Challenges in riveting quality prediction: a literature survey
| Autor: | Fahim Ahmed, Kyoung-Yun Kim, Jaemun Sim, Sattar Ameri, Noor-E Jannat |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Structure (mathematical logic)
0209 industrial biotechnology Measure (data warehouse) business.industry Process (engineering) Computer science media_common.quotation_subject 02 engineering and technology Industrial and Manufacturing Engineering Manufacturing engineering 020303 mechanical engineering & transports 020901 industrial engineering & automation 0203 mechanical engineering Artificial Intelligence Prediction methods Manufacturing Rivet Quality (business) Literature survey business media_common |
| Zdroj: | Procedia Manufacturing. 38:1143-1150 |
| ISSN: | 2351-9789 |
| Popis: | Riveting is a prominent joining method due to the capability of easily assembling an innovated material (e.g., dissimilar materials with light-weight and stronger performance) to an enhanced structure (e.g., body-in-white). It receives attention recently in the transportation industry and the packing manufacturing industry (e.g., white goods). The quality prediction of riveting can increase the efficiency of the riveting process and design. Even though there is much potential to data mining technique for the prediction, the mining approach is rarely used for riveting. At this perspective, we carried out a survey to make an accessible bibliography of riveting prediction for helping researchers. In this research, we searched for the past 25 year’s works of literature related to the quality measure and prediction method of riveting with special interests to self-piercing riveting (SPR). To do this, we retrieve research papers indexed at Engineering Village Database. Firstly, we categorize riveting-quality measures and prediction methods. Secondly, we analyze the topic and trend of riveting research from the collected papers. Finally, we conclude with the remaining challenges for the riveting quality prediction as well as in a data mining perspective. |
| Databáze: | OpenAIRE |
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