Decision-making for multi-criteria optimization of process planning

The objective of this work is to develop a methodology for the automatic generation of optimised and innovative machining process planning that enable aeronautical subcontractors to face current productivity and competitiveness issues. A four-step methodology is proposed, allowing the user to obtain optimised machining ranges that respect his know-how and experience and introduce innovation. This methodology is based on a representation of the decisional behaviour of the user in a given situation as well as in the face of the risk of industrialisation and broadens the formalisation of the performance of a process by taking into account other performance criteria other than machining time or overall cost. A genetic algorithm is used to generate optimized process planning. An AHP method is used to represent the decision-making process. The methodology presents the best processes generated and the use of social choice theory enables it to target the most efficient ranges to be implemented, by integrating a risk criterion to the industrialization.

[1]  Jeffrey D. Tew,et al.  Simulation optimization by genetic search , 1994 .

[2]  Berend Denkena,et al.  Knowledge Management in Process Planning , 2007 .

[3]  Ramy Harik.  Spécifications de fonctions pour un système d'aide à la génération automatique de gamme d'usinage : Application aux pièces aéronautiques de structure, prototype logiciel dans le cadre du projet RNTL USIQUICK , 2007 .

[4]  Asif Iqbal,et al.  A fuzzy expert system for optimizing parameters and predicting performance measures in hard-milling process , 2007, Expert Syst. Appl..

[5]  F. Echeverri,et al.  New promising Euphorbiaceae extracts with activity in human lymphocytes from primary cell cultures , 2011, Immunopharmacology and immunotoxicology.

[6]  Sami Hassini.  Qualification multi-critères des gammes d'usinage : application aux pièces de structure aéronautique en alliage Airware® , 2015 .

[7]  F. W. Taylor.  The Art of Cutting Metals , 1907 .

[8]  Hong-Chao Zhang,et al.  Computer Aided Process Planning: the state-of-the-art survey , 1989 .

[9]  Türkay Dereli,et al.  Optimisation of process planning functions by genetic algorithms , 1999 .

[10]  D. Dubois,et al.  Concepts et méthodes pour l'aide à la décision. 3, analyse multicritère , 2006 .

[11]  Wei Wang,et al.  A feature-based method for NC machining time estimation , 2013 .

[12]  Dimitris Kiritsis,et al.  Petri net techniques for process planning cost estimation , 1999 .

[13]  Thomas R. Kurfess,et al.  Cost optimization and experimental design in milling using surrogate models and value of information , 2015 .

[14]  B. J. Davies,et al.  An interactive process planning system for prismatic parts (ICAPP) , 1981 .

[15]  Xun Xu,et al.  Computer-aided process planning – A critical review of recent developments and future trends , 2011, Int. J. Comput. Integr. Manuf..

[16]  Ruxu Du,et al.  A fuzzy expert system for the design of machining operations , 1995 .

[17]  J. Vivancos,et al.  Optimal machining parameters selection in high speed milling of hardened steels for injection moulds , 2004 .

[18]  Colin R. Reeves,et al.  A genetic algorithm for flowshop sequencing , 1995, Comput. Oper. Res..

[19]  Leonardo Ensslin,et al.  Decision Support Systems in action: Integrated application in a multicriteria decision aid process , 1999, Eur. J. Oper. Res..

[20]  Eun Young Heo,et al.  Estimation of NC machining time using NC block distribution for sculptured surface machining , 2006 .

[21]  Gülçin Büyüközkan,et al.  A fuzzy optimization model for QFD planning process using analytic network approach , 2006, Eur. J. Oper. Res..

[22]  D. Dubois,et al.  Concepts et méthodes pour l'aide à la décision. 1, outils de modélisation , 2006 .

[23]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[24]  R. W. Saaty,et al.  The analytic hierarchy process—what it is and how it is used , 1987 .

[25]  H.J.J. Kals,et al.  XPLANE, a Generative Computer Aided Process Planning System for Part Manufacturing , 1986 .

[26]  Vincent Pateloup.  Amélioration du comportement cinématique des machines outils UGV , 2005 .

[27]  Shee-Hock Yeo.  Knowledge-based feature recognizer for machining , 1994 .

[28]  Weiming Shen,et al.  Applications of agent-based systems in intelligent manufacturing: An updated review , 2006, Adv. Eng. Informatics.

[29]  James A. Reggia,et al.  A Comparative Analysis of Methods for Expert Systems , 1986, Int. J. Man Mach. Stud..

[30]  K. Ridgway,et al.  Selection of cutting tools and conditions of machining operations using an expert system , 2000 .

[31]  Wen Wang,et al.  Design of neural network-based estimator for tool wear modeling in hard turning , 2008, J. Intell. Manuf..

[32]  Tony L. Schmitz,et al.  Application of decision analysis to milling profit maximisation: an introduction , 2009 .

[33]  Yusuf Altintas,et al.  Prediction of part machining cycle times via virtual CNC , 2015 .

[34]  F. L. Krause.  Technological planning systems for the future , 1990 .

[35]  Lihui Wang,et al.  A review of function blocks for process planning and control of manufacturing equipment , 2012 .

[36]  Andrea C. Hupman,et al.  Incentives versus value in manufacturing systems: An application to high-speed milling , 2015 .

[37]  W. Eversheim,et al.  Computer-aided process planning—State of the art and future development , 1993 .

[38]  Atsushi Fujimoto,et al.  Title Flexible Modulation of Risk Attitude during Decision-Making under Quota Running title : Decision-Making under Quota Authors and Affiliations : , 2016 .

[39]  Angappa Gunasekaran,et al.  Computer-aided process planning: A state of art , 1998 .

[40]  James Gao,et al.  Web-based Process Planning for Machine Tool Maintenance and Services , 2015 .

[41]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[42]  D. Dimitrov,et al.  A Cost Modelling Approach for Milling Titanium Alloys , 2016 .

[43]  Edmundas Kazimieras Zavadskas,et al.  Fuzzy multiple criteria decision-making techniques and applications - Two decades review from 1994 to 2014 , 2015, Expert Syst. Appl..

[44]  Manoj Kumar Tiwari,et al.  Multi-agent system for distributed computer-aided process planning problem in e-manufacturing environment , 2009 .

[45]  Ciro A. Rodríguez,et al.  Influence of tool path strategy on the cycle time of high-speed milling , 2003, Comput. Aided Des..

[46]  I. K. Hui,et al.  Development of a computer-integrated system to support CAD to CAPP , 2005 .

[47]  Ferruh Öztürk,et al.  Neural network based non-standard feature recognition to integrate CAD and CAM , 2001, Comput. Ind..

[48]  Andrew Y. C. Nee,et al.  A fuzzy set AHP-based DFM tool for rotational parts , 2003 .

[49]  Hoda A. ElMaraghy,et al.  Evolution and Future Perspectives of CAPP , 1993 .

[50]  Lihui Wang,et al.  Embedding a process plan in function blocks for adaptive machining , 2010 .

[51]  Harold J. Steudel,et al.  Computer-aided process planning: past, present and future , 1984 .

[52]  C. Ou-Yang,et al.  Developing an integrated framework for feature-based early manufacturing cost estimation , 1997 .

[53]  Yoong-Ho Jung,et al.  Five-axis machining time estimation algorithm based on machine characteristics , 2007 .

[54]  Yusri Yusof,et al.  Survey on computer-aided process planning , 2014, The International Journal of Advanced Manufacturing Technology.

[55]  Richard A. Wysk.  AN AUTOMATED PROCESS PLANNING AND SELECTION PROGRAM: APPAS. , 1977 .

[56]  Howard Raiffa,et al.  Preferences for multi-attributed alternatives , 2006 .

[57]  Jong-Yun Jung,et al.  Manufacturing cost estimation for machined parts based on manufacturing features , 2002, J. Intell. Manuf..

[58]  Y. S. Tarng,et al.  Cutting-parameter selection for maximizing production rate or minimizing production cost in multistage turning operations , 2000 .

[59]  Mustafa Yurdakul,et al.  AHP as a strategic decision-making tool to justify machine tool selection , 2004 .

[60]  Inyong Ham,et al.  Computer-Aided Process Planning: The Present and the Future , 1988 .

[61]  Taha Hossein Rashidi,et al.  Impact of risk attitudes and perception on game theoretic driving interactions and safety. , 2016, Accident; analysis and prevention.

[62]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[63]  Mojtaba Salehi,et al.  Application of genetic algorithm to computer-aided process planning in preliminary and detailed planning , 2009, Eng. Appl. Artif. Intell..

[64]  Tong Yifei,et al.  A feature-extraction-based process-planning system , 2008 .

[65]  Stefania Pellegrinelli,et al.  An integrated approach to support the joint design of machine tools and process planning , 2013 .