Merging Machining and Measurement for Cognitive Manufacturing

2010 ◽  
Author(s):  
Yaoyao F. Zhao ◽  
Frederick M. Proctor ◽  
John A. Horst ◽  
Xun Xu
Keyword(s):  
Process Planning ◽  
Manufacturing Systems ◽  
Measurement Process ◽  
Machining Process ◽  
Prototype System ◽  
New Paradigm ◽  
Research Attention ◽  
On Line ◽  
Manufacturing Technologies ◽  
Machining Process Planning

Machining process planning and measurement process planning have long received research attention from industry and academia. Machining and measurement process automation is well established for mass production in today’s manufacturing systems. However, over the years manufacturing systems have evolved in response to many external drivers including the introduction of new manufacturing technologies and materials, the constant evolution of new products and the increased emphasis on quality as well as the escalating global competition and pressing need for responsiveness, agility and adaptability. These external drivers compel the realization of cognitive manufacturing, in which machining and measurement are merged together to form a more informed, more flexible, and more controlled manufacturing environment. In this way, when unforeseen changes or significant alternations happen, machining process planning systems can receive on-line measurement results, make decisions, and adjust machining operations accordingly in real-time. This paper presents a new paradigm of process planning research and outlines the way to reach cognitive manufacturing. An integrated machining and measurement process planning prototype system has been developed and tested with case studies.

scholarly journals A Holonic-Based Self-Learning Mechanism for Energy-Predictive Planning in Machining Processes

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 739 ◽  
Author(s):  
Seung-Jun Shin ◽  
Young-Min Kim ◽  
Prita Meilanitasari
Keyword(s):  
Machine Tool ◽  
Process Planning ◽  
Predictive Modeling ◽  
Manufacturing Systems ◽  
Minimum Energy ◽  
Hybrid Learning ◽  
Prototype System ◽  
Learning Approach ◽  
Machining Processes ◽  
Self Learning

The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufacturing objects under the paradigm of Holonic Manufacturing Systems. This mechanism allows machines and manufacturing objects to acquire their data and model interconnection and to perform model-driven autonomous and collaborative behaviors. The hybrid learning approach is designed to obtain predictive modeling ability in both data-existent and even data-absent environments via accommodating machine learning (which extracts knowledge from data) and transfer learning (which extracts knowledge from existing knowledge). The present work also implements a prototype system to demonstrate automatic predictive modeling and autonomous process planning for energy reduction in milling processes. The prototype generates energy-predictive models via hybrid learning and seeks the minimum energy-using machine tool through the contract net protocol combined with energy prediction. As a result, the prototype could achieve a reduction of 9.70% with respect to energy consumption as compared with the maximum energy-using machine tool.

scholarly journals Decision-making for multi-criteria optimization of process planning

2019 ◽  
Vol 20 (8) ◽  
pp. 806 ◽  
Author(s):  
Laurent Delolme ◽  
Anne-Lise Antomarchi ◽  
Séverine Durieux ◽  
Emmanuel Duc
Keyword(s):  
Decision Making ◽  
Process Planning ◽  
Choice Theory ◽  
Social Choice Theory ◽  
Automatic Generation ◽  
Machining Process ◽  
Performance Criteria ◽  
Machining Time ◽  
Machining Process Planning ◽  
Productivity And Competitiveness

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.

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