Chang-Xue Feng

951 total citations
30 papers, 744 citations indexed

About

Chang-Xue Feng is a scholar working on Industrial and Manufacturing Engineering, Management of Technology and Innovation and Mechanical Engineering. According to data from OpenAlex, Chang-Xue Feng has authored 30 papers receiving a total of 744 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Industrial and Manufacturing Engineering, 15 papers in Management of Technology and Innovation and 10 papers in Mechanical Engineering. Recurrent topics in Chang-Xue Feng's work include Manufacturing Process and Optimization (19 papers), Product Development and Customization (15 papers) and Advanced Measurement and Metrology Techniques (6 papers). Chang-Xue Feng is often cited by papers focused on Manufacturing Process and Optimization (19 papers), Product Development and Customization (15 papers) and Advanced Measurement and Metrology Techniques (6 papers). Chang-Xue Feng collaborates with scholars based in United States, China and Hong Kong. Chang-Xue Feng's co-authors include Andrew Kusiak, Xianfeng Wang, Jin Wang, Jinsong Wang, Chun‐Che Huang, Xianfeng Wang, David He, J. Wang, Gary Lin and Peigen Li and has published in prestigious journals such as International Journal of Production Research, Computers & Industrial Engineering and Annals of Operations Research.

In The Last Decade

Chang-Xue Feng

30 papers receiving 679 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Chang-Xue Feng United States 16 368 312 191 112 88 30 744
Hsu‐Pin Wang United States 20 635 1.7× 211 0.7× 82 0.4× 43 0.4× 70 0.8× 41 983
Gianfranco La Rocca Netherlands 17 464 1.3× 270 0.9× 143 0.7× 52 0.5× 104 1.2× 55 1.1k
N. Duffie United States 18 929 2.5× 383 1.2× 356 1.9× 34 0.3× 99 1.1× 36 1.3k
Mark J. Jakiela United States 13 223 0.6× 245 0.8× 116 0.6× 34 0.3× 62 0.7× 40 967
Masataka Yoshimura Japan 14 180 0.5× 156 0.5× 107 0.6× 47 0.4× 35 0.4× 102 785
Tien‐Chien Chang United States 20 636 1.7× 251 0.8× 61 0.3× 98 0.9× 272 3.1× 40 912
Jeongsam Yang South Korea 15 203 0.6× 66 0.2× 59 0.3× 92 0.8× 63 0.7× 48 540
Jong Gye Shin South Korea 15 282 0.8× 205 0.7× 35 0.2× 132 1.2× 200 2.3× 79 812
Gary A. Gabriele United States 13 189 0.5× 150 0.5× 71 0.4× 106 0.9× 20 0.2× 53 649
Gábor Erdős Hungary 17 386 1.0× 110 0.4× 45 0.2× 37 0.3× 50 0.6× 42 716

Countries citing papers authored by Chang-Xue Feng

Since Specialization
Citations

This map shows the geographic impact of Chang-Xue Feng's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Chang-Xue Feng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chang-Xue Feng more than expected).

Fields of papers citing papers by Chang-Xue Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chang-Xue Feng. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Chang-Xue Feng. The network helps show where Chang-Xue Feng may publish in the future.

Co-authorship network of co-authors of Chang-Xue Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Chang-Xue Feng. A scholar is included among the top collaborators of Chang-Xue Feng based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chang-Xue Feng. Chang-Xue Feng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Feng, Chang-Xue, Liang Gao, Peigen Li, & Xinyu Shao. (2010). Selection and Comparison of Supervised Predictive Data Mining Models for Electronics Fabrication Data. 3–7. 1 indexed citations
2.
Feng, Chang-Xue, et al.. (2006). Design and analysis of experiments in CMM measurement uncertainty study. Precision Engineering. 31(2). 94–101. 45 indexed citations
3.
Feng, Chang-Xue, et al.. (2006). Practical guidelines for developing BP neural network models of measurement uncertainty data. Journal of Manufacturing Systems. 25(4). 239–250. 19 indexed citations
4.
Feng, Chang-Xue, et al.. (2005). Selection and validation of predictive regression and neural network models based on designed experiments. IIE Transactions. 38(1). 13–23. 36 indexed citations
5.
Feng, Chang-Xue, et al.. (2005). Threefold vs. fivefold cross validation in one-hidden-layer and two-hidden-layer predictive neural network modeling of machining surface roughness data. Journal of Manufacturing Systems. 24(2). 93–107. 37 indexed citations
6.
Feng, Chang-Xue & Xianfeng Wang. (2004). Data mining techniques applied to predictive modeling of the knurling process. IIE Transactions. 36(3). 253–263. 13 indexed citations
7.
Feng, Chang-Xue & Xianfeng Wang. (2003). Surface roughness predictive modeling: neural networks versus regression. IIE Transactions. 35(1). 11–27. 80 indexed citations
8.
Feng, Chang-Xue & Xianfeng Wang. (2002). Subset selection in predictive modeling of the CMM digitization uncertainty. Journal of Manufacturing Systems. 21(6). 419–439. 10 indexed citations
9.
Feng, Chang-Xue, et al.. (2002). Neural networks modeling of honing surface roughness parameters defined by ISO 13565. Journal of Manufacturing Systems. 21(5). 395–408. 51 indexed citations
10.
Feng, Chang-Xue & Xianfeng Wang. (2002). Digitizing uncertainty modeling for reverse engineering applications: regression versus neural networks. Journal of Intelligent Manufacturing. 13(3). 189–199. 26 indexed citations
11.
Feng, Chang-Xue, Peigen Li, & Ming Liang. (2001). Fuzzy mapping of requirements onto functions in detail design. Computer-Aided Design. 33(6). 425–437. 12 indexed citations
12.
Matta, Renato de, Vernon Ning Hsu, & Chang-Xue Feng. (2001). Short-Term Capacity Adjustment with Offline Production for a Flexible Manufacturing System under Abnormal Disturbances. Annals of Operations Research. 107(1-4). 83–100. 3 indexed citations
13.
Feng, Chang-Xue & Andrew Kusiak. (1999). Robust Tolerance Synthesis With the Design of Experiments Approach. Journal of Manufacturing Science and Engineering. 122(3). 520–528. 31 indexed citations
14.
Feng, Chang-Xue & Andrew Kusiak. (1996). Design for Manufacturing: Discrete or Continuous Tolerances. 1 indexed citations
15.
Kusiak, Andrew & Chang-Xue Feng. (1996). Robust Tolerance Design for Quality. Journal of Engineering for Industry. 118(1). 166–169. 20 indexed citations
16.
Kusiak, Andrew & Chang-Xue Feng. (1995). Deterministic tolerance synthesis: a comparative study. Computer-Aided Design. 27(10). 759–768. 31 indexed citations
17.
Feng, Chang-Xue, et al.. (1995). Probabilistic tolerance synthesis: A comparative study. Iowa Research Online (University of Iowa). 357. 1 indexed citations
18.
Kusiak, Andrew, J. Wang, David He, & Chang-Xue Feng. (1995). A structured approach for analysis of design processes. IEEE Transactions on Components Packaging and Manufacturing Technology Part A. 18(3). 664–673. 34 indexed citations
19.
Feng, Chang-Xue & Andrew Kusiak. (1995). Constraint-based design of parts. Computer-Aided Design. 27(5). 343–352. 35 indexed citations
20.
Kusiak, Andrew & Chang-Xue Feng. (1994). Design of Products for an Agile Manufacturing Environment. 413–420. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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