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A scalable random forest (SRF) approach for non-linear predictive modelling using small manufacturing datasets

Meshari A. Al-Ebrahim, Rajesh Ransing Orcid Logo

Journal of Intelligent Manufacturing

Swansea University Author: Rajesh Ransing Orcid Logo

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DOI (Published version): 10.1007/s10845-026-02834-2

Abstract

This paper presents an integrated, scalable Random Forest (SRF)–based predictive framework for estimating the effects of process interventions, including (i) adjusting operating ranges for continuous process parameters within specified tolerances, (ii) selecting specific categories for discrete proc...

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Published in: Journal of Intelligent Manufacturing
ISSN: 0956-5515 1572-8145
Published: Springer Science and Business Media LLC 2026
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URI: https://cronfa.swan.ac.uk/Record/cronfa71470
first_indexed 2026-02-19T15:12:17Z
last_indexed 2026-06-25T06:24:00Z
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spelling 2026-06-24T13:58:29.8868485 v2 71470 2026-02-19 A scalable random forest (SRF) approach for non-linear predictive modelling using small manufacturing datasets 0136f9a20abec3819b54088d9647c39f 0000-0003-4848-4545 Rajesh Ransing Rajesh Ransing true false 2026-02-19 ACEM This paper presents an integrated, scalable Random Forest (SRF)–based predictive framework for estimating the effects of process interventions, including (i) adjusting operating ranges for continuous process parameters within specified tolerances, (ii) selecting specific categories for discrete process parameters, and (iii) combining adjustments to both continuous and discrete parameters. The framework moves beyond linear assumptions by employing a non-linear ensemble approach to identify critical process inputs and quantify their contributions to predicting the process response. These contributions are then leveraged to derive optimal operating ranges for continuous parameters and optimal categories for discrete parameters through a Decision Path Search (DPS) procedure based on tree decision paths. The proposed framework scales to a large number of process factors with complex non-linear dependencies and enables data-driven process improvement without requiring extensive domain expertise. Missing values in mixed-type datasets are addressed using an iterative Random Forest–based imputation scheme, while automatic forest-size optimisation enhances modelstability. All preprocessing and modelling steps are embedded within a leakage-safe pipeline, supported by learning-curve analysis and leakage-sanity diagnostics to guard against overfitting. Across the evaluated case studies, SRF delivers accurate predictions together with transparent, practitioner-ready operating windows, translating complex mixed-type manufacturing data into actionable guidance. Journal Article Journal of Intelligent Manufacturing 0 Springer Science and Business Media LLC 0956-5515 1572-8145 Random Forest, Common-Cause Variation, Predictive Analytics, Data Augmentation, Small Data, Quality Improvement 1 6 2026 2026-06-01 10.1007/s10845-026-02834-2 COLLEGE NANME Aerospace Civil Electrical and Mechanical Engineering COLLEGE CODE ACEM Swansea University Other This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors. 2026-06-24T13:58:29.8868485 2026-02-19T14:51:23.8109259 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Meshari A. Al-Ebrahim 1 Rajesh Ransing 0000-0003-4848-4545 2 71470__37038__f13cbb4c99fc4da3a2acda95584727cc.pdf 71470.VoR.pdf 2026-06-24T13:56:51.5681776 Output 7959743 application/pdf Version of Record true © The Author(s) 2026. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title A scalable random forest (SRF) approach for non-linear predictive modelling using small manufacturing datasets
spellingShingle A scalable random forest (SRF) approach for non-linear predictive modelling using small manufacturing datasets
Rajesh Ransing
title_short A scalable random forest (SRF) approach for non-linear predictive modelling using small manufacturing datasets
title_full A scalable random forest (SRF) approach for non-linear predictive modelling using small manufacturing datasets
title_fullStr A scalable random forest (SRF) approach for non-linear predictive modelling using small manufacturing datasets
title_full_unstemmed A scalable random forest (SRF) approach for non-linear predictive modelling using small manufacturing datasets
title_sort A scalable random forest (SRF) approach for non-linear predictive modelling using small manufacturing datasets
author_id_str_mv 0136f9a20abec3819b54088d9647c39f
author_id_fullname_str_mv 0136f9a20abec3819b54088d9647c39f_***_Rajesh Ransing
author Rajesh Ransing
author2 Meshari A. Al-Ebrahim
Rajesh Ransing
format Journal article
container_title Journal of Intelligent Manufacturing
container_volume 0
publishDate 2026
institution Swansea University
issn 0956-5515
1572-8145
doi_str_mv 10.1007/s10845-026-02834-2
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description This paper presents an integrated, scalable Random Forest (SRF)–based predictive framework for estimating the effects of process interventions, including (i) adjusting operating ranges for continuous process parameters within specified tolerances, (ii) selecting specific categories for discrete process parameters, and (iii) combining adjustments to both continuous and discrete parameters. The framework moves beyond linear assumptions by employing a non-linear ensemble approach to identify critical process inputs and quantify their contributions to predicting the process response. These contributions are then leveraged to derive optimal operating ranges for continuous parameters and optimal categories for discrete parameters through a Decision Path Search (DPS) procedure based on tree decision paths. The proposed framework scales to a large number of process factors with complex non-linear dependencies and enables data-driven process improvement without requiring extensive domain expertise. Missing values in mixed-type datasets are addressed using an iterative Random Forest–based imputation scheme, while automatic forest-size optimisation enhances modelstability. All preprocessing and modelling steps are embedded within a leakage-safe pipeline, supported by learning-curve analysis and leakage-sanity diagnostics to guard against overfitting. Across the evaluated case studies, SRF delivers accurate predictions together with transparent, practitioner-ready operating windows, translating complex mixed-type manufacturing data into actionable guidance.
published_date 2026-06-01T06:12:12Z
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score 11.109911

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