Innovative Data Models: Transforming Material Process Design and Optimization
保存先:
| 出版年: | Metals vol. 15, no. 8 (2025), p. 873-902 |
|---|---|
| 第一著者: | |
| その他の著者: | , |
| 出版事項: |
MDPI AG
|
| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| タグ: |
タグなし, このレコードへの初めてのタグを付けませんか!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3244046671 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2075-4701 | ||
| 024 | 7 | |a 10.3390/met15080873 |2 doi | |
| 035 | |a 3244046671 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231648 |2 nlm | ||
| 100 | 1 | |a Horr, Amir M | |
| 245 | 1 | |a Innovative Data Models: Transforming Material Process Design and Optimization | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a As the use of data models and data science techniques in industrial processes grows exponentially, the question arises: to what extent can these techniques impact the future of manufacturing processes? This article examines the potential future impacts of these models based on an assessment of existing trends and practices. The drive towards digital-oriented manufacturing and cyber-based process optimization and control has brought many opportunities and challenges. On one hand, issues of data acquisition, handling, and quality for proper database building have become important subjects. On the other hand, the reliable utilization of this available data for optimization and control has inspired much research. This research work discusses the fundamental question of how far these models can help design and/or improve existing processes, highlighting their limitations and challenges. Furthermore, it reviews state-of-the-art practices and their successes and failures in material process applications, including casting, extrusion, and additive manufacturing (AM), and presents some quantitative indications. | |
| 653 | |a Innovations | ||
| 653 | |a Simulation | ||
| 653 | |a Data models | ||
| 653 | |a Continuous casting | ||
| 653 | |a Physics | ||
| 653 | |a Data acquisition | ||
| 653 | |a Data science | ||
| 653 | |a Power | ||
| 653 | |a Optimization | ||
| 653 | |a Data processing | ||
| 653 | |a Phase transitions | ||
| 653 | |a Manufacturing | ||
| 653 | |a Design optimization | ||
| 653 | |a Strategic planning | ||
| 653 | |a Boundary conditions | ||
| 653 | |a Systems engineering | ||
| 653 | |a Efficiency | ||
| 653 | |a Case studies | ||
| 700 | 1 | |a Hartmann, Matthias | |
| 700 | 1 | |a Haunreiter Fabio | |
| 773 | 0 | |t Metals |g vol. 15, no. 8 (2025), p. 873-902 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244046671/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244046671/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244046671/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |