Embedding a process plan in function blocks for adaptive machining

Wang, Lihui; Holm, Magnus; Adamson, Göran

doi:10.1016/j.cirp.2010.03.144

Cited by 50 publications

(13 citation statements)

References 13 publications

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“…Based on the previous research [7][8][9], a dynamic feature enabled adaptive process planning approach for complex part machining is proposed (Fig. 1).…”

Section: Dynamic Feature Based Adaptive Process Planningmentioning

confidence: 99%

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Dynamic feature based adaptive process planning for energy-efficient NC machining

Wang

Liu

2017

CIRP Annals

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“…Based on the previous research [7][8][9], a dynamic feature enabled adaptive process planning approach for complex part machining is proposed (Fig. 1).…”

Section: Dynamic Feature Based Adaptive Process Planningmentioning

confidence: 99%

“…In order to make a process plan flexible, the machining process is planned at two levels for: supervisory planning and operation planning [7]. In supervisory planning, critical machining sequence influenced by datum references and manufacturing constraints is decided.…”

Section: Dynamic Feature Based Adaptive Process Planningmentioning

confidence: 99%

Dynamic feature based adaptive process planning for energy-efficient NC machining

Wang

Liu

2017

CIRP Annals

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“…Distributed process planning proposed by Wang et.al [5][6] has a two-layer structure: shop-level supervisory planning and machine-level operation planning. The shop-level supervisory planning generates generic data that is portable to an alternative machine according to the availability of the machine for dynamic job dispatching.…”

Section: Related Workmentioning

confidence: 99%

A Machining Feature Information Model for Dynamic Manufacturing Planning

Liu

2014

Procedia CIRP

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This paper presents an information model that facilitates responsive changes to the dynamics of machining features in 2.5/3D machining. The model consists of basic information that remains unchanged throughout the manufacturing process and also adaptive information that may be redefined in response to changes in manufacturing resources. A suite of solutions have been developed to enable adaptive feature interpretation to the real time process plans without frequently re-recognising and re-planning that is common in current industrial practice. Examples will be provided to demonstrate the feasibility and potential benefits of the developed methodology.

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“…The prototype system extends the distributed process-planning (DPP) [229] concept and a two-stage generic-specific planning method [232]. For better connectivity between the cloud and machines, function blocks [234] are used to bring a process plan to a chosen machine for execution while monitoring its status at the same time. Since machine availability and capability can be monitored in real time through the cloud, resource virtualization has been eliminated.…”

Section: Manucloudmentioning

confidence: 99%

Cloud-enabled prognosis for manufacturing

et al. 2015

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Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. As an emerging infrastructure, cloud computing provides new opportunities to achieve the goals of advanced manufacturing. This paper reviews the historical development of prognosis theories and techniques and projects their future growth enabled by the emerging cloud infrastructure. Techniques for cloud computing are highlighted, as well as the influence of these techniques on the paradigm of cloud-enabled prognosis for manufacturing. Finally, this paper discusses the envisioned architecture and associated challenges of cloud-enabled prognosis for manufacturing

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Embedding a process plan in function blocks for adaptive machining

Cited by 50 publications

References 13 publications

Dynamic feature based adaptive process planning for energy-efficient NC machining

Dynamic feature based adaptive process planning for energy-efficient NC machining

A Machining Feature Information Model for Dynamic Manufacturing Planning

Cloud-enabled prognosis for manufacturing

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