Progressive measurement and monitoring for multi-resolution data in surface manufacturing considering spatial and cross correlations

Suriano, Saumuy; Wang, Hui; Shao, Chenhui; Hu, S. Jack; Sekhar, Praveen

doi:10.1080/0740817x.2014.998389

Cited by 33 publications

(22 citation statements)

References 24 publications

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“…Data mining is a tool that commonly implements machine learning, statistical methods, visualization or other pattern recognition technologies to find useful features or trends. Since it is successful in discovering potential patterns hidden inside big data, data mining can be used to optimize the operation, improve products quality and other aspects, including fault recognition, quality diagnostic, prediction, and scheduling [75,76]. Different from other techniques, data mining does not require specific data collection processes, and because of development of machine learning, especially deep learning, data mining is playing an increasingly important role in big data analytics for manufacturing.…”

Section: Big Data Analytics and Data Miningmentioning

confidence: 99%

Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review

et al. 2018

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With the rapid development of sensing, communication, computing technologies, and analytics techniques, today’s manufacturing is marching towards a new generation of sustainability, digitalization, and intelligence. Even though the significance of both sustainability and intelligence is well recognized by academia, industry, as well as governments, and substantial efforts are devoted to both areas, the intersection of the two has not been fully exploited. Conventionally, studies in sustainable manufacturing and smart manufacturing have different objectives and employ different tools. Nevertheless, in the design and implementation of smart factories, sustainability, and energy efficiency are supposed to be important goals. Moreover, big data based decision-making techniques that are developed and applied for smart manufacturing have great potential in promoting the sustainability of manufacturing. In this paper, the state-of-the-art of sustainable and smart manufacturing is first reviewed based on the PRISMA framework, with a focus on how they interact and benefit each other. Key problems in both fields are then identified and discussed. Specially, different technologies emerging in the 4th industrial revolution and their dedications on sustainability are discussed. In addition, the impacts of smart manufacturing technologies on sustainable energy industry are analyzed. Finally, opportunities and challenges in the intersection of the two are identified for future investigation. The scope examined in this paper will be interesting to researchers, engineers, business owners, and policymakers in the manufacturing community, and could serve as a fundamental guideline for future studies in these areas.

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Section: Big Data Analytics and Data Miningmentioning

confidence: 99%

Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review

et al. 2018

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“…First, model f ðU l ðsÞ; b l Þ is employed to capture the relationship between the process inputs and the surface height in face milling processes. It is reported that the surface height variation in a face milling process is strongly influenced by a number of engineering factors [13,20,21], including (1) the product/surface design that characterizes the design patterns of a surface, e.g., size, shape, and spatial distribution of holes and slots, (2) physical attributes of part materials, such as the defects and heterogeneous physical attributes caused by manufacturing flaws from suppliers, (3) manufacturing process conditions, such as feed rate, spindle tilt, spindle speed, depth of cut, cutter path, and clamping force, and (4) multistage interdependence that characterizes the effects of downstream stages on the surface shapes created in the upstream stages. For details, please refer to Ref.…”

Section: Engineering-guided Multitask Learning Surfacementioning

confidence: 99%

“…For details, please refer to Ref. [13]. The functional form of f ðÁÞ should be determined based on a thorough understanding of the process physics.…”

Section: Engineering-guided Multitask Learning Surfacementioning

confidence: 99%

“…One limitation with these data-driven approaches is that the precision of the surface predictions is constrained by the density of measurements. To deal with the limitation, process knowledge can be integrated with surface measurements to cost-effectively model surface shapes [13,14]. The prediction improvement was achieved by fusing multiresolution measurements with cutting force dynamics during surface manufacturing.…”

Section: Introductionmentioning

confidence: 99%

“…We first establish a surface model consisting of a global trend that is induced by cutting force dynamics and a local variation term that can be characterized by a zero-mean Gaussian process. The global trend captures the overall shape variation across the surface, and can be well explained by the process physics [13]. An iterative multitask Gaussian process learning algorithm is then developed to learn the parameters from the global trend and the local variation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving Machined Surface Shape Prediction by Integrating Multi-Task Learning With Cutting Force Variation Modeling

Shao

Ren

Wang

et al. 2016

Journal of Manufacturing Science and Engineering

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The shapes of machined surfaces play a critical role affecting powertrain performance, and therefore, it is necessary to characterize the shapes with high resolution. State-of-the-art approaches for surface shape characterization are mostly data-driven by interpolating and extrapolating the spatial data but its precision is limited by the density of measurements. This paper explores the new opportunity of improving surface shape prediction through considering the similarity of multiple similar manufacturing processes. It is a common scenario when the process of interest lacks sufficient data whereas rich data could be available from other similar-but-not-identical processes. It is reasonable to transfer the insights gained from other relevant processes into the surface shape prediction. This paper develops an engineering-guided multitask learning (EG-MTL) surface model by fusing surface cutting physics in engineering processes and the spatial data from a number of similar-but-not-identical processes. An iterative multitask Gaussian process learning algorithm is developed to learn the model parameters. Compared with the conventional multitask learning, the proposed method has the advantages in incorporating the insights on cutting force variation during machining and is potentially able to improve the prediction performance given limited measurement data. The methodology is demonstrated based on the data from real-world machining processes in an engine plant.

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Surface Characterization and Evaluation

2019

High Definition Metrology Based Surface Quality Control and Applications

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Progressive measurement and monitoring for multi-resolution data in surface manufacturing considering spatial and cross correlations

Cited by 33 publications

References 24 publications

Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review

Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review

Improving Machined Surface Shape Prediction by Integrating Multi-Task Learning With Cutting Force Variation Modeling

Surface Characterization and Evaluation

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