI 304 stainless steel , 2008 .

[15]  Bilgin Tolga Simsek,et al.  Optimization of cutting fluids and cutting parameters during end milling by using D-optimal design of experiments , 2013 .

[16]  B. Bhushan,et al.  Role of Fractal Geometry in Roughness Characterization and Contact Mechanics of Surfaces , 1990 .

[17]  Mi Sandar Mon,et al.  Heat Exchanger Design , 2008 .

[18]  Aldo Roberto Ometto,et al.  Quality tools applied to Cleaner Production programs: a first approach toward a new methodology , 2013 .

[19]  Rich Charron,et al.  The Lean Six Sigma Black Belt Handbook: Tools and Methods for Process Acceleration , 2013 .

[20]  Sophie Hallstedt,et al.  Key elements for implementing a strategic sustainability perspective in the product innovation process , 2013 .

[21]  Carmita Camposeco-Negrete,et al.  Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA , 2013 .

[22]  George J. Besseris,et al.  Applying the DOE toolkit on a Lean‐and‐Green Six Sigma Maritime‐Operation Improvement Project , 2011 .

[23]  Henrikke Baumann,et al.  Mapping the green product development field: engineering, policy and business perspectives , 2002 .

[24]  Paul Mativenga,et al.  Sustainable machining: selection of optimum turning conditions based on minimum energy considerations , 2010 .

[25]  Rajesh Kumar Bhushan,et al.  Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites , 2013 .

[26]  Imtiaz Ahmed Choudhury,et al.  A critical assessment of lubrication techniques in machining processes: a case for minimum quantity lubrication using vegetable oil-based lubricant , 2013 .

[27]  Ming Zhou,et al.  Selection and evaluation of green production strategies: analytic and simulation models , 2012 .

[28]  George J. Besseris,et al.  Multi-response multi-factorial master ranking in non-linear replicated-saturated DOE for qualimetrics , 2012 .

[29]  Kim Hua Tan,et al.  Green as the new Lean: how to use Lean practices as a catalyst to greening your supply chain , 2013 .

[30]  Irad Ben-Gal,et al.  Robust eco-design: A new application for air quality engineering , 2008 .

[31]  Thomas Pyzdek,et al.  The Six Sigma Handbook , 2000 .

[32]  George J. Besseris,et al.  Characterization and comparative machinability investigation of extruded and drawn copper alloys using non-parametric multi-response optimization and orthogonal arrays , 2011 .

[33]  George J. Besseris,et al.  Eco‐design in total environmental quality management , 2012 .

[34]  Jeroen de Mast,et al.  An analysis of the Six Sigma DMAIC method from the perspective of problem solving , 2012 .

[35]  C. Roques-carmes,et al.  Fractal approach to two-dimensional and three-dimensional surface roughness , 1986 .

[36]  Babur Ozcelik,et al.  Evaluation of vegetable based cutting fluids with extreme pressure and cutting parameters in turning of AISI 304L by Taguchi method , 2011 .

[37]  D. Joanes,et al.  Comparing measures of sample skewness and kurtosis , 1998 .

[38]  Ming-Chang Jeng,et al.  Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis , 2009 .

[39]  R. Domingo,et al.  Model of efficient and sustainable improvements in a lean production system through processes of environmental innovation , 2013 .

[40]  Peter Krajnik,et al.  Transitioning to sustainable production – Part I: application on machining technologies , 2010 .

[41]  M. G. Bulmer,et al.  Principles of Statistics. , 1969 .

[42]  George J. Besseris,et al.  Profiling effects in industrial data mining by non-parametric DOE methods: An application on screening checkweighing systems in packaging operations , 2012, Eur. J. Oper. Res..

[43]  Janez Kopac,et al.  Environmental management inside production systems , 2005 .

[44]  Cristian Caizar,et al.  Application of Taguchi method to selection of optimal lubrication and cutting conditions in face milling of AlMg3 , 2011 .