Dazhong Wu

Bio: Dazhong Wu is an academic researcher from University of Central Florida. The author has contributed to research in topics: Cloud computing & Cloud-based design and manufacturing. The author has an hindex of 25, co-authored 73 publications receiving 3441 citations. Previous affiliations of Dazhong Wu include Iowa State University & Pennsylvania State University.

Cloud-based design and manufacturing

Abstract: Cloud-based design manufacturing (CBDM) refers to a service-oriented networked product development model in which service consumers are enabled to configure, select, and utilize customized product realization resources and services ranging from computer-aided engineering software to reconfigurable manufacturing systems. An ongoing debate on CBDM in the research community revolves around several aspects such as definitions, key characteristics, computing architectures, communication and collaboration processes, crowdsourcing processes, information and communication infrastructure, programming models, data storage, and new business models pertaining to CBDM. One question, in particular, has often been raised: is cloud-based design and manufacturing actually a new paradigm, or is it just "old wine in new bottles"? To answer this question, we discuss and compare the existing definitions for CBDM, identify the essential characteristics of CBDM, define a systematic requirements checklist that an idealized CBDM system should satisfy, and compare CBDM to other relevant but more traditional collaborative design and distributed manufacturing systems such as web- and agent-based design and manufacturing systems. To justify the conclusion that CBDM can be considered as a new paradigm that is anticipated to drive digital manufacturing and design innovation, we present the development of a smart delivery drone as an idealized CBDM example scenario and propose a corresponding CBDM system architecture that incorporates CBDM-based design processes, integrated manufacturing services, information and supply chain management in a holistic sense. We present a new paradigm in digital manufacturing and design innovation, namely cloud-based design and manufacturing (CBDM).We identify the common key characteristics of CBDM.We define a requirement checklist that any idealized CBDM system should satisfy.We compare CBDM with other relevant but more traditional collaborative design and distributed manufacturing systems.We describe an idealized CBDM application example scenario.

A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

Abstract: 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 closedform mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR. [DOI: 10.1115/1.4036350]

Prediction of surface roughness in extrusion-based additive manufacturing with machine learning

Abstract: Additive manufacturing (AM), also known as 3D printing, has been increasingly adopted in the aerospace, automotive, energy, and healthcare industries over the past few years. While AM has many advantages over subtractive manufacturing processes, one of the primary limitations of AM is surface integrity. To improve the surface integrity of additively manufactured parts, a data-driven predictive modeling approach to predicting surface roughness in AM is introduced. Multiple sensors of different types, including thermocouples, infrared temperature sensors, and accelerometers, are used to collect temperature and vibration data. An ensemble learning algorithm is introduced to train the predictive model of surface roughness. Features in the time and frequency domains are extracted from sensor-based condition monitoring data. A subset of these features is selected to improve computational efficiency and prediction accuracy. The predictive model is validated using the condition monitoring data collected from a set of AM tests conducted on a fused filament fabrication (FFF) machine. Experimental results have shown that the proposed predictive modeling approach is capable of predicting the surface roughness of 3D printed components with high accuracy.

Smart manufacturing: Past research, present findings, and future directions

Abstract: Today, the manufacturing industry is aiming to improve competitiveness through the convergence with cutting-edge ICT technologies in order to secure a new growth engine. Smart Manufacturing, which is the fourth revolution in the manufacturing industry and is also considered as a new paradigm, is the collection of cutting-edge technologies that support effective and accurate engineering decision-making in real time through the introduction of various ICT technologies and the convergence with the existing manufacturing technologies. This paper surveyed and analyzed various articles related to Smart Manufacturing, identified the past and present levels, and predicted the future. For these purposes, 1) the major key technologies related to Smart Manufacturing were identified through the analysis of the policies and technology roadmaps of Germany, the U.S., and Korea that have government-driven leading movements for Smart Manufacturing, 2) the related articles on the overall Smart Manufacturing concept, the key system structure, or each key technology were investigated, and, finally, 3) the Smart Manufacturing-related trends were identified and the future was predicted by conducting various analyses on the application areas and technology development levels that have been addressed in each article.