[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-125006-105":59,"doc-detail-125006-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","characteristics-of-machining-data-and-machine-learning-models-a-case-study","Characteristics of Machining Data and Machine Learning Models - A Case Study","","Advances in computing—including IoT, cloud computing, and artificial intelligence—are pushing manufacturing toward automation to raise productivity. A key approach is deploying prediction models and machine learning (ML) algorithms directly in production. This paper provides a comprehensive review of AI implementation for machining materials and outlines methodology for building prediction models. It examines the characteristics of experimental machining data and the key attributes required for model development through a case study.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/characteristics-of-machining-data-and-machine-learning-models-a-case-study/125006/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/characteristics-of-machining-data-and-machine-learning-models-a-case-study/125006.png","ImageObject",300,407,{"name":92,"@type":93},"Liam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-30","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main objective of the paper?","Question",{"text":112,"@type":113},"To review how AI can be implemented in machining and to present a methodology for developing prediction models. It also discusses experimental data characteristics and model attributes using a case study.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the paper relate Industry 4.0 to manufacturing automation?",{"text":117,"@type":113},"It describes Industry 4.0 as connecting components through cyber-physical systems, enabling digital manufacturing. It also notes that AI/ML adoption in manufacturing is still limited by constraints like budget, support, and awareness.",{"name":119,"@type":110,"acceptedAnswer":120},"What distinguishes automation from smart manufacturing in the document?",{"text":121,"@type":113},"Automation is described as conducting manufacturing with minimal human intervention using a robust control system and limited sensors. 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One of the key technologies or preferable methods to increase the productivity is implementing prediction models or machine learning (ML) algorithms in production. This article is aimed to show a comprehensive review on AI implementation in machining of materials, and to present methodology in prediction model development. The characteristic of experimental data and the key attributes in the model development arepresented and discussed with a case study.  \n1. Introduction  \nIndustry 4.0 is a holistic approach of utilizing internet of things (IoT) and internet of services (IoS) where resources are connected together in cyber physical system (CPS) . Industry 4.0 has been adopted in developed countries and some developing countries in the recent years. Having the components such as Cyber-Physical Systems (IoT and IoS), Augmented reality, Virtual reality, Autonomous robots, Cloud computing, the Industry 4.0 perfectly conducts digital manufacturing for the satisfaction of the customers or clients. The practice of Artificial Intelligence (AI) and Machine Learning (ML) ranges from big giant applications to the tiniest deployment of the technologies. Both are commonly practiced in the social media networks, business operations, and marketing. Their contribution in manufacturing sectors is however limited for various reasons. They may be constrained by budget, support from stockholders includingthe Government, limited resources, and facilities etc., in the worst-to-worst case, because of the unawareness of their usability, credibility and benefits.  \nWagner et al.,[21] detailed how industry 4.0 impacts lean manufacturing system Cagnetti et al.,[3] presented challenges in implementing industry 4.0 and how new technological philosophy can be implemented in lean manufacturing. Kolla et al.,[13] presented challenges in small and medium scale industries (SMEs) and discussed how hybrid model consisting of lean manufacturing and industry 4.0 technologies will help SMEs, while readiness and maturity model was presented Schumacher et al.,[19] . Ortt et al.,[18] reviewed research articles published in this area in a last decade and discussed the implementation method. Gallo et al.,[8] conducted a systematic review on tools for implementing industry 4.0 in lean manufacturing system.  \nAutonomous material handling, numerical controlled machining, robotic assistance is included in automation of manufacturing process. Numerical controlled machining is preferred to use in order to have dimensional accuracy in near-net shape. No doubt, how robust product is manufactured, some machining processes must be conducted in the production of industrial products. For instance, drilling  \nand boring of hole, tapping, screwing are very common processes applied in the production. Conventional machining processes have been replaced by numerical controlled machining Machine selection, cutter selection, cutting parameters setting, process planning is controlled in automation.  \nOne of the challenges in the machining is to avoid geometrical errors and poor surface integrities, for which different algorithms have been attempted in the past. Though AI and ma","cbCaimznqtcPFbgS","https://ap.wps.com/l/cbCaimznqtcPFbgS","pdf",110134,"English","# Introduction\n## Related Literature on Machine Learning Models for Machining Data","[{\"question\":\"What is the main objective of the paper?\",\"answer\":\"To review how AI can be implemented in machining and to present a methodology for developing prediction models. It also discusses experimental data characteristics and model attributes using a case study.\"},{\"question\":\"How does the paper relate Industry 4.0 to manufacturing automation?\",\"answer\":\"It describes Industry 4.0 as connecting components through cyber-physical systems, enabling digital manufacturing. It also notes that AI/ML adoption in manufacturing is still limited by constraints like budget, support, and awareness.\"},{\"question\":\"What distinguishes automation from smart manufacturing in the document?\",\"answer\":\"Automation is described as conducting manufacturing with minimal human intervention using a robust control system and limited sensors. Smart manufacturing emphasizes connecting men, machines, tools, and sensors via a networked cyber-physical system for decision making with little or no human involvement.\"}]","Characteristics of Machining Data and Machine Learning Models - A Case Study | PDF"]