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Journal of Manufacturing Science and Engineering is a peer-reviewed journal published by ASME, covering Advanced machining processes and optimization, Advanced Surface Polishing Techniques, Manufacturing Process and Optimization. Over its lifetime it has published 4,103 papers, which have received 117,194 citations.
| Publisher | ASME |
|---|---|
| Reference style | Numbered (ASME) Numbered — [1], [2] in the text [1] Smith, A., Jones, B., and Lee, C., 2023, "A Representative Article Title," Journal of Manufacturing Science and Engineering, 12(3), pp. 45–58.
Formats any DOI in Journal of Manufacturing Science and Engineering style. No sign-up. |
| Publishes research in | Advanced machining processes and optimization Advanced Surface Polishing Techniques Manufacturing Process and Optimization Additive Manufacturing and 3D Printing Technologies Metal Forming Simulation Techniques |
| ISSN | 1087-1357 |
| Citation impact (2-yr) | 2.41 |
| h-index | 125 |
| i10-index | 2,593 |
| Top institutions publishing here | University of Michigan |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Additive manufacturing (AM), the process of joining materials to make objects from three-dimensional (3D) model data, usually layer by layer, is distinctly a different form and has many advantages over traditional manufacturing processes. Commonly known as “3D printing,” AM provides a cost-effective and time-efficient way to produce low-volume, customized products with complicated geometries and advanced…
The mechanistic and unified mechanics of cutting approaches to the prediction of forces in milling operations are briefly described and compared. The mechanistic approach is shown to depend on milling force coefficients determined from milling tests for each cutter geometry. By contrast the unified mechanics of cutting approach relies on an experimentally determined orthogonal cutting…
There is consensus among both the research and industrial communities, and even the general public, that additive manufacturing (AM) processes capable of processing metallic materials are a set of game changing technologies that offer unique capabilities with tremendous application potential that cannot be matched by traditional manufacturing technologies. Unfortunately, with all what AM has to…
Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closed-form mathematical models. However, prior knowledge of system behavior is…
Abstract Today’s manufacturing systems are becoming increasingly complex, dynamic, and connected. The factory operations face challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in artificial intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for…
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