Smart Manufacturing

Davis, Jim; Edgar, Thomas F.; Graybill, Robert; Korambath, Prakashan; Schott, Brian; Swink, Denise; Wang, Jianwu; Wetzel, Jim

doi:10.1146/annurev-chembioeng-061114-123255

Cited by 142 publications

(77 citation statements)

References 4 publications

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“…manufacturing cost estimation and/or process optimization, better understanding of the customer's requirements, etc., support is needed to handle the high dimensionality, complexity, and dynamics involved (Davis et al, 2015;Loyer, Henriques, Fontul, & Wiseall, 2016;Wuest, 2015). New developments in certain domains like mathematics and computer science (e.g.…”

Section: Open Accessmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning in manufacturing: advantages, challenges, and applications

Wuest

Weimer²,

Irgens

et al. 2016

Production & Manufacturing Research

808

438

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The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

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Section: Open Accessmentioning

confidence: 99%

“…sensor data from the production line, environmental data, machine tool parameters, etc. (Davis et al, 2015). Different names are used for this phenomenon, e.g.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in manufacturing: advantages, challenges, and applications

Wuest

Weimer²,

Irgens

et al. 2016

Production & Manufacturing Research

808

438

View full text Add to dashboard Cite

show abstract

“…The need to obtain physics-based models of chemical processes from operating data is growing as there are greater pushes toward smart/next-generation manufacturing techniques that seek to streamline process operation and control [26,27]. From an operational standpoint, physics-based process models can improve an engineer's understanding of the dynamics of a process to aid in trouble-shooting and therefore to enhance profits.…”

Section: Introductionmentioning

confidence: 99%

Data-Based Nonlinear Model Identification in Economic Model Predictive Control

Giuliani

Durand

2018

Smart Sustain. Manuf. Syst.

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Many chemical/petrochemical processes in industry are not completely modeled from a first-principles perspective because of the complexity of the underlying physico-chemical phenomena and the cost of obtaining more accurate, physically relevant models. System identification methods have been utilized successfully for developing empirical, though not necessarily physical, models for advanced model-based control designs such as model predictive control (MPC) for decades. However, a fairly recent development in MPC is economic model predictive control (EMPC), which is an MPC formulated with an economics-based objective function that may operate a process in a dynamic (i.e., off steady-state) fashion, in which case the details of the process model become important for obtaining sufficiently accurate state predictions away from the steady-state, and the physics and chemistry of the process become important for developing meaningful profit-based objective functions and safety-critical constraints. Therefore, methods must be developed for obtaining physically relevant models from data for EMPC design. While the literature regarding developing models from data has rapidly expanded in recent years, many new techniques require a model structure to be assumed a priori, to which the data is then fit. However, from the perspective of developing a physically meaningful model for a chemical process, it is often not obvious what structure to assume for the model, especially considering the often complex nonlinearities characteristic of chemical processes (e.g., in reaction rate laws). In this work, we suggest that the controller itself may facilitate the identification of physically relevant models online from process operating data by forcing the process state to nonroutine operating conditions

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“…In addition to the continued development of novel manufacturing processes, recent interest in smart manufacturing and automated process planning supported by new technologies and standards (e.g., machine interoperability standards) has stimulated research and educational developments (Davis et al, 2015). Recent advances in manufacturing technologies and tools can address current challenges in the engineering field.…”

Section: Introductionmentioning

confidence: 99%

A Pedagogical Module Framework to Improve Scaffolded Active Learning in Manufacturing Engineering Education

Mirkouei

Bhinge

McCoy

et al. 2016

Procedia Manufacturing

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Recent interest in improving pedagogical approaches in science, technology, engineering, and mathematics (STEM) fields has stimulated research at many universities. Several educational methodologies are reviewed in the context of manufacturing and through the lens of sustainability. It is found that there is a need to identify and understand the STEM educational challenges, and to assess the usefulness of existing methodologies using case-based analyses. In particular, this research aims to support student learning in manufacturing engineering through real-time process evaluations. A pedagogical framework is presented that can assist engineering educators in developing learning modules in support of this goal. The framework encompasses four steps: define the learning outcomes, create instructional resources, create active learning resources, and create a summative assessment mechanism. The framework emphasizes engagement of manufacturing engineering students in psychomotor learning, which remains a challenge due to the high cost of instructional laboratories. The framework is applied to develop a participatory pedagogy for manufacturing courses through the use of computer numerical control of manufacturing operations, and real-time monitoring, visualization, and data analysis of machine energy use. The framework is demonstrated for upper-level undergraduate and graduate manufacturing engineering courses at two universities (i.e., Computer-Aided Design and Manufacturing at Oregon State University and Precision Manufacturing at University of California, Berkeley). It is found that the framework can effectively support learning module development in manufacturing engineering education.

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Smart Manufacturing

Cited by 142 publications

References 4 publications

Machine learning in manufacturing: advantages, challenges, and applications

Machine learning in manufacturing: advantages, challenges, and applications

Data-Based Nonlinear Model Identification in Economic Model Predictive Control

A Pedagogical Module Framework to Improve Scaffolded Active Learning in Manufacturing Engineering Education

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