[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126876-en":3,"doc-seo-126876-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126876,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Integrating Machine Learning with Machine Parameters to Predict Plastic Part Quality in Injection Moulding","The plastic injection moulding process is a high-productivity technique whose output quality depends strongly on controllable machine settings and related thermal conditions. This study investigates how primary machine parameters influence part quality, intentionally excluding time effects, mould-feature considerations, and cooling-channel geometry. Machine learning models are used to build an understanding of how machine parameters can be leveraged for quality prediction. The reported results support optimisation of injection moulding to improve product quality and consistency.","Integrating Machine Learning with Machine Parameters to Predict Plastic Part Quality in Injection Moulding  \nManafAl-Ahmad1*, Song Yang1, Yi Qin1  \n1Centre for Precision Manufacturing, Department of DMEM, The University of Strathclyde, Glasgow, United Kingdom  \nAbstract. The plastic injection moulding process is a critical manufacturing technique renowned for its high productivity, cost-effectiveness, and ability to produce intricate plastic components for various industries including medical and aerospace. The quality of the manufactured parts is influenced by several parameters, such as machine settings and mould characteristics, particularly thermal aspects. This paper specifically investigates the influence of primary machine parameters on part quality, excluding considerations of time, mould features, and cooling channel geometries. By focusing on the machine parameters and employing advanced machine learning methods, a comprehensive understanding is developed on how these factors can be utilised to predict the quality of the parts produced. The findings provide valuable insights into optimising the injection moulding  \nprocess to enhance product quality and consistency.  \n1 Introduction  \nThe quality of plastic parts is paramount in mass production, playing a critical role in industries such as medical equipment, aerospace, and micro and nano manufacturing. This importance has spurred extensive research into the plastic injection moulding process and the impact of various parameters on part quality. These parameters originate from both machine and mould characteristics, with cavity parameters intricately linked to machine settings. Consequently, this study focuses on machine parameters to determine their influence on part quality, employing Machine Learning (ML) techniques for quality prediction.  \nThe following sections outline the stages of the injection process and highlight the key parameters involved. Certain parameters remain fixed after cavity and cooling channel design, emphasising the importance of monitoring modifiable parameters during the injection process for quality prediction. The impact of process parameters on quality criteria has been widely studied in manufacturing. For example, one study [1] examined the effects of nozzle temperature, screw rotational speed, mould temperature, and cooling time on energy consumption. Other research [2][3] examined the impact of melting temperature, injection pressure, packing pressure, and packing time, concluding that packing pressure is optimal for  \n* [Corresponding autour :](Corresponding autour : manaf.al-ahmad@strath.ac.uk)[ ](Corresponding autour : manaf.al-ahmad@strath.ac.uk)[manaf.al-ahmad@strath.ac.uk](Corresponding autour : manaf.al-ahmad@strath.ac.uk)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nmaximising tensile strength.Additionally, the Taguchi method has been employed to analyse the optimal parameters affecting part quality [4], including mould temperature [5] . Further studies [6] have focused on the effects of pressures at various stages ofthe process—such as injection pressure, holding pressure, and back pressure—on the final product quality. Moreover, Artificial Neural Networks and Support Vector Machines have been utilised to classify the quality of produced parts [7] .  \n2 Experimental setup  \nThe plastic parts were initially designed and simulated using SolidWorks Plastic software. This software facilitated virtual simulations to optimise parameters such as mould filling, cooling times, and material flow characteristics. SolidWorks Plastic was used to identify and theoretically implement optimal moulding conditions. These theoretical findings were then used to inform the physical experimental setup with targeted parameters for validation. The vi","cbCaihdfSIgHrSaS","https://ap.wps.com/l/cbCaihdfSIgHrSaS","pdf",1828719,1,6,"English","en",105,"# Introduction\n## Process parameters and quality criteria\n## Related work and parameter impacts\n# Experimental setup\n## Virtual design and simulation\n## Mould, materials, and sensor instrumentation","[{\"question\":\"Why is part quality critical in plastic injection moulding?\",\"answer\":\"Part quality strongly affects performance in demanding industries such as medical equipment, aerospace, and micro/nano manufacturing, making it a central concern in mass production.\"},{\"question\":\"Which factors does the study focus on, and what does it exclude?\",\"answer\":\"The study focuses on primary machine parameters and their thermal aspects for predicting quality, while excluding considerations of time, mould features, and cooling channel geometries.\"},{\"question\":\"How is experimental data collected for machine learning?\",\"answer\":\"A plastic injection moulding machine equipped with sensors records machine variables (e.g., hydraulic pressure, screw position, nozzle temperature, heating water temperature), while mould cavities use integrated pressure–temperature sensors; data are captured at high frequency and stored for analysis.\"}]","Integrating Machine Learning with Machine Parameters to Predict Plastic Part Quality in Injection Moulding | PDF",1785935345,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"integrating-machine-learning-with-machine-parameters-to-predict-plastic-part-quality-in-injection-moulding","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/integrating-machine-learning-with-machine-parameters-to-predict-plastic-part-quality-in-injection-moulding/126876/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is part quality critical in plastic injection moulding?","Question",{"text":75,"@type":76},"Part quality strongly affects performance in demanding industries such as medical equipment, aerospace, and micro/nano manufacturing, making it a central concern in mass production.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which factors does the study focus on, and what does it exclude?",{"text":80,"@type":76},"The study focuses on primary machine parameters and their thermal aspects for predicting quality, while excluding considerations of time, mould features, and cooling channel geometries.",{"name":82,"@type":73,"acceptedAnswer":83},"How is experimental data collected for machine learning?",{"text":84,"@type":76},"A plastic injection moulding machine equipped with sensors records machine variables (e.g., hydraulic pressure, screw position, nozzle temperature, heating water temperature), while mould cavities use integrated pressure–temperature sensors; 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