No Cover Image

Journal article

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

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...

Full description

Published in: Journal of Intelligent Manufacturing
Published:
URI: https://cronfa.swan.ac.uk/Record/cronfa71470
first_indexed 2026-02-19T15:12:17Z
last_indexed 2026-02-19T15:12:17Z
id cronfa71470
recordtype SURis
fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>71470</id><entry>2026-02-19</entry><title>A Scalable Random Forest (SRF) Approach for Non-linear Predictive Modelling using Small Manufacturing Datasets.</title><swanseaauthors><author><sid>0136f9a20abec3819b54088d9647c39f</sid><ORCID>0000-0003-4848-4545</ORCID><firstname>Rajesh</firstname><surname>Ransing</surname><name>Rajesh Ransing</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2026-02-19</date><deptcode>ACEM</deptcode><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 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.</abstract><type>Journal Article</type><journal>Journal of Intelligent Manufacturing</journal><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords>Random Forest, Common-Cause Variation, Predictive Analytics, Data Augmentation, Small Data, Quality Improvement</keywords><publishedDay>0</publishedDay><publishedMonth>0</publishedMonth><publishedYear>0</publishedYear><publishedDate>0001-01-01</publishedDate><doi/><url/><notes/><college>COLLEGE NANME</college><department>Aerospace, Civil, Electrical, and Mechanical Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>ACEM</DepartmentCode><institution>Swansea University</institution><apcterm/><funders/><projectreference/><lastEdited>2026-02-19T15:14:39.7232122</lastEdited><Created>2026-02-19T14:51:23.8109259</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering</level></path><authors><author><firstname>Meshari A.</firstname><surname>Al-Ebrahim</surname><order>1</order></author><author><firstname>Rajesh</firstname><surname>Ransing</surname><orcid>0000-0003-4848-4545</orcid><order>2</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling 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 Random Forest, Common-Cause Variation, Predictive Analytics, Data Augmentation, Small Data, Quality Improvement 0 0 0 0001-01-01 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University 2026-02-19T15:14:39.7232122 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
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
institution Swansea University
college_str Faculty of Science and Engineering
hierarchytype
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 0
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 0001-01-01T15:14:41Z
_version_ 1857567149667647488
score 11.461559

Similar Items