{
      "creators": [
        {
          "name": {
            "honourific": null,
            "lineage": null,
            "family": "Al-Zubaidi",
            "given": "Salah"
          }
        },
        {
          "name": {
            "family": "Ghani",
            "honourific": null,
            "lineage": null,
            "given": "Jaharah A."
          }
        },
        {
          "name": {
            "honourific": null,
            "given": "Che Hassan Che",
            "lineage": null,
            "family": "Haron"
          }
        },
        {
          "name": {
            "given": "Adnan Naji Jameel",
            "honourific": null,
            "lineage": null,
            "family": "Al-Tamimi"
          }
        },
        {
          "name": {
            "lineage": null,
            "honourific": null,
            "family": "Mohammed",
            "given": "M.N."
          }
        },
        {
          "name": {
            "honourific": null,
            "family": "Ruggiero",
            "lineage": null,
            "given": "Alessandro"
          }
        },
        {
          "name": {
            "honourific": null,
            "family": "Sarhan",
            "lineage": null,
            "given": "Samaher M."
          }
        },
        {
          "name": {
            "lineage": null,
            "honourific": null,
            "given": "Oday I.",
            "family": "Abdullah"
          }
        },
        {
          "name": {
            "lineage": null,
            "honourific": null,
            "family": "Salleh",
            "given": "Mohd Shukor"
          }
        }
      ],
      "refereed": "TRUE",
      "publisher": "Walter de Gruyter GmbH",
      "publication": "Journal of the Mechanical Behavior of Materials",
      "abstract": "Titanium alloys are broadly used in the medical and aerospace sectors. However, they are categorized within the hard-to-machine alloys ascribed to their higher chemical reactivity and lower thermal conductivity. This aim of this research was to study the impact of the dry-end-milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. This research aims to study the impact of the dry-end milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. Also, it seeks to develop a new hybrid neural model based on the training back propagation neural network (BPNN) with swarm optimization-gravitation search hybrid algorithms (PSO-GSA). Full-factorial design of the experiment with L27 orthogonal array was applied, and three end-milling parameters (cutting speed, feed rate, and axial depth of cut) with three levels were selected (50, 77.5, and 105 m/min; 0.1, 0.15, and 0.2 mm/tooth; and 1, 1.5, and 2 mm) and investigated to show their influence on the obtained surface roughness. The results revealed that the surface roughness is significantly affected by the feed rate followed by the axial depth. A 0.49 \u00ce\u00bcm was produced as a minimum surface roughness at the optimized parameters of 105 m/min, 0.1 mm/tooth, and 1 mm. On the other hand, a neural network having a single hidden layer with 1-20 hidden neurons, 3 input neurons, and 1 output neuron was trained with both PSO and PSO-GSA algorithms. The hybrid BPNN-PSO-GSA model showed its superiority over the BPNN-PSO model in terms of the minimum mean square error (MSE) that was calculated during the testing stage. The best BPNN-PSO-GSA hybrid model was the 3-18-1 structure, which reached the best testing MSE of 3.8 \u00c3\ufffd 10-11 against 2.42 \u00c3\ufffd 10-5 of the 3-8-1 BPNN-PSO hybrid model. \u00c2\u00a9 2023 the author(s), published by De Gruyter.",
      "eprint_status": "archive",
      "datestamp": "2026-06-23 07:21:22",
      "volume": 32,
      "lastmod": "2026-06-23 07:21:22",
      "metadata_visibility": "show",
      "note": "Cited by: 5; All Open Access; Gold Open Access; Green Open Access",
      "full_text_status": "none",
      "uri": "https://khub.utp.edu.my/scholars/id/eprint/20584",
      "official_url": "https://www.scopus.com/pages/publications/85177181457?origin=resultslist",
      "issn": 21910243,
      "date": 2023,
      "userid": 1,
      "number": 1,
      "rev_number": 3,
      "dir": "disk0/00/02/05/84",
      "status_changed": "2026-06-23 07:21:22",
      "type": "article",
      "id_number": "10.1515/jmbm-2022-0300",
      "title": "Investigation of the performance of integrated intelligent models to predict the roughness of Ti6Al4V end-milled surface with uncoated cutting tool",
      "ispublished": "pub",
      "eprintid": 20584
    }