Duc Pham

Prof. Duc Pham

Chance Professor of Engineering, School of Engineering, The University of Birmingham, Birmingham B15 2TT, UK
orcid

Research Focus & Metrics

Academic Career Length

48Years (1979-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 January 2006
Verified
Book Chapter
Closed access
Duc PhamDuc Pham
Duc Pham
AGAfshin Ghanbarzadeh
Afshin Ghanbarzadeh
EKE. Koç
E. Koç
SOS. Otri
S. Otri
SRS. Rahim
S. Rahim
MZM. Zaidi
M. Zaidi

1 October 1998 in International Journal of Machine Tools and Manufacture
Verified
Journal Article
Closed access
Duc PhamDuc Pham
Duc Pham
RGR.S Gault
R.S Gault

1 January 2005 in Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
Verified
Journal Article
Closed access
Duc PhamDuc Pham
Duc Pham
SDS S Dimov
S S Dimov
CNC D Nguyen
C D Nguyen
The K-means algorithm is a popular data-clustering algorithm. However, one of its drawbacks is the requirement for the number of clusters, K, to be specified before the algorithm is applied. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Factors that affect this selection are then discussed and a new measure to assist the selection is proposed. The paper concludes with an analysis of the results of using the proposed measure to determine the number of clusters for the K-means algorithm for different data sets.

1 June 2004 in Journal of Materials Processing Technology
Verified
Journal Article
Closed access
Duc PhamDuc Pham
Duc Pham
SDS.S Dimov
S.S Dimov
Samuel BigotSamuel Bigot
Samuel Bigot
AIA Ivanov
A Ivanov
KPK Popov
K Popov

27 November 2018 in International Journal of Production Research
Verified
Journal Article
Closed access
QLQuan Liu
Quan Liu
Zhihao LiuZhihao Liu
Zhihao Liu
Wenjun XuWenjun Xu
Wenjun Xu
QTQuan Tang
Quan Tang
ZZZude Zhou
Zude Zhou
Duc PhamDuc Pham
Duc Pham
Sustainable manufacturing is a global front-burner issue oriented to the sustainable development of humanity and society. In this context, this paper takes the human-robot collaborative disassembly (HRCD) as the topic on its contribution to economic, environmental and social sustainability. In addition, a detailed enabling systematic implementation for HRCD is presented, combined with a set of advanced technologies such as cyber-physical production system (CPPS) and artificial intelligence (AI), and it involves five aspects which including perception, cognition, decision, execution and evolution aiming at the dynamics, uncertainties and complexities in disassembly. Deep reinforcement learning, incremental learning and transfer learning are also investigated in the systematic approaches for HRCD. The demonstration in the case study contains experiment results of multi-modal perception for robot system and human body in hybrid human-robot collaborative disassembly cell, sequence planning for an HRCD task, distance based safety strategy and motion driven control method, and it manifests high feasibility and effectiveness of the proposed approaches for HRCD and verifies the functionalities of the systematic framework.

1 January 1995
Verified
Book
Closed access
Duc PhamDuc Pham
Duc Pham
XLXing Liu
Xing Liu