Tribological analysis of titanium alloy (Ti-6Al-4V) hybrid metal matrix composite through the use of Taguchi’s method and machine learning classifiers

TYPE O iginal Resea ch PUBLISHED 12 Ap il 2024 DOI 10.3389/ ma s.2024.1375200 OPEN ACCESS EDITED BY Na ayan L., Sa ee ha Uni e si y, India REVIEWED BY Mahesh Shewale, ASML, Uni ed S a es Na een Venka esh S., Vello e Ins i u e o Technology (VIT), India Subash Thanappan, KAAF Uni e si y College, Ghana *CORRESPONDENCE Sachin Saluankhe, [email p o ec ed] RECEIVED 23 Janua y 2024 ACCEPTED 21 Ma ch 2024 PUBLISHED 12 Ap il 2024 CITATION Ja i VS, Sawan DA, Deshpande R, Saluankhe S, Cep R, Nas EA and Mahmoud HA (2024), T ibological analysis o i anium alloy (Ti-6Al-4V) hyb id me al ma ix composi e h ough he use o Taguchi’s me hod and machine lea ning classi ie s. F on . Ma e . 11:1375200. doi: 10.3389/ ma s.2024.1375200 COPYRIGHT © 2024 Ja i, Sawan , Deshpande, Saluankhe, Cep, Nas and Mahmoud. This is an open-access a icle dis ibu ed unde he e ms o he C ea i e Commons A ibu ion License (CC BY). The use, dis ibu ion o ep oduc ion in o he o ums is pe mi ed, p o ided he o iginal au ho (s) and he copy igh owne (s) a e c edi ed and ha he o iginal publica ion in his jou nal is ci ed, in acco dance wi h accep ed academic p ac ice. No use, dis ibu ion o ep oduc ion is pe mi ed which does no comply wi h hese e ms. T ibological analysis o i anium alloy (Ti-6Al-4V) hyb id me al ma ix composi e h ough he use o Taguchi’s me hod and machine lea ning classi ie s Vijaykuma S. Ja i1, Dh u A. Sawan 1, Rashmi Deshpande2, Sachin Saluankhe3*, Robe Cep4, Emad Abouel Nas 5and Hai ham A. Mahmoud5 1Symbiosis Ins i u e o Technology, Symbiosis In e na ional Deemed Uni e si y, Pune, India, 2Depa men o Ins umen a ion Enginee ing, D Y Pa il Ins i u e o Technology, Sa i ibai Phule Pune Uni e si y, Pune, India, 3Depa men o Biosciences, Sa ee ha School o Enginee ing, Sa ee ha Ins i u e o Medical and Technical Sciences, Gazi Uni e si y Facul y o Enginee ing, Depa men o Mechanical Enginee ing, Mal epe, Tü kiye, 4Depa men o Machining, Assembly and Enginee ing Me ology, Facul y o Mechanical Enginee ing, VSB-Technical Uni e si y o Os a a, Os a a, Czechia, 5Depa men o Indus ial Enginee ing, College o Enginee ing, King Saud Uni e si y, Riyadh, Saudi A abia The p epa a ion and ibological beha io o he i anium me al ma ix (Ti- 6Al-4V) composi e ein o ced wi h ungs en ca bide (WCp) and g aphi e (G p) pa icles we e in es iga ed in his s udy. The s i cas ing p ocedu e was used o ab ica e he i anium me al ma ix composi es (TMMCs), which had 8 weigh pe cen o WCp and G p. The ibological s udies we e designed using Taguchi’s L27 o hogonal a ay echnique and we e ca ied ou as wea es s using a pin- on-disc de ice. Acco ding o Taguchi’s analysis and ANOVA, he mos signi ican ac o s ha a ec wea a e a e load and dis ance, ollowed by eloci y. The wea p ocess was asce ained by scanning elec on mic oscopy in es iga ion o he wo n su aces o he composi e specimens. Pea son’s hea map and Fea u e impo ance (F- es ) we e plo ed o da a analysis o s udy he signi icance o inpu pa ame e s on wea . Machine lea ning classi ica ion algo i hms such as k- nea es neighbo s, suppo ec o machine, and XGBoos algo i hms accu a ely classi ied he wea a e da a, gi ing an accu acy alue o 71.25%, 65%, and 56.25%, espec i ely. KEYWORDS i anium me al ma ix composi e, K-nea es neighbo ing, suppo ec o machine, XGBoos , wea a e, ibology 1 In oduc ion Ti anium alloys a e widely used in many echnical applica ions due o hei excellen combina ion o high ha dness, wea esis ance, s eng h, co osion esis ance, s i ness, and low densi y (Jiaoe al., 2018;Su eshe al., 2018;Cao and Liang, 2020). Ti anium alloys a e widely u ilized o hei excep ional s eng h- o-weigh a io o lowe ene gy consump ion, inc ease p oduc i i y, and ex end p oduc li e in he au omo i e, ae ospace, spo s, anspo a ion, and medical equipmen indus ies (A a e al., 2018;Chaoe al., F on ie s in Ma e ials 01 on ie sin.o g Ja i e al. 10.3389/ ma s.2024.1375200 2019;Pe undyu aiThanga ele al., 2020). Ti anium me al ma ix composi es (TMMCs) can be b oadly classi ied in o wo ypes based on he shape and dis ibu ion o ein o cemen s: con inuously ein o ced TMMCs and discon inuously ein o ced TMMCs (Guoe al., 2012;Jiaoe al., 2018;Haya e al., 2019). WC, Al2O3, TiB, CNTs, SiC, FE3O4, B4C, TiC, G , and o he ce amic pa icles and whiske s commonly ein o ce TMMCs (Lie al., 2015; Zhange al., 2016;Sune al., 2020). F a y e al. (F a ye al., 2003) epo ed ha he mechanical and physical p ope ies o pu e i anium ein o ced wi h 10 weigh pe cen WP we e compa able o hose o he Ti-6Al-4V (Ti64) alloy. A s udy by Choe e al. (Choee al., 2005a;Choee al., 2005b) ound ha he size o he WP signi ican ly in luences he mechanical cha ac e is ics o WP/Ti composi es. A ange o mic os uc u es wi h good ensile s eng h and elonga ion we e p oduced as a consequence o Wang e al.’s in es iga ions o he a ied size dis ibu ion o TiC ein o cemen o i anium by DED (Wange al., 2018). Using Ti/B4C composi e powde eeds ocks, Xia e al. p oduced TiB + TiC ein o ced i anium in si u. They also ho oughly examined he de elopmen o he in si u mic os uc u e and he in e ac ion zone be ween he i anium and ce amic ein o cemen (Xiae al., 2017). Fu he mo e, in si u B4C/BN ein o ced Ti6Al4V composi es we e s udied by Gup a e al. The main issue was wea pe o mance, and s eng hening educed he sliding coe icien o ic ion (COF) by hal compa ed o he Ti6Al4V ma ix (Gup ae al., 2018). Using an in-si u app oach, Choi e al. p oduced hyb id composi es o Ti6Al4V alloy wi h ein o cemen s TiB and TiC ha had a cons an ein o cemen alloca ion. The esul s showed a signi ican ela ionship be ween ein o cemen con en and mo e excellen TMC wea esis ance, wi h mo e ein o cemen con en esul ing in lowe wea loss (Kime al., 2011). A e elec oless pla ing o manu ac u e coppe -coa ed CNTs, Wange al. (2017) used spa k plasma sin e ing p ocedu es o c ea e coppe ma ix composi es. The esul s showed ha elec oless pla ing signi ican ly imp o ed he mechanical p ope ies by enhancing he elemen link be ween coppe and ca bon nano ubes and enabling uni o m dispe sion o CNTs. By using elec oless nickel pla ing and SPS o c ea e composi es o g aphi e lakes and coppe , Ren e al. d ama ically inc eased he bonding a he g aphi e/coppe base con ac . Acco ding o he indings, he bending cha ac e is ics and coe icien o he mal expansion we e signi ican ly imp o ed by ins alling he NieP ansi ion laye (Chene al., 2017). When 0.35w % o mul i-walled ca bon nano ubes (MWCNTs) was added, Kondoh e al. disco e ed ha he ensile pa ame e s, such as s eng h and yield, inc eased by up o 27% and 42%, espec i ely, in con as o hose o pu e i anium (Kondohe al., 2008). Wang e al. used an 823K sin e ing empe a u e in conjunc ion wi h a spa k plasma sin e ing echnique o c ea e a TMC composi e. The indings demons a ed ha when he olume ac ion o MWCNTs eached 0.4 weigh pe cen , he ma e ial’s comp essi e s eng h and yield s eng h bo h a ained hei maximum alues. Then, when he MWCNT con en was aised e en u he , he comp essi e s eng h d opped (Wange al., 2015). Jin e al. s udied he p oduc ion o pu e i anium powde wi h TiB2 ein o cing pa icles using a selec i e lase mel ing me hod. All so s o wea cha ac e is ics, including adhesion, ab asion, and oxida ion, we e imp o ed due o he Ti/TiB2 composi es (Jine al., 2021). The i anium and Z O2 nanopa icle composi es ha Abd- Elwahed e al. p oduced we e made ia powde me allu gy. The esul s demons a ed ha aising he usual load imp o ed he bonding, wea , and ic ion p ope ies, inc eased he o al amoun o Z O2nanopa icles, and enhanced he sliding wea a e (Abd- Elwahede al., 2020). An e al. assessed he ibological beha io s o TMC coa ings a high empe a u es. The indings demons a e ha delamina ion, plowing, and oxida ion wea p ocesses occu a 500C oge he wi h inc eased wea a es o TMC coa ings (AnQie al., 2019). Rega ding ha dness and wea esis ance a oom empe a u e, An e al.’s es s (Ane al., 2018;AnQ.e al., 2019) show ha he hyb id TiBand TiC pa icles boos ed wi h i anium coa ing and in e -g ow h ce amic s uc u es ou pe o m he TiB/Ti64 coa ing. Fa ias concen a ed on how he ibological cha ac e is ics o he TiC ein o cing pa icles wi h open po osi y in TMCs we e a ec ed by spa k plasma sin e ing. The s udy’s indings e ealed ha adding TiC pa icles enhanced ibological cha ac e is ics like wea esis ance, coe icien o ic ion, and nano ha dness (Fa íase al., 2019). Insu icien bonding be ween he ein o cemen pa icles and ma ix migh cause ce amic pa icles o unc ion as an ab asi e ma e ial, as pe he indings o Ram P abh e al. The dimensions, mass, and mo phology o he al e na i e phase ein o ced pa icles, he equi alen ma e ial, he load, he mic os uc u e, he en i onmen , and he humidi y can all impac he wea esis ance o composi e ma e ials (Rame al., 2014). Room empe a u e esea ch was done by Huang Xie, who also ca ied ou sliding wea ials on TMCs wi h mild s eel g ade 35. I on oxide addi i es, independen o load, a ely imp o e he TMC’s wea pe o mance because hey equi e lub ica ing capabili ies. The MLG/Fe2O3 nanocomposi e and MLG wi h Fe2O3 mechanical combina ion may signi ican ly imp o e he wea pe o mance (Xiee al., 2021). Zhou e al. examined he e ec o g aphene/Fe2O3 nanocomposi es on he ibological pe o mance o he TC11 alloy. They ound ha on he inju ed su ace, a hin, s able double ibolaye consis ing o laye s p ima ily composed o MLG and Fe2O3 was c ea ed, signi ican ly educing wea and ic ion. This esea ch shows ha adding speci ic nanopa icles o ma e ials can imp o e hei ibological p ope ies (Zhoue al., 2017). Ti anium hyb id composi es wi h single and mul iple ein o cemen s we e c ea ed by Lixia Xi e al. and ab ica ed using SLM. The hyb id composi e esul se be ween he ein o cemen s and ma ix gene a ed he in e acial s uc u e. The wea esis ance and CoF cha ac e is ics a e enhanced when TiC and TiN ein o cemen pa icles a e in oduced (Xie al., 2021). P akash conside ed emo ing his p ope y lag. Ti anium alloy (Ti-6Al-4V) is used o o i y bo on ca bide (B4C) ce amic pa icles using powde me allu gy (PM). As a esul o his esea ch, a newe composi e was c ea ed and es ed, imp o ing ha dness, co osion esis ance, and educed densi y. The wea pe o mance o he composi e specimens is mo e a ec ed by he applied loads han by he amoun o B4C added. Scanning elec on mic oscopy esul s demons a e ha he B4C- ein o ced Ti-6Al-4V composi e has be e wea esis ance han he un ein o ced Ti alloy and shows signs o mildly wo n su aces (Soo yaP akashe al., 2016). Hu in es iga ed and analyzed he quali y o he componen s, he p ocesses by which mic os uc u es o m, and he e icacy o F on ie s in Ma e ials 02 on ie sin.o g Ja i e al. 10.3389/ ma s.2024.1375200 wo kpiece wea in TiB- ein o ced Ti ma ix composi es, which we e p oduced using he LENS app oach. The esul s sugges ha TiB-TMCs, wi h hei inno a i e mic os uc u es and TiB ein o cemen , exhibi ed supe io wea pe o mance compa ed o bulk componen s composed o comme cially pu e i anium. Fu he mo e, by a ying he lase powe , he cha ac e is ics o he p oduced componen s we e imp o ed wi h ewe in e nal de ec s, leading o be e wea pe o mance (Hue al., 2017). Using i anium alloy ein o cemen o imp o e he g apheme’s ul ima e comp essi e s eng h, ensile s eng h, wea esis ance, he mal conduc i i y, and di usi i y, Gu buz e al. in es iga ed he p ope ies o ibological, mechanical, and he mal aspec s (Gü büze al., 2021). Measu ing he pa icula wea a e is c ucial o he ma e ial selec ion and op imiza ion p ocess. Enginee s can choose he bes ma e ials o a gi en applica ion by compa ing he wea a es o se e al magnesium o magnesium alloys wi h o he ma e ials. This is especially c ucial in sec o s like manu ac u ing, ae ospace, and au omo i e, whe e wea esis ance signi ican ly impac s he longe i y and dependabili y o componen s. A i icial In elligence (AI) has yielded many bene i s and made signi ican s ides in se e al indus ies, including manu ac u ing and heal hca e. A i icial in elligence (AI) has shown o be ex emely use ul in he medical indus y o asks including disease diagnosis, he apy planning, medica ion de elopmen , and pa ien moni o ing (Ma hews, 2019;Buccinoe al., 2023a;Buccinoe al., 2023b; Yange al., 2023). A ype o a i icial in elligence called machine lea ning algo i hms has been widely used o e alua e complex medical da a, spo ends, and gene a e p ecise o ecas s. As a esul , he e has been an imp o emen in he p ecision o diagnoses, he c ea ion o cus omized ea men p og ams, and he gene al quali y o pa ien ca e. Simila ly, AI has conside ably changed ope a ions in he manu ac u ing sec o by acili a ing p edic i e main enance, inc easing p oduc i i y, and s eamlining p ocedu es (Malekie al., 2022;Malekie al., 2023). Demand o ecas ing, supply chain op imiza ion, au oma ion, and quali y con ol ha e all bene i ed om using machine lea ning algo i hms. These applica ions ha e imp o ed ope a ional e ec i eness, lowe ed p oduc ion cos s, and imp o ed p oduc quali y. The in eg a ion o E olu iona y Compu ing wi h Machine Lea ning algo i hms has ecei ed e y li le esea ch a en ion despi e he ema kable ad ancemen s in AI and Machine Lea ning. E olu iona y compu ing is a sub ield o a i icial in elligence ha uses me hods om na u al e olu ion, including gene ic algo i hms, pa icle swa m op imiza ion, and an colony op imiza ion, o ackle challenging op imiza ion issues. Combining machine-lea ning echniques and e olu iona y compu ing has excellen p omise in se e al ields. Resea che s can mo e e ec i ely handle complex op imiza ion and p edic ion asks by combining he adap i e sea ch capabili ies o E olu iona y Compu ing wi h he lea ning and p edic i e powe s o Machine Lea ning algo i hms. This in eg a ion can be u he implemen ed in a ious ma e ial science and manu ac u ing domains (Dhunganae al., 2019;Dhunganae al., 2021;Sadeke al., 2021; Dhunganae al., 2022;Mish a and Ja i, 2023a;Dhunganae al., 2023;G ecoe al., 2023). This hyb id echnique can help ind he bes solu ions in complex p oblem spaces, inc ease he p ecision and e ec i eness o op imiza ion algo i hms, and imp o e ea u e selec ion in machine-lea ning models. Es ima ing he p ecise wea a e o Hyb id Me al Ma ix Ti anium alloy is essen ial in sec o s whe e wea esis ance is a i al componen . While machine lea ning has demons a ed po en ial in p edic i e modeling, e alua ing hei pe o mance and choosing which algo i hm wo ks bes o his pa icula use case is necessa y. Fu he mo e, he e needs o be a mo e ho ough analysis and compa ison o a ious algo i hms and a pauci y o esea ch in his a ea. Thus, compa ing he e ec i eness o machine lea ning algo i hms, namely K-Nea es Neighbou ing (KNN), Suppo Vec o Machine (SVM), and XGBoos classi ica ion in o ecas ing he p ecise wea a e o he Hyb id Me al Ma ix Ti anium alloy is he opic his esea ch s udy a emp s o add ess. This s udy used he s i cas ing me hod o c ea e a i anium alloy, Ti-6Al-4V, wi h ein o cemen s made o ungs en ca bide (WC) and g aphi e (G ) hyb id me al ma ix composi e. P ocess a iables like load, sliding eloci y, sliding dis ance, and ibological expe imen s we e pe o med based on he Taguchi L27 o hogonal a ay. 2 Ma e ials and me hods The p ima y ma ix ma e ial used was he i anium alloy Ti- 6Al-4V; Table1 shows he chemical composi ion o he ma ix alues o his alloy. The pa icles ein o ced wi h WC and G ha e been selec ed. The a e age size o he g aphi e pa icles was 25μm, whe eas he WC pa icles we e 45μm. The equi ed amoun o Ti- 6Al-4V i anium alloy was mel ed in a g aphi e c ucible using an elec ical u nace. The ein o cing pa icles we e hea ed o 500°C o emo e he mois u e. A speci ic quan i y o ein o cing pa icles was mixed wi h he i anium alloy. The hyb id composi e ma e ial was egula ly blended. A e he hyb id composi e was inse ed in o he p epa ed die a 800°C, i was le o solidi y a oom empe a u e. The sample’s mic os uc u es and wo n su aces we e examined using op ical and scanning elec on mic oscopy. Mic oha dness es e o model-Mic oha dness es e om OMNI ech, MVH-1 au oma ic es load 10gm o 1,000gm employed o mic oha dness measu emen . As indica ed in Figu e1, he composi e specimens’ d y sliding wea quali ies we e e alua ed using he DUCOM pin- on-disc sliding wea es ing appa a us (Manu ac u e : DUCOM, Bangalo e, India). The ASTM G99-95 ules we e ollowed when conduc ing he d y sliding wea es ing. The pin was cleaned wi h ace one, and i s ini ial mass was measu ed wi h a digi al elec onic balance. A e ha , he pin was held up agains a e ol ing EN-32 s eel disk (coun e ace) wi h 65 HRC ha dness h oughou he es . Figu e2A,B depic s he cas ed and machined samples, espec i ely. Th oughou he es ing, adjus men s we e made o he dis ance, eloci y, and a e age load. A he end o each es , he pin’s ul ima e mass was measu ed a e being cleaned wi h ace one. We calcula ed he mass loss o he pin due o sliding wea by aking he di e ence be ween i s ini ial and inal masses. The olume loss owing o wea was calcula ed using he densi y alues linked wi h he pin. Nex , he wea a e o he composi e pins was asce ained. The ollowing is he p ocess o ca ying ou he wea es : Fi s , he es sample is ca e ully weighed on a s a e-o - he-a digi al balance, and i s o iginal mass is eco ded. The specimen is hen secu ely secu ed using he no ch, and i s su ace is posi ioned so ha i makes con ac wi h he disk. The ack adius is hen modi ied o mee he unique equi emen s o he es . Following p ope F on ie s in Ma e ials 03 on ie sin.o g Ja i e al. 10.3389/ ma s.2024.1375200 TABLE 1 Chemical composi ion o Ti6Al4V. Alloying elemen s Ti Al V Fe O N C Chemical con en (w %) 85.096 7.75 6.5 0.34 0.02 0.04 0.05 FIGURE 1 Wea es on disc (Mish a and Ja i, 2023b;Mish ae al., 2023) FIGURE 2 (A) Cas wea pins. (B) Machined wea pins. specimen posi ioning speci ied no mal loads a e supplied, and he sliding eloci y is se in compliance wi h he es pa ame e s. The es is hen un o co e he speci ied dis ance o e a compu ed ime in e al. Fo e e y es , he pin olume loss was de e mined using he pin-heigh loss me hod. Each es was conduc ed h ee imes o gua an ee epea abili y, and he a e age o he h ee es s was used o de e mine he wea a e using Eq.(1). This p ocess is epea ed o o he specimens wi h a ying olume pe cen ages and could be F on ie s in Ma e ials 04 on ie sin.o g Ja i e al. 10.3389/ ma s.2024.1375200 TABLE 2 Inpu p ocess pa ame e s. Le el Sliding speed (m/s) Load (N) Sliding dis ance (m) 1 2 20 500 2 4 30 1,000 3 6 40 1,500 es ed unde a ious condi ions. This me hod makes i possible o compa e wea cha ac e is ics unde a ious ci cums ances. The wea pa ame e s chosen o he es ing based on machine capaci y, li e a u e analysis, and pilo ials a e shown in Table2. Pilo expe imen s we e conduc ed o asce ain he p ac ical limi s o he p e iously indica ed pa ame e s necessa y o he wea o occu in a s eady s a e. Re e ing o ASTM G99-95, he pin used in he wea es is 30mm long and has an 8mm diame e . Wea a e(mm3/Nm)=(Volumeloss∗Ha dness)/ (No malLoad∗Slidingdis ance)(1) The Taguchi echnique aims o minimize a ia ion in a p ocess h ough obus expe imen design. The main objec i e o he p ocedu e is o p o ide he make wi h high-quali y ou pu a a low cos . D . Genichi Taguchi o Japan de eloped he Taguchi me hod and has pe sis ed in using ha a ia ion. Thus, bo h he p oduce and socie y a e impac ed by low p ocess quali y. He de eloped a sys em o designing expe imen s o in es iga e how di e en pa ame e s a ec he mean and a iance o a p ocess pe o mance cha ac e is ic ha shows how well he p ocess is doing. Taguchi’s expe imen al design ga he s he necessa y da a o iden i y he a iables signi ican ly in luencing p oduc quali y wi h mino expe imen a ion, sa ing ime and esou ces. This is accomplished using o hogonal a ays o o ganize he a iables in luencing he p ocedu e and he magni udes a which hey should be shi ed. Key p ocess ac o s we e iden i ied using analysis o a iance. An L27 o hogonal a ay was chosen o he cu en expe imen , as indica ed in Table3. This s udy classi ies and p edic s he wea a e o hyb id me al ma ix composi e using KNN, SVM, and XG Boos machine lea ning classi ica ion echniques. To build a con usion ma ix and AUC-ROC cu es o u he in-dep h analysis and o p ecisely assess he wea a e using classi ica ion, sample da a was cons uc ed using a syn he ic da a gene a ion ool in MATLAB based on he expe imen al da a. The classi ica ion me hod is a supe ised lea ning echnique ha ca ego izes new obse a ions using aining da a. Using he da ase o supplied obse a ions, a p og am lea ns how o ca ego ize esh obse a ions in o a ious classes o g oups in he classi ica ion p ocess. The da a in he cu en s udy is di ided in o wo g oups acco ding o whe he he alue exceeds o alls sho o he 0.084 a e age o all wea a e alues. The con usion ma ix was plo ed using he Py hon me ics module om he sklea n package. In o de o achie e p ecise ou comes o da a p edic ion, he da ase was spli in o wo sec ions: 80 pe cen o aining da a and 20 pe cen o andom es da a. The con usion ma ix is composed o h ee ypes o da a: False posi i es (FP) and False nega i es (FN), which e lec w ongly o ecas ed da a o sugges ha he e was an e o in he p edic ion p ocess; and T ue posi i es (TP) and T ue nega i es (TN), which ep esen success ully an icipa ed da a. K- old (k = 1) was conside ed o he numbe o neighbo s (k), and KNN, SVM, and XGBoos classi ica ion was pe o med o ob ain a wide ange o classi ica ion da a. Pea son’s hea map analysis and ea u e impo ance plo (F- es ) we e plo ed u he o unde s and he signi icance o ea u es on wea loss. A lowcha explaining hea map analysis is depic ed in Figu e3. The cu en s udy uses a k-nea es neighbo (kNN) classi ica ion me hod, which inds he closes Euclidean dis ance be ween he a e age alue and each wea a e alue ( e e o Figu e4A). Fo he kNN classi ica ion me hod, he e a e a ious ypes o hype pa ame e op ions, such as: a) The numbe o neighbo s decides he numbe o nea es neighbo s o classi y each alue o poin in he a ge da ase . b) Dis ance me ic, which is used o measu e dis ance be ween 2 poin s. c) Dis ance weigh decides whe he he dis ance is equal o in e se (1/dis ance). SVMs we e c ea ed o sol e bina y classi ica ion issues. Howe e , when compu a ionally demanding mul iclass p oblems become mo e common, se e al bina y classi ie s a e buil and coupled o c ea e SVMs ha can ca y ou hese mul iclass classi ica ions using bina y me hods. The SVM classi ie unc ion (SVC) is de ined using inpu pa ame e s like he ype o ke nel used, he ma gin, and he hype plane. Once he da a is ained using he SVM classi ie unc ion, he es da a is p edic ed based on he ained and alida ed da a. The wo k low o he SVM classi ica ion algo i hm is depic ed in Figu e4B. XGBoos is a scalable and accu a e g adien - boos ing solu ion ha pushes he compu a ional bounda ies o boos ed ee algo i hms, p ima ily accele a ing compu a ional speed and machine lea ning model pe o mance. As pa o an ensemble app oach mean o p oduce supe io p edic ions wi h imbalanced- class da a, Ex eme G adien Boos ing, o XGBoos , has become inc easingly popula as a p edic ion algo i hm in ecen yea s. XGBoos classi ica ion is simila o he F- es as i selec s he bes ea u es o p edic he da a a e i ains and alida es he aining da ase . I con inues un il he bes ea u e, which has he mos signi icance on he da a, is selec ed, and no ea u es emain in he da ase o e alua e. The wo k low o he XGBoos classi ica ion algo i hm is depic ed in Figu e4C. The alues a e eco ded a e he da ase is collec ed using wea loss expe imen s. Hea map analysis and F- es a e done o iden i y he mos signi ican ea u es o he da ase . Then, supe ised machine lea ning algo i hms, in his case, classi ica ion algo i hms like kNN, SVM, and XGBoos , a e used o classi y he da a based on he aining and alida ion da ase , and he da a is p edic ed using he ained model. The esul s a e hen analyzed o he bes wea loss a e based on he bes ea u es. 3 Resul s and discussions In o de o es ima e he p ecise wea a e o hyb id composi es, his sec ion p esen s he wea a e esul s oge he wi h s a is ical analysis and a machine lea ning echnique. The expe imen al pa ame e s used o he s udies a e shown in Table3, oge he wi h he co esponding obse ed wea a e alues. F on ie s in Ma e ials 05 on ie sin.o g Ja i e al. 10.3389/ ma s.2024.1375200 TABLE 3 Expe imen al layou wi h obse ed alues. S. No. Load (N) Veloci y (m/s) Dis ance (m) Wea a e (mm3/N-mm) 1 20 2 500 0.00021959 2 20 2 500 0.00026440 3 20 2 500 0.00044366 4 20 4 1,000 0.00018150 5 20 4 1,000 0.00009635 6 20 4 1,000 0.00015909 7 20 6 1,500 0.00056914 8 20 6 1,500 0.00007618 9 20 6 1,500 0.00004332 10 30 2 500 0.00014341 11 30 2 500 0.00028083 12 30 2 500 0.00024797 13 30 4 1,500 0.00007569 14 30 4 1,500 0.00006075 15 30 4 1,500 0.00006672 16 30 6 500 0.00017328 17 30 6 500 0.00024498 18 30 6 500 0.00023602 19 40 2 1,500 0.00006797 20 40 2 1,500 0.00005676 21 40 2 1,500 0.00007618 22 40 4 500 0.00023751 23 40 4 500 0.00011876 24 40 4 500 0.00014341 25 40 6 1,000 0.00010531 26 40 6 1,000 0.00009635 27 40 6 1,000 0.00004369 3.1 S a is ical analysis The d y sliding wea es was pe o med using pin-on-disc equipmen . The analysis o a iance and signal- o-noise (S/N) a io echniques we e used o de e mine he signi ican pa ame e s. The S/N a ios a e used o e alua e how noise ac o s a ec pe o mance me ics. Th ee S/N a ios a e ypical and equen ly u ilized; hey e alua e he deg ee o a ia ion in he answe da a and he deg ee o which he a e age esponse esembles he a ge . Theo e ically, highe , smalle , and be e a e he be e o hem. Smalle is be e . The guideline was applied in his s udy o educe wea a e. The signal- o-noise a io, o S/N a io, gauges he suscep ibili y o he quali y a ibu e unde s udy o expe imen ally induced uncon ollable e en s. F on ie s in Ma e ials 06 on ie sin.o g Ja i e al. 10.3389/ ma s.2024.1375200 FIGURE 3 Hea map lowcha . FIGURE 4 (A) kNN classi ica ion lowcha . (B) SVM classi ica ion lowcha . (C) XG-Boos classi ica ion lowcha . TABLE 4 Analysis o a iance o wea a e. Sou ce DF Seq SS Adj SS Adj MS F P Load (N) 2 0.0000001 0.0000001 0.0000001 3.31 0.057 Veloci y (m/s) 2 0.0000001 0.0000001 0.0000001 0.49 0.622 Dis ance (m) 2 0.0000001 0.0000001 0.0000001 3.35 0.056 E o 20 0.0000002 0.0000002 0.0000002 To al 26 0.0000004 An analysis o a iance (ANOVA) was pe o med o in es iga e he e ec s o load, eloci y, and dis ance on he wea a e; he esul s a e shown in Table4. The load and he dis ance subs an ially impac ed he wea a e, acco ding o he F- es and p- alue esul s. Table5 and Table6 depic he esponse able o signal- o-noise a ios and means, espec i ely. As pe he esul s, he load ollowed by dis ance and eloci y a ec s he wea a e. Figu e5A displays he plo o he wea a e’s p ima y in luence. I is e iden ha he wea a e ends o inc ease wi h highe eloci ies and no mal loads. Fu he mo e, Figu e5B illus a es he wea a e in e ac ion plo . The lines in e sec o e eal a s ong in e ac ion e ec be ween he wea a e and he load and eloci y, load and dis ance, and eloci y and dis ance. Figu e5C displays a esidual plo o he pa icula wea a e. A s anda d p obabili y plo deno es a no mal dis ibu ion o he F on ie s in Ma e ials 07 on ie sin.o g Ja i e al. 10.3389/ ma s.2024.1375200 TABLE 5 Response able o signal- o-noise a ios. Le el Load (N) Veloci y (m/s) Dis ance (m) 1 71.94 75.31 72.70 2 76.40 78.33 78.88 3 79.96 74.66 78.78 Del a 8.02 3.67 6.18 Rank 1 3 2 TABLE 6 Response able o means. Le el Load (N) Veloci y (m/s) Dis ance (m) 1 0.000228 0.000200 0.000229 2 0.000170 0.000127 0.000114 3 0.000105 0.000176 0.000116 Del a 0.000123 0.000073 0.000116 Rank 1 3 2 esiduals. The e was no e idence o da a skewness o ou lie s in he his og am plo . The e we e no appa en pa e ns in he esidual e sus o de ed plo o he esidual e sus i ed plo . These indings imply ha di e ences in ime o en i onmen al condi ions did no cause any inaccu acies in he da a ga he ing. The wea a e indica es he amoun o ma e ial loss o wea olume pe uni o sliding dis ance and uni load. Se e al wea egimes we e ound in he wea map by analyzing he wea a e alues. A di e en combina ions o sliding eloci ies and no mal loads, hese wea egimes o e insigh s in o he p edominan wea mechanisms, including adhesi e wea , ab asi e wea , delamina ion, plas ic de o ma ion, oxida ion, and mel ing. Changes in wea a e o e ela i ely modes di e ences in pa ame e s like a e age load, sliding eloci y, empe a u e, and ime a e e e ed o as wea ansi ions. Wea ansi ion cha s usually iden i y and cha ac e ize a ious wea egimes o mechanisms. Low wea a es and mild con ac condi ions wi h a mix o adhesi e and oxida i e wea p edomina e a e cha ac e is ics o he mild wea egime. On he o he hand, he se e e wea egime is cha ac e ized by ele a ed wea a es and usually a ises om mo e igo ous wo king ci cums ances. Nume ous mechanisms, including ab asi e wea , plowing, and delamina ion, can con ibu e o se e e wea . Plowing emo es ma e ial due o he in e ac ion o mic oscopic aspe i ies on one su ace wi h ano he , which causes ab asi e wea . Rough su aces o he p esence o ha d pa icles a e linked o his mechanism. Oxida i e wea is caused by he in e ac ion o he alloy wi h ambien oxygen, p oducing oxide laye s on he alloy su ace. Inc eased wea a e and su ace de e io a ion may esul om his mechanism. The e m “delamina ion” desc ibes how laye s FIGURE 5 (A) Main e ec s plo o wea a e. (B) In e ac ion plo o wea a e. (C) Residual plo s o wea a e. come away om he su ace o a subs ance. I equen ly happens in a eas wi h a concen a ion o localized ension, which causes wea deb is and su ace oughness o accumula e. This phenomenon is known as plas ic de o ma ion, when a ma e ial de o ms and lows due o an applied a e age load. A wo-dimensional g aph o he wea a e alues is displayed in Figu es6A–C. The colo ep esen ed he wea a e alues. This image made he analysis o wea a e a ia ions unde a ious es se ings possible. These wea maps help de e mine p e ailing wea egimes, comp ehend he wea mechanisms, and choose he bes ma e ials and ope a ing se ings o speci ic applica ions. This in es iga ion ound ha he wea a e dec eased a lowe eloci y and load alues and inc eased F on ie s in Ma e ials 08 on ie sin.o g Ja i e al. 10.3389/ ma s.2024.1375200 FIGURE 6 (A) Con ou plo o wea a e e sus load, eloci y. (B) Con ou plo o wea a e e sus load, dis ance. (C) Con ou plo o wea a e e sus eloci y, dis ance. a highe eloci y and load alues. The wea map’s a ied colo ed zones co espond o dis inc wea a es a a ying eloci ies and loads. The ansi ion lines we e used o de ine he zones by he expe imen al se up. The ideal eloci y and load combina ion ha educes wea a e can be ound h ough wea map analysis, making i possible o iden i y ope a ing si ua ions whe e he ma e ial exhibi s excellen wea pe o mance. This knowledge is a e e ence o echnical applica ions using he hyb id me al ma ix composi e alloy. This is especially impo an in he au omo i e, ae ospace, and biomedical indus ies, whe e wea esis ance is c i ical. Fo he p esen s udy, he wea a e is lowe be ween 3.5 and 4.5m/s eloci y and 25N–35N load; see Figu es6A,B shows ha he wea a e is lowe be ween 27.5 N and 40N load and 1,000m–1,500m dis ance. Figu e6C shows ha he wea a e is lowe be ween 2 and 4.5m/s eloci y and 1,250m–1,500m dis ance. 3.2 Op ical and SEM analysis The op ical mic og aph o he composi e is shown in Figu e7A. The sample has excellen compac ness and is ee o mic o- issu es and po es. The WCp-G p ein o cemen was e enly dis ibu ed h oughou he composi es o pe mi i s p esence in he Ti-6Al-4V ma ix. The Ti-6Al-4V ma ix has uni o mly dis ibu ed iny WCp- G p ein o cemen s, e en hough Ti anium Composi es In e ac ion does no obse e any eac ions. I is impo an o no e ha WCp and G p do no combine o o m a s aigh o wa d bina y combina ion bu p oduce a new in e ace s uc u e. The a e age alue o Vicke s Mic oha dness o he hyb id composi e ob ained using a mic oha dness es e is 892 VHN. SEM analysis o he wea su aces de eloped in o d y sliding wea in he s eady s a e egime p o ides a c ucial ool o accu a ely cha ac e izing he wea beha io o he composi es. Figu e7B shows he composi es’ wo n-ou su ace ollowing wea . The ex ao dina y ha dness o he composi e means ha he wo n-ou su aces a e ba ely pe cep ible. This example has a e y smoo h su ace because he WCp and GRp ein o cemen pa icles a e secu ely bonded o he ma ix phase a ha le el. I is also clea ha ein o cemen s ha e no wo n down only a li le. The sel -lub ica ing ac ion o he ibo su ace ein o cemen s causes his. The wo n su ace o he composi e makes he p esence o lamina ed laye s qui e e iden . In his pic u e, he laye has changed he easily obse able sliding su ace. The su aces also appea smoo h because o he ein o cing componen . 3.3 Wea a e p edic ion using machine lea ning classi ie Da a was ca ego ized using machine lea ning based on he ape angle o he squa e slo s in he s ainless s eel pla e. Using Py hon lib a ies, Pea son’s hea map and F- es plo s we e c ea ed o de e mine he ea u e impo ance o inpu pa ame e s, including load, eloci y, and dis ance. Pea son’s hea map analysis is plo ed as shown in Figu e8A, which depic s he minimal signi icance o load and eloci y on he wea a e. As shown in Figu e8B, a ea u e is deemed insigni ican i i s F- es alue is below he F-dis ibu ion alue. Howe e , any F- es alue o an inpu pa ame e o e he c i ical F-dis ibu ion will be ega ded as a signi ican ea u e o inpu pa ame e . The k-nea es Neighbo s (kNN) me hod p edic s he label o alue o a new da a poin by conside ing he labels o alues o i s k-nea es neighbo s in he aining da ase . In he cu en analysis o wea loss, he alues below he a e age alue o wea loss we e conside ed as 1, and he alues abo e he a e age alue we e conside ed as 0, e e o Figu e9A. Wea loss should be minimal as i will gi e be e esul s. The p edic ion accu acy o he kNN classi ica ion o wea loss was 71.25%, e e o Table7. F on ie s in Ma e ials 09 on ie sin.o g