Cloud-based adaptive process planning considering availability and capabilities of machine tools

Abstract Disturbances on manufacturing shop-floors and the increasing number of product variants necessitate adaptive and flexible process planning methods. This paper proposes a service-oriented Cloud-based software framework comprising two services. The first service generates non-linear process plans using event-driven function blocks and a genetic algorithm. The second service, gathers data from shop-floor machine tools through sensors, input from operators, and machine schedules. An information fusion technique processes the monitoring data in order to feed the process planning service with the status, specifications, and availability time windows of machine tools. The methodology is validated in a case study of a machining SME.

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

[2]  Rajkumar Roy,et al.  Automated in-service damage identification , 2014 .

[3]  Ahmed Azab,et al.  Mathematical Modeling for Reconfigurable Process Planning , 2007 .

[4]  Krzysztof Jemielniak,et al.  Advanced monitoring of machining operations , 2010 .

[5]  Lihui Wang,et al.  An overview of function block enabled adaptive process planning for machining , 2015 .

[6]  Ji Ma,et al.  ASP: An Adaptive Setup Planning Approach for Dynamic Machine Assignments , 2010, IEEE Transactions on Automation Science and Engineering.

[7]  Lihui Wang,et al.  Designing function blocks for distributed process planning and adaptive control , 2009, Eng. Appl. Artif. Intell..

[8]  Xun Xu,et al.  Development of a Hybrid Manufacturing Cloud , 2014 .

[9]  Andrew Y. C. Nee,et al.  Integrated Condition Monitoring and Fault Diagnosis for Modern Manufacturing Systems , 2000 .

[10]  Kanji Ueda,et al.  An Emergent Synthesis Approach to Simultaneous Process Planning and Scheduling , 2007 .

[11]  George Chryssolouris,et al.  An Experimental Study of Strategies for Integrating Sensor Information in Machining , 1989 .

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

[13]  Lihui Wang,et al.  Adaptive Distributed Process Planning and Executions for Multi-tasking Machining Centers with Special Functionalities , 2015 .

[14]  Berend Denkena,et al.  Data mining approach for knowledge-based process planning , 2014 .

[15]  Lihui Wang,et al.  Architecture design for distributed process planning , 2003 .

[16]  Hans-Christian Möhring,et al.  Process monitoring with sensory machine tool components , 2010 .

[17]  Xun Xu,et al.  From cloud computing to cloud manufacturing , 2012 .

[18]  George Chryssolouris,et al.  Sensor Synthesis for Control of Manufacturing Processes , 1992 .

[19]  M. A. Mannan,et al.  Monitoring and Adaptive Control of Cutting Process by Means of Motor Power and Current Measurements , 1989 .

[20]  Maria Manuela Cruz-Cunha,et al.  E-Business Issues, Challenges and Opportunities for SMEs: Driving Competitiveness , 2010 .

[21]  James Llinas,et al.  Handbook of Multisensor Data Fusion , 2001 .

[22]  Martin Gilje Jaatun,et al.  Beyond lightning: A survey on security challenges in cloud computing , 2013, Comput. Electr. Eng..

[23]  Thomas L. Saaty,et al.  DECISION MAKING WITH THE ANALYTIC HIERARCHY PROCESS , 2008 .

[24]  George Chryssolouris.  Sensors in Laser Machining , 1994 .

[25]  Anjali Awasthi,et al.  Using AHP and Dempster-Shafer theory for evaluating sustainable transport solutions , 2011, Environ. Model. Softw..

[26]  George Chryssolouris,et al.  Manufacturing Systems: Theory and Practice , 1992 .

[27]  Dimitris Mourtzis,et al.  Machine Availability Monitoring for Adaptive Holistic Scheduling: A Conceptual Framework for Mass Customization , 2014 .

[28]  Lihui Wang,et al.  Generic machining process sequencing through a revised enriched machining feature concept , 2015 .

[29]  Lihui Wang,et al.  Machine availability monitoring and machining process planning towards Cloud manufacturing , 2013 .

[30]  Nikolaos Tapoglou,et al.  Cloud based platform for optimal machining parameter selection based on function blocks and real time monitoring , 2015 .

[31]  Dimitris Mourtzis,et al.  A Cloud-based Approach for Maintenance of Machine Tools and Equipment Based on Shop-floor Monitoring☆ , 2016 .

[32]  Lihui Wang,et al.  Latest Advances in Function Block Enabled Adaptive Process Planning , 2015 .

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

[34]  D. E. Dimla,et al.  Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods , 2000 .

[35]  Xun Xu,et al.  A novel energy demand modelling approach for CNC machining based on function blocks , 2014 .