Minimising Defect Formation in Sand Casting of Sheet Lead: a Doe Approach

metals

Article Minimising Defect Formation in Sand Casting of Sheet Lead: A DoE Approach

Arun Prabhakar, Michail Papanikolaou * , Konstantinos Salonitis and Mark Jolly Manufacturing Theme, Cranfield University, Cranfield MK43 0AL, UK; a.prabhakar@cranfield.ac.uk (A.P.); k.salonitis@cranfield.ac.uk (K.S.); m.r.jolly@cranfield.ac.uk (M.J.) * Correspondence: m.papanikolaou@cranfield.ac.uk; Tel.: +44-(0)-1234-758235

Received: 28 January 2020; Accepted: 12 February 2020; Published: 13 February 2020

Abstract: Sand casting of lead sheet is a traditional manufacturing process used up to the present due to the special features of sand cast sheet such as their attractive sheen. Similarly to any casting process, sand casting of lead sheet suffers from the presence of surface defects. In this study, a surface defect type, hereby referred to as ‘grooves’, has been investigated. The focus has been laid on the identification of the main factors affecting defect formation in this process. Based on a set of screening experiments performed using Scanning Electron Microscopy (SEM) as well as the existing literature, a number of factors affecting the formation of such defects was identified and their corresponding significance was estimated using the Analysis of Variance (ANOVA) technique. The obtained results suggest that the most significant factor affecting defect formation in sand casting of lead sheet is the composition of the moulding mixture. Defect formation was also proven to be dependent on the sand grain fineness, the quality of the melt and some of the interactions between the aforementioned process parameters. Finally, an optimal set of process parameters leading to the minimisation of surface defects was identified.

Keywords: sand casting; sheet lead; defects

1. Introduction Lead is one of the first metals used in manufacturing, its use dating back to around 3000 BC [1,2]. The Romans used sand cast lead sheet for the manufacture of water pipes and coffins amongst other artefacts [3]. Nowadays, lead is used for a wide variety of applications including battery grids, ammunition and radiation protection. One of the most common lead products is sheet lead, being widely used in the construction industry mainly for roofing and flashing applications. The high malleability and ductility of lead combined with its high resistance to corrosion makes it an appropriate material for roofing applications and architectural cladding [4]. Lead sheet are also used in the manufacture of lead lined boards for radiation protection in the healthcare industry due to their high density (11,340 kg/m3) and attenuation coefficient (5.549 cm2/g at 100 KeV) especially for high energy X-rays [5,6]. There are three main methods for manufacturing lead sheet, namely: (a) rolling (also referred to as milling), (b) continuous casting and (c) sand casting [4,7,8]. Amongst the aforementioned methods, rolling and continuous casting are often preferred due to their low cost and thickness consistency. Despite being the oldest and most traditional method, sand casting is still being used nowadays for the production of sheets for roofing, flashing and plumbing applications in the heritage industry as well as the renovation of old churches, cathedrals, castles and state homes. Sand cast sheets have a more attractive sheen compared to the ones manufactured via rolling or continuous casting methods; the top side (exposed to the atmosphere during casting) has a shiny mottled appearance while the bottom side (surface in contact with the sand bed) retains the texture of the sand [8].

Metals 2020, 10, 252; doi:10.3390/met10020252 www.mdpi.com/journal/metals Metals 2020, 10, 252 2 of 12

One of the most common problems encountered in metal casting is the emergence of defects during filling/pouring or solidification. As reported by Campbell [9] the majority of casting defects arise due to poorly designed running and filling systems or improper selection of casting process parameters. Entrainment defects mainly arise due to the presence of turbulence during filling; oxide films submerge into the melt by folding and form oxide bifilms, which often entrap bubbles, sand particles and other inclusions. The presence of turbulence leads to a wide spectrum of casting defects in the final cast product including bubble trails, gas microporosity, hot tears and cold cracks [10]. Besides surface turbulence, there are additional factors leading to defective castings such as hydrogen gas bubbles generated upon solidification due to the lower solubility of hydrogen in the solid phase [11]. Moreover, the presence of sand-casting defects is also dependent on the moulding and sand mixture [12]. This is because green sand mixture usually consists of several components such as sand, clay, water while the proportion of the aforementioned constituents affects the bonding strength of the sand mould. Defects on the product surface may arise when one of these constituents are out of balance due to sand expansion. Although the physical phenomena leading to defects in castings have been identified as mentioned above, the elimination of defects in the final cast product remains a challenging task. This is because of the fact that there are various process parameters associated with surface turbulence or melt quality. Several approaches on defect analysis have been established in the past: historical data analysis, cause effect diagrams, if-then rules, Design of Experiments (DoE) and trial and error [13]. The DoE method is an effective tool for reducing foundry defects by optimising various process parameters and can be used to establish a relationship between various input variables (factors) and an output variable (response). Dabade et al. [14] proposed a method combining DoE and numerical simulations to obtain the optimal process parameters for a cast iron component in order to reduce defects. Guharaja et al. [15] optimised the process parameters of a green sand-casting process (green strength, mould hardness, moisture content and permeability) using the Taguchi’s optimisation method to minimise defects in spheroidal graphite cast iron rigid coupling castings. In a similar study, Kumar et al. [16] analysed different process parameters of pressure die casting of aluminium alloys to reduce defects to a minimum using Taguchi’s method. Five different parameters (solidification time, molten metal temperature, injection pressure, filling time and velocity) were selected while three different levels were chosen for each of these parameters. Experiments were subsequently conducted using different combinations of the process parameters as per the Taguchi’s orthogonal array and the parameters were optimised for minimal casting defects. Besides DoE, Artificial Neural Networks (ANNs) have also been used for detecting the root cause of defects in castings. Perzyk et al. [17] trained an ANN using the simulated annealing algorithm in order to efficiently detect the root cause of gas porosity defects in steel castings. Although previous studies have been focused on various topics related to the manufacturing [18], mechanical properties [7,19] of lead sheet as well as the numerical modelling of the corresponding sand casting process [20], there is still limited documented knowledge on the relationship between the process parameters and defect formation. This investigation is focused on a specific surface defect type occurring at the sand side of the sand cast sheet. The defect type under investigation has a groove like appearance and appears along the cast sheet (in the direction of flow of the metal) and will be addressed as ‘grooves’ throughout this manuscript. The objective of the current investigation is to identify the main process parameters affecting the formation of grooves and propose remedies leading to the reduction or elimination of the aforementioned defect type.

2. Materials and Methods The process of manufacturing sandcast lead sheet essentially involves pouring molten lead on a previously prepared sandbed and subsequently flattening the surface by smearing the flowing metal using a strickle guided by a screed rail as illustrated in Figure1. The thickness of the final cast sheet is determined by adjusting the lead pouring temperature and by using shims in between the screed rail Metals 2020, 10, 252 3 of 12

Metals 2020, 10, 252 3 of 12 and strickle. The eﬀect of various process parameters such as the pouring temperature, screed velocity and clearance between the sandbed and the screed on the lead sheet thickness has been investigated velocity and clearance between the sandbed and the screed on the lead sheet thickness has been in [20] via means of numerical simulation. investigated in [20] via means of numerical simulation.

(a) (b)

FigureFigure 1. 1.( a()a) Snapshot Snapshot and and ( b(b)) schematic schematic representation representation of of the the lead lead sheet sheet casting casting process. process.

2.1.2.1. Materials Materials ForFor the the purpose purpose of of this this experiment, experiment, silica silica sand sand of of two two di differentfferent American American Foundry Foundry Society Society (AFS) (AFS) GrainGrain Fineness Fineness Numbers Numbers (AFS (AFS 50 50 and and 70 70 sourced sourced from from Tarmac, Tarmac, Solihull, Solihull, UK) UK) and and bentonite bentonite clay clay were were used.used. The The sand sand moulds moulds were were prepared prepared by by mixing mixing silica silica sand sand (95 (95 to to 98 98 wt%) wt%) with with water water (2 (2 wt%) wt%) and and bentonitebentonite clay clay (0 (0 to to 3 3 wt%) wt%) depending depending on on the the case case under under examination. examination. Moreover, Moreover, two two types types of of melt melt werewere used: used: (a) (a) pure pure lead lead and and (b) (b) a 50:50a 50:50 mix mix of scrapof scrap with with pure pure lead. lead. Scrap Scrap lead lead primarily primarily consisted consisted of oldof roofingold roofing sheets, sheets, flashings flashings and lead and pipes. lead A SPECTROMAXxpipes. A SPECTROMAXx stationary metalstationary analyser metal (SPECTRO analyser Analytical(SPECTRO Instruments, Analytical Kleve,Instruments, Germany) Kleve, [21 ]German was usedy) to[21] determine was used the to elemental determine composition the elemental of thecomposition scrap lead of as the shown scrap in lead Table as1 .shown Pure leadin Table (99.9%) 1. Pure was lead obtained (99.9%) by was melting obtained scrap by lead melting and then scrap refininglead and it usingthen refining sodium nitrateit using and sodium caustic nitrate soda. and Lead caustic was melted soda. inLead 120-ton was steelmelted kettles in 120-ton and mixed steel inkettles a smaller and 5-tonmixed kettle in a smaller prior to 5-ton casting. kettle prior to casting.

TableTable 1. 1.Mean Mean and and standard standard deviation deviation (SD) (SD) values values of of common common impurities impurities obtained obtained from from elemental elemental analysisanalysis of of 80 80 di differentﬀerent scrap scrap melt melt batches. batches.

Elements (wt%)Elements Cu(wt%) Cu Zn Zn Sn Sn Sb Sb Bi Bi Ag Ag Mean Mean0.0283 0.0283 0.0029 0.0029 0.0860.086 0.0315 0.0315 0.0179 0.0179 0.0041 0.0041 SD SD0.0255 0.0255 0.0101 0.0101 0.0011 0.0011 0.079 0.079 0.0030 0.0030 0.0184 0.0184

2.2.2.2. Process Process TheThe process process workflow workflow is alsois also presented presented in Figurein Figure2. The 2. The lead lead sheet sheet casting casting process process starts starts with thewith preparationthe preparation of a rectangularof a rectangular sand sand bed, usuallybed, usuall 3–7y m 3–7 long m andlong 0.75–1.3and 0.75–1.3 m wide. m wide. The surfaceThe surface of the of sandthe sand bed is bed flattened is flattened using using a strickle. a strickle. Moreover, Moreover, the sand the surface sand surface is levelled is levelled and smoothened and smoothened using steelusing floaters steel tofloaters minimise to minimise any surface any undulations. surface undulations. The skill ofThe the skill operator of the is vitaloperator in these is vital operations. in these Onceoperations. the bed isOnce prepared, the bed molten is prepared, lead is poured molten at aroundlead is 345poured◦C (the at pouringaround temperature345 °C (the depends pouring ontemperature the thickness depends of the sheet on the to thickness be cast) using of the a sheet hopper to frombe cast) the using one end a hopper of the castingfrom the bench. one end Pouring of the iscasting followed bench. by smearing Pouring ofis followed the melt usingby smearing a strickle of andthe melt seconds using later a strickle the melt and solidifies. seconds later the melt solidifies. 2.3. Defect Analysis This investigation is focused on a surface defect type, hereby addressed as ‘grooves’, appearing on the sand side of sand cast lead sheets as shown in Figure3. As it can be observed, the nature of this defect type is different from surface roughness, which can be effectively controlled by AFS grain fineness number of the sand. These defects are formed along the direction of the flow and may range

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between 20 mm and 300 mm in length while their root cause has not been identiﬁed in the literature yet. Since lead sheets are used for rooﬁng applications, which involve bending and manipulation of the sheet, cracks are often initiated at these locations. Moreover, grooves give the perception of poor quality and lead to rejection by customers. Metals 2020, 10, 252 4 of 12

Figure 2. Lead sheet sand casting process workflow.

2.3. Defect Analysis This investigation is focused on a surface defect type, hereby addressed as ‘grooves’, appearing on the sand side of sand cast lead sheets as shown in Figure 3. As it can be observed, the nature of this defect type is different from surface roughness, which can be effectively controlled by AFS grain fineness number of the sand. These defects are formed along the direction of the flow and may range between 20 mm and 300 mm in length while their root cause has not been identified in the literature yet. Since lead sheets are used for roofing applications, which involve bending and manipulation of the sheet, cracks are often initiated at these locations. Moreover, grooves give the perception of poor quality and lead to rejection by customers. There are several factors which could potentially influence the quality of cast lead sheets and promote the formation of grooves. Hence, based on process observation, literature review [12,22,23] and the experience of foundry engineers, all the possible factors affecting the quality of sheets were Figure 3. SandSand side side of of cast cast sheet sheet with grooves (1000 mm × 600600 mm). mm). categorised based on a 5 M model (Man, Material (Melt), Machine (Mould),× Method, Measurement) and environmentThere are several [24] (see factors Table which 2). could potentially influence the quality of cast lead sheets and promote the formation of grooves. Hence, based on process observation, literature review [12,22,23] and the experience of foundry engineers, all the possible factors affecting the quality of sheets were categorised based on a 5 M model (Man, Material (Melt), Machine (Mould), Method, Measurement) and environment [24] (see Table2). Initially a qualitative approach (visual examination) was followed; a set of screening experiments was conducted to check the influence of different process parameters on the formation of grooves and narrow down the factors affecting defect formation. The formation of grooves was hardly affected by altering the pouring temperature of the molten metal. Increased moisture content resulted in the formation of new defects such as blow holes. To investigate the effect of the stress induced by the screed rail on the metal during the process on the formation of defects, trials were conducted by casting

Figure 3. Sand side of cast sheet with grooves (1000 mm × 600 mm).

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Table 2. Factors potentially affecting the quality of cast sheets.

No. Process Parameters Classification as per 5 M 1 Temperature of Melt Material Metals 2020, 10, 252 5 of 12 2 Melt Composition Material 3 Sand Machine sheets without the4 use of a screedWater rail. content Trials were also conductedMachine by altering the inclination of the sandbed as well. It5 was inferred Velocity that the of screed screed rail and inclination Method of sandbed did not have much of an influence on the6 formation Angle ofof groovesinclination when of sand other bed operating conditions Machine were set. Experiments were also performed using7 99.99% pure Use lead of and screed unrefined scrap. According Method to naked-eye observations the use of scrap melt led8 to additionalAmbient defects temperature due to the presence ofEnvironment impurities, as expected. With regard to the concentration9 of grooves perBed unitpreparation area, which was the observable Man in this investigation, slightly higher values were observed in the case of unrefined scrap melt. Finally, it was observed that coarser sandInitially resulted ina poorqualitative surface approach finish, as expected.(visual examination) The initial screening was followed; experiments a set performed of screening were usedexperiments for the finalwas selectionconducted of to three check factors the influence affecting theof different formation process of grooves parameters which were on the subsequently formation usedof grooves in the and DoE narrow analysis, down as it willthe factors be discussed affecting in Section defect 2.3formation.. The formation of grooves was hardly affected by altering the pouring temperature of the molten metal. Increased moisture content resulted in the formationTable of 2. newFactors defects potentially such aasffecting blow theholes. quality To ofinvestigate cast sheets. the effect of the stress induced by the screed rail on the metal during the process on the formation of defects, trials were No. Process Parameters Classification as per 5 M conducted by casting sheets without the use of a screed rail. Trials were also conducted by altering the inclination of the1 sandbed as well. Temperature It was inferred of Melt that the screed rail and inclination Material of sandbed did not 2 Melt Composition Material have much of an3 influence on the formation Sand of grooves when other operating Machine conditions were set. Experiments were4 also performed using Water 99.99% content pure lead and unrefined Machine scrap. According to naked- eye observations5 the use of scrap melt Velocity led ofto screed additional defects due to the Method presence of impurities, as expected. With regard6 to the Angle concentration of inclination of grooves of sand bed per unit area, which Machine was the observable in this investigation, slightly7 higher values were Use of observed screed in the case of unrefined Method scrap melt. Finally, it was 8 Ambient temperature Environment observed that coarser9 sand resulted Bed preparationin poor surface finish, as expected. Man The initial screening experiments performed were used for the final selection of three factors affecting the formation of grooves which were subsequently used in the DoE analysis, as it will be discussed in Section 2.3. In order to thoroughlythoroughly investigateinvestigate groove formation, microscopic analysis of the defects was carried out. Square samples (20 mm 20 mm) were cut out from the central part of defective carried out. Square samples (20 mm × 20× mm) were cut out from the central part of defective sheets. sheets.Since defects Since defectswere observed were observed to be touniformly be uniformly distributed distributed over over the thesurface surface of ofthe the sheet, sheet, the aforementioned sample sample was was considered to to be indicativeindicative of the propertiesproperties of thethe wholewhole sheet.sheet. The samples were cleaned with ethyl alcoholalcohol (ethanol) at concentrationconcentration 70% and the sand side of the samples was observed under a ScanningScanning ElectronElectron Microscope (SEM) with an ElectroElectro DispersiveDispersive Spectrometer (EDX). A Phillips XL30ESEM (Phillips, Amsterdam, The Netherlands) en environmentalvironmental scanning electron microscope was used for this purpose.purpose. The The result resultss from the EDX confirmedconfirmed the inclusion of sand particles in the vicinity of grooves as illustrated in Figures 44 and and5 .5. Based Based on on this this observation, observation, it it was was assumed that the the composition composition of of the the mould mould materi materialal may may have have an an effect effect on on defect defect formation. formation.

(a) (b)

Figure 4. SEMSEM images images of of (a ()a the) the groove groove defect defect at at350× 350 magnificationmagniﬁcation and and (b) (ab sand) a sand inclusion inclusion at 26× at × 26magnification.magniﬁcation. ×

Metals 2020, 10, 252 6 of 12 Metals 2020, 10, 252 6 of 12

(a) (b)

FigureFigure 5. 5.(a) (aSEM) SEM Image Image of ofsand sand inclusion inclusion at atmagnification magniﬁcation 150× 150 andand (b) ( bspectral) spectral analysis analysis of ofthe the × correspondingcorresponding regions regions (1–4). (1–4).

2.4.2.4. Design Design of Experiments of Experiments AnalysisAnalysis of of Variance Variance (ANOVA)(ANOVA) is is a seta ofset statistical of statistical techniques techniques used in used both trivialin both and trivial complicated and complicatedexperimental experimental designs. ANOVA designs. isANOVA often employed is often employed in order toin findorder out to howfind out the averagehow the valueaverage of a valuedependent of a dependent variable variesvariable across varies a setacross of conditions a set of conditions tested within tested the within same experiment.the same experiment. The various Theconditions various conditions being compared being arecompared called independent are called independent variables or variables factors [25 or]. factors ANOVA [25]. is veryANOVA useful is in veryDoE useful where in a DoE number where of factorsa number may of a factorsffect a dependent may affect variable, a dependent since variable, there is no since limit there to the is numberno limit of to discretethe number effects of thatdiscrete ANOVA effects can that predict. ANOVA It can can also predict. be used It can to predict also be the used optimum to predict set the of factor optimum values setto of maximise factor values or minimise to maximise the or output minimise or dependent the output variable or dependent [26]. Thevariable general [26]. layout The general of an ANOVAlayout of tablean ANOVA is presented table is in presented Table3. SS inE Tableand SS 3.Trt SSareE and the SS errorTrt are and the treatment error and sumtreatment of squares sum of respectively. squares respectively.The degrees The of freedom degrees for of the freedom aforementioned for the aforementioned sum of squares are sumg-1 andof squaresN-g, respectively, are g-1 and where N-g,g is respectively,the number where of treatments g is the number and N the of totaltreatments number and of N observations. the total number The meanof observations. sum of squares The mean (MS) is sumgiven of squares by the sum(MS) of is squaresgiven by divided the sum by of the squares degrees divided of freedom. by the degrees The F-statistic of freedom. (or F-ratio) The F-statistic is equal to (orthe F-ratio) ratio ofis equal mean to squares the ratio of theof mean treatments squares to of that the of treatments the error. to This that quantity of the error. is used This to quantity test the nullis usedhypothesis, to test the i.e., null that hypothesis, the means of i.e., all thethat treatments the means are of theall samethe treatments versus the alternativeare the same that versus some ofthe the alternativetreatment that means some di ffofer the [25 treatment]. means differ [25].

Table 3. General layout of the ANOVA table. Table 3. General layout of the ANOVA table.

SourceSource DF DF SS SSMS F MS F Treatments Treatmentsg-1 g-1 SSTrt SSTrtSSTrt/(g-1) SSTrt/(g-1) MSTrt/MSE Error N-g SSE MSTrtSS/MSE/(NE- g) Error N-g SSE SSE/(N-g)

TheThe implementation implementation of of the the DoE DoE method method consists consists of of the the following following 5 steps, namely:namely: (a) selectionselection of of factorsfactors and and corresponding corresponding levels, levels, (b) identification (b) identification of the responseof the response variable, variable, (c) selection (c) of selection experimental of experimentaldesign (d) conducting design (d) the conducting experiment the and experiment (e) analysis and of data.(e) analysis In this study,of data. the experimentalIn this study, factors the experimentalwere selected factors based were on literature selected review, based process on literature observation review, and process operator observation interviews. and As discussed operator in interviews.Section 2.2 As, according discussed to in SEM Section observations 2.2, according and screeningto SEM observations experiments, and it screening was concluded experiments, that defects it waswere concluded caused asthat a result defects of were poor mouldcaused properties.as a result Greenof poor sand mould mould properties. consists Green of sand, sand water mould and a consistsbinder of (usually sand, water clay) and to bond a binder the sand(usually particles clay) together.to bond the A goodsand particles casting is together. produced A whengood casting there is a is goodproduced balance when between there is the a componentsgood balance of between a green sandthe components mixture. Hence, of a green the eff sandect of mixture. these parameters Hence, theon effect theformation of these parameters of defects on the surfaceformation was of investigated. defects on the Moreover, surface was the investigated. use of pure lead Moreover, does not theresult use of in pure the mottled lead does appearance not result on in thethe topmottl sheeted appearance surface, which on the is one top ofsheet the mostsurface, characteristic which is one and of desiredthe most properties characteristic of cast and lead desired sheet properties [8]; thus, of most cast foundries lead sheet use [8]; a thus, mix ofmost pure foundries (refined) use and a mix scrap of lead.pure This(refined) factor and was scrap also includedlead. This in factor the study was asalso defects included could in also the bestudy caused as defects due to thecould melt also quality be causedor interaction due to the between melt quality the melt or andinteraction the mould. betw Tableeen 4the summarises melt and the the mould. experimental Table parameters4 summarises and thetheir experimental corresponding parameters levels thatand havetheir beencorresponding used in this levels study. that The have factors been usedused forin this the DoEstudy. analysis The factors used for the DoE analysis were ‘sand’ (based on their AFS fineness number) (A), ‘bentonite

Metals 2020, 10, 252 7 of 12 were ‘sand’ (based on their AFS ﬁneness number) (A), ‘bentonite percentage’ (B) and ‘type of melt’ (C). Each factor was set to two levels equivalent to the high and low values of the corresponding experimental variables.

Table 4. Factors and their levels.

Factors Type Low Level High Level A: Sand AFS (Dimensionless) Numeric 50 70 B: Clay (wt%) Numeric 0 3 C: Melt Categoric Pure 50:50 Mix of reﬁned and scrap lead

After identifying the various factors and their levels, a suitable experimental design was established. Considering the relatively small number of factors, a full factorial design was preferred. In full factorial designs, all possible combinations of factors are tested in order to identify the statistically significant ones. Full factorial designs are very effective especially when the number of factors is relatively low due to the small number of experiments needed to be conducted. The design layout was formed as per the parameters defined in Table4, while the run order was randomised in order to minimise systematic errors. All trials were replicated twice and a total of sixteen experiments was conducted. The experimental design is shown in Table5. A square sample (500 mm 500 mm) was cut out from × the middle section of the cast sheet and the total length of defects per unit area was measured using a digital caliper or a ruler depending or their length; as mentioned in the previous subsection, their length can exceed 300 mm. The total length of defects per unit area was considered as the response variable (Y) of the DoE analysis.

Table 5. Experimental Design.

Standard A: Sand AFS B: Bentonite C: Melt Response: Y Run Order Order (Dimensionless) (wt%) (Composition) (1/mm) 1 1 50 0 Pure 620 15 2 50 3 50:50 105 2 3 70 0 Pure 538 16 4 70 3 50:50 99 3 5 50 3 Pure 110 12 6 70 3 Pure 70 9 7 50 0 Pure 640 8 8 70 3 50:50 96 4 9 70 3 Pure 65 6 10 70 0 50:50 582 13 11 50 0 50:50 712 14 12 70 0 50:50 600 7 13 50 3 50:50 105 11 14 50 3 Pure 95 10 15 70 0 Pure 548 5 16 50 0 50:50 685

Minitab (version 18, Minitab Inc., State College, PA, USA) [27] was used for the analysis of the collected results. As mentioned in the Introduction section, the main objective of using a full factorial DoE is to estimate the main and interaction effects with a significant influence on the response variable. In this study a 95% confidence interval was assumed while the factor effects were considered to be statistically significant for P 0.05. ≤ 3. Results and Discussion Experiments were conducted on a 7000 mm long casting bench under identical conditions at a foundry based in Derby, UK. Each mould mixture was prepared in a rotating sand mixer while the Metals 2020, 10, 252 8 of 12 Metals 2020, 10, 252 8 of 12 meltorder was to constantly prepared inmonitor a furnace the temperature next to the casting before bed pouring at 450 at◦ C.345 The °C. meltThe pouring was then temperature transferred was to a hopperheld constant where for any all dross trials. formed As illustrated was removed. in Tabl Ae k-type6, the factors thermocouple and interactions with a temperature whose P-values range are of 200 C to 1250 C sourced from Omega Engineering Ltd (city, counrty) was dipped in the melt in −less than◦ 0.005 ◦have a significant effect on the response variable Y. Moreover, the sand-melt orderinteraction to constantly and the monitor three-way the temperatureinteraction between before pouring sand, atclay 345 and◦C. Themelt pouring were considered temperature to was be heldinsignificant constant and for allwere trials. hence As illustratedneglected insince Table their6, the P values factors andwere interactions greater than whose 0.005. P-values The R-squared are less than(R2) statistic 0.005 have is indicative a significant of etheffect variation on the response of the response variable Y.variable Moreover, due theto the sand-melt variation interaction of the input and thevariables; three-way larger interaction values of betweenR2 adjusted sand, indicate clayand models melt of were greater considered predictive to ability be insignificant [28]. Predicted and were and 2 henceadjusted neglected R2 values since were their found P values to be wereequal greater to 99.92% than an 0.005.d 99.85%, The respectively, R-squared (R and) statistic in agreement is indicative with ofeach the other. variation Thus, of the the values response obtained variable in the due experi to thements variation are considered of the input satisfactory variables; for larger the validation values of 2 2 Rof ANOVA.adjusted indicate models of greater predictive ability [28]. Predicted and adjusted R values were found to be equal to 99.92% and 99.85%, respectively, and in agreement with each other. Thus, the values obtained in the experiments are consideredTable 6. ANOVA satisfactory table. for the validation of ANOVA.

Source DFTable Adj 6. ANOVA SS Adj table. MS F-Value P-Value SourceModel DF7 1,119,746 Adj SS 159,964 Adj MS 1412.48 F-Value 0.000 P-Value Linear 3 1,111,617 370,539 3271.87 0.000 Model 7 1,119,746 159,964 1412.48 0.000 LinearSand 31 1,111,61714,042 14 370,539,042 123.99 3271.87 0.000 0.000 SandClay 11 1,092,025 14,042 1,092,025 14,042 9642.60 123.99 0.000 0.000 ClayMelt 11 1,092,0255550 1,092,0255550 49.01 9642.60 0.000 0.000 2-WayMelt Interactions 1 3 5550 7552 2517 5550 22.23 49.01 0.000 0.000 2-Way InteractionsSand-Clay 31 75525776 5776 2517 51.00 22.23 0.000 0.000 Sand-Clay 1 5776 5776 51.00 0.000 Sand-MeltSand-Melt 11 1212 12 12 0.11 0.110.751 0.751 Clay-MeltClay-Melt 11 17641764 1764 1764 15.58 15.58 0.004 0.004 3-Way3-Way Interactions Interactions 1 1 576 576 576 576 5.09 5.09 0.054 0.054 Sand-Clay-MeltSand-Clay-Melt 11 576 576 576 576 5.09 5.09 0.054 0.054 Error 8 906 113 - - Error 8 906 113 - - Total 15 1,120,652 - - - Total 15 1,120,652 - - -

ANOVA cancan alsoalso bebe validatedvalidated through through the the normal normal probability probability plot plot of of residuals residuals (or (or error), error), which which is ais graphicala graphical technique technique for for assessing assessing whether whether a a data data set set is is approximately approximately normally normallydistributed. distributed. IfIf the data points of a normal probability plot lie on the vicinity of a straight line, it can be assumed that the residuals areare normallynormally distributed.distributed. In Figure6 6 the the plottedplotted datadata pointspoints fallfall onon aa straight-linestraight-line indicatingindicating compliance to the normality assumption.assumption.

70 60 50 40 30 Probability (%) Probability 20

1 -20 -1 0 0 10 20 Residual

Figure 6. Normal probability plot of residuals.

SignificantSignificant eeffectsffects can be identifiedidentified from thethe normalnormal plotplot ofof thethe eeffectsffects asas shownshown inin FigureFigure7 7.. AllAll the eeffectsffects lyinglying in thethe proximityproximity of the straight line are insignificantinsignificant while the ones lying far from it are statistically significant.significant. The response variable is increased when the positive effects effects change from low level to high level while negative effects have the inverse effect on the response variable. Those effects further from x = 0 are more significant [29]. A Pareto plot was also generated to evaluate the

Metals 2020, 10, 252 9 of 12

Metals 2020, 10, 252 9 of 12 lowMetals level 2020, to10, high252 level while negative effects have the inverse effect on the response variable. Those9 of 12 significanceeffects further of fromthe main x = 0 and are moreinteraction significant effects [29 as]. shown A Pareto in plotFigure was 8. also The generated vertical red to evaluateline at 2.3 the is significancesignificance of the main and interaction eeffectsffects as shown in Figure8 8.. TheThe verticalvertical redred lineline atat 2.32.3 isis called reference line; any effects with bars reaching beyond this value are considered significant. Fromcalled the reference Pareto line; and any normal effects probability with bars reachingplots of beyondthe effects, this it value is evident are considered that the significant.most significant From theFrom Pareto the Pareto and normal and normal probability probability plots of plots the e ffofects, the iteffects, is evident it is thatevident the most that significantthe most significant effect is B effect is B (clay content in the moulding mixture). Other significant effects influencing defect (clayformation content are in A the (sand moulding type), mixture).C (melt composition), Other significant AB (sand-clay effects influencing interaction) defect and formation BC (clay-melt are A (sandformation type), are C (meltA (sand composition), type), C (melt AB (sand-claycomposition), interaction) AB (sand-clay and BC interaction) (clay-melt interaction).and BC (clay-melt It can interaction). It can also be observed that AC (sand-melt interaction) and ABC (three-way interaction betweenalso be observed sand, clay that and AC melt) (sand-melt are insignificant interaction) and and consequently ABC (three-way have interaction negligible between influence sand, on claythe andbetween melt) sand, are insignificant clay and melt) and consequentlyare insignificant have and negligible consequently influence have on negligible the formation influence of defects. on the formation of defects.

(response is Y, α = 0.05) (response is Y, α = 0.05) 99 99 Effect T y p e NotEffect Significant T y p e Not Significant 95 Significant 95 Significant 90 AB Factor Name AB 90 ASandFactor Name ASand 80 BClay C BClay 80 C CMelt 70 CMelt 70 60 60 50 50 40 BC 40 30 BC 30 Probability (%) Probability A 20 Probability (%) Probability A 20 10 B 10 B 5 5

1 1 -1 00 -80 -60 -40 -20 0 -1 00 -80 -60 -40 -20 0 Standardized Effect Standardized Effect

Figure 7. Normal probability plot of the effects. Figure 7. Normal probability plot of the effects. eﬀects.

Pareto Chart of the Standardized Effects Pareto Chart of the Standardized Effects (response is Y, α = 0.05) (response is Y, α = 0.05) Term 2.3 Term 2.3 Factor Name B ASandFactor Name B BClayASand CMeltBClay A CMelt A

AB AB

C C

BC BC

ABC ABC

AC AC

0 20 40 60 80 100 0 20 40 60 80 100 Standardized Effect Standardized Effect

Figure 8. Pareto chart of Standardised effects. Figure 8. Pareto chart of Standardised eeffects.ﬀects.

The main effects effects of the three factors considered on the formation of defects on the cast sheet have been plotted in Figure 9 9.. AsAs itit cancan bebe observed,observed, thethe clayclay contentcontent ofof thethe mouldingmoulding mixturemixture isis thethe mostmost significantsignificant amongamong thethe 33 factors factors examined examined while while defects defect haves have been been found found to to reduce reduce significantly significantly with with an anincrease increase in bentonite in bentonite content. content. This This is because is because a good a good casting casting is produced is produced when therewhen is there a good is balancea good balancebetween between the components the components of the green of the sand green mixture. sand Leadmixture. is a metalLead is with a metal a high with density a high and density has a highand valuehas a high of 3.22% value for of contraction 3.22% for cont on solidificationraction on solidification [9]. Expansion [9]. defects Expansion are very defects common are very in castings common and in castings and they originate in part due to expansion of the sand grains which get heated by the molten metal. Silica sand expands to a greater extent when compared with other sand like zircon, olivine and chromite [30]. Apart from expansion, these defects are also reliant on moisture. When the molten metal flows over the sand bed, the moisture in the mould is converted to steam and this steam

Metals 2020, 10, 252 10 of 12 they originate in part due to expansion of the sand grains which get heated by the molten metal. Silica sand expands to a greater extent when compared with other sand like zircon, olivine and chromite [30]. Apart from expansion, these defects are also reliant on moisture. When the molten metal flows over Metals 2020, 10, 252 10 of 12 the sand bed, the moisture in the mould is converted to steam and this steam permeates through the sandpermeates grains through in the mould. the sand At grains the point in the when mould. the At steam the point reaches when a temperaturethe steam reaches lower a thantemperature 100 ◦C, itlower condenses, than 100 and °C, a wetit condenses, layer is formed. and a wet This layer wet is layer formed. is weaker This wet than layer the green is weaker sand than or the the hot green dry sand.sand or However, the hot whendry sand. the hotHowever, sand expands when the due hot to thesand heat expands absorbed due from to the the heat molten absorbed metal, from this wet the layermolten shears metal, to allowthis wet expansion, layer shears and ridgesto allow of sandexpansion, spread and into theridges mould of sand cavity, spread and thereby, into the grooves mould arecavity, formed and onthereby, the cast grooves sheet. Thisare formed phenomenon on the iscast intensified sheet. This due phenomenon to the contraction is intensified of the solidifying due to the metalcontraction and erosion of the ofsolidifying sand. Addition metal and of a bindererosion holds of sand. the Addition sand particles of a binder together holds thereby the sand reducing particles this etogetherffect and thereby explains reducing the reduction this effect in formation and explai ofns these the defects.reduction in formation of these defects.

Main Effects Plot for Y Fitted Means Sand Clay Melt

600

500

400

Mean of Y of Mean 300

200

100 50 70 0 3 Pure 50:50 FigureFigure 9.9. EEffectﬀect ofof processprocess parametersparameters onon defectdefect formation.formation.

GroovesGrooves werewere alsoalso foundfound toto reducereduce withwith anan increaseincrease inin thethe AFSAFS graingrain finenessfineness number.number. FineFine grainedgrained sandsand helpshelps toto attainattain aa betterbetter surfacesurface finishfinish owingowing toto lowerlower metalmetal penetrationpenetration intointo thethe mouldmould butbut needsneeds moremore binderbinder content.content. However, aa lowlow permeabilitypermeability mightmight result in formation of gas defects. CoarseCoarse grainsgrains provideprovide higherhigher permeabilitypermeability butbut maymay allowallow metalmetal penetration.penetration. AsAs aa result,result, therethere shouldshould bebe aa balancebalance between these parameters to to attain attain the the best best results results [31]. [31]. Finally, Finally, the the use use of of50:50 50:50 mix mix of ofrefined refined to unrefined to unrefined lead lead increases increases the formation the formation of defects of defects as expected. as expected. This is Thisbecause is because of the already of the alreadyexisting existing inclusions inclusions in the in50:50 the 50:50mix mixwhich which contribu contributete to tothe the formation formation of of additional additional defects. TheThe optimumoptimum settingssettings forfor minimalminimal defectsdefects areare obtainedobtained forfor aa combinationcombination ofof 7070 AFMAFM sand,sand, 3%3% BentoniteBentonite andand purepure melt.melt.

4.4. ConclusionsConclusions SandSand castingcasting ofof leadlead sheetsheet isis aa traditionaltraditional methodmethod forfor manufacturingmanufacturing premiumpremium leadlead sheetsheet forfor roofingroofing applicationsapplications inin heritageheritage buildings.buildings. SimilarlySimilarly toto allall castingcasting processes,processes, leadlead sheetsheet castingcasting susuffersffers fromfrom the the presence presence of of defects. defects. In thisIn this investigation investigation the focusthe focus has been has laidbeen on laid a surface on a surface defect type, defect hereby type, addressedhereby addressed as ‘grooves’, as ‘grooves’, appearing appearing on the on sand the side sand of side cast of lead cast sheets. lead sheets. The presence The presence of groove-type of groove- defectstype defects in the in final the cast final sheet cast wassheet quantified was quantified by estimating by estimating the ratio the of ratio their of total their length total to length the sample to the unitsample area. unit There area. are There several are factors several potentially factors potentially affecting theaffecting formation the formation of this type of ofthis defects. type of Based defects. on processBased on observation, process observation, literature reviewliterature and review foundry and experience, foundry experience, all possible all factors possible affecting factors quality affecting of sheetsquality were of sheets listed. were The listed. initial The set ofinitial factors set selectedof factors was selected narrowed was downnarrowed based down on a based set of on screening a set of experiments,screening experiments, literature review literature and the review experience and ofthe foundry experience engineers. of foundry Analysis engineers. of Variance Analysis (ANOVA) of wasVariance implemented (ANOVA) in orderwas implemented to identify the in process order parametersto identify the or factors process which parameters had the or most factors significant which contributionhad the most to significant the formation contribution of grooves to while the formation an optimal of set grooves of process while parameters an optimal was set proposed of process in parameters was proposed in order to minimise defects in the final product. The main conclusions drawn from this investigation are summarised below: (1) The majority of the defects produced in lead sheet casting industry can be attributed to mould properties and moulding materials. This paper substantiates the fact that green sand casting provides better surface quality and finish as compared to traditional sand mixtures used in sand casting of lead sheet.

Metals 2020, 10, 252 11 of 12 order to minimise defects in the ﬁnal product. The main conclusions drawn from this investigation are summarised below:

(1) The majority of the defects produced in lead sheet casting industry can be attributed to mould properties and moulding materials. This paper substantiates the fact that green sand casting provides better surface quality and finish as compared to traditional sand mixtures used in sand casting of lead sheet. (2) The trials conducted with the green sand mixture showed an improved surface finish and substantial reduction in the grooves produced on the sand side. Addition of bentonite in the sand mixture results in a substantial reduction of groove defects in sand cast lead sheet. (3) Grooves are reduced for higher AFS grain fineness numbers. Fine grained sand contributes to superior surface finish due to the decreased lead penetration into the sand. (4) The quality of the melt also affects the formation of the grooves. Pure lead should be favoured over a mix of refined and scrap lead. (5) Groove defects in sand cast lead sheet can be minimised by 90% for a combination of 70 AFS sand, 3% bentonite and pure melt.

Author Contributions: Conceptualization, A.P.; methodology, A.P.; software, A.P.; validation, A.P.; formal analysis, A.P.; investigation, A.P.; resources, K.S. and M.J.; data curation, A.P.; writing—original draft preparation, A.P., M.P.; writing—review and editing, M.P. and K.S.; visualization, A.P.; supervision, K.S. and M.J.; project administration, K.S.; funding acquisition, K.S. and M.J. All authors have read and agreed to the published version of the manuscript. Funding: The authors would like to acknowledge ML Operations, Derby, UK for their support in conducting this research and Innovate UK for providing funding through the Knowledge Transfer Partnership (Project Number 9855). Conﬂicts of Interest: The authors declare no conﬂict of interest.

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