Towards high performance living manufacturing systems ‒ A new convergence between biology and engineering

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Towards high performance living manufacturing systems - A new convergence between biology and engineering

Byrne G.

^a,

*, Damm O.

^b

, Monostori L.

^c

, Teti R.

^d

, van Houten F.

^e,^†

, Wegener K.

^f

, Wertheim R.

^g

, Sammler F.

^h

aSFII-FORMCentreforAdvancedManufacturing,SchoolofMechanicalandMaterialsEngineering,UniversityCollegeDublin,Belﬁeld,Dublin4,Ireland

bDepartmentofIndustrialEngineering,StellenboschUniversity,BanghoekRoad,Stellenbosch7600,SouthAfrica

cCentreofExcellenceinProductionInformaticsandControl,InstituteforComputerScienceandControl,Kendeu.13-17,Budapest1111,Hungary

dDepartmentofChemical,MaterialsandIndustrialProductionEngineering,UniversityofNaplesFedericoII,PiazzaleTecchio80,Naples80125,Italy

eDepartmentofDesign,ProductionandManagement,UniversityofTwente,Drienerlolaan5,7522NBEnschede,TheNetherlands

fDepartmentofMechanicalandProcessEngineering,InstituteofMachineToolsandManufacturing,ETHZürich,Switzerland

gFraunhoferInstituteforMachineToolsandFormingTechnologiesIWU,09126Chemnitz,GermanyandOrtBraudeCollege,Karmiel,Israel

hSchoolofEngineering,HochschulefürTechnikundWirtschaftBerlin,12059Berlin,Germany

ARTICLE INFO Articlehistory:

Availableonline5January2021

Keywords:

Biologicalisation Manufacturingsystems Highperformance Convergence Living Biology Engineering Sustainability Bio-intelligence Bio-integration Bio-inspiration

ABSTRACT

Thispaperreportsonahighlyambitiousinternationalstudyundertakenintheperiod2018–2020onthe topicofconvergencebetweenbiologyandadvancedmanufacturingsystems.Theinternationalteam (authorsofthispaper)workedtogethertoanalysethestatusofthisconvergencethroughtheassessment ofconcreteexamples,referredtohereasdemonstrators,withinadvancedmanufacturingsystems.Four independentdemonstratorsfromdifferentsectionsofthemanufacturingvaluechainandinvolvingbio- inspiration,bio-integrationand/orbio-intelligencewereselectedtotestthefollowinghypothesis:“That FutureManufacturingSystemswillincorporateComponents,Features,CharacteristicsandCapabilitiesthat enabletheconvergencetowardsLivingSystems”.Eachofthesefourdemonstrators havesucceededin supportingthishypothesisandinprovidingclearevidencetoconfirmthatsignificantperformancebenefits maybederivedthroughthe“biologicalisation”ofadvancedmanufacturingsystems.Thisconclusionisof greatsignificanceforthenextphasesofdevelopmentofmanufacturingscienceandengineeringglobally.

Theevidencereportedinthispaperprovidesarobustbasisforrecommendingthatadeeperanalysisof theimplicationsofbiologicalisedmanufacturingsystemsbeundertaken.Asaresultofthisearlystage work,itisconcludedthatthereisahighlikelihoodthatthisnewconvergencewillleadtoamajor paradigmshiftinadvancedmanufacturing.Outstandingopportunitiesexistforhighlevelsofinnovationin thenextstagesofdevelopmentofadvancedmanufacturingprocessesandsystemsfromthebiological perspective.Therelationshipbetweenthehuman andthephysicalmanufacturingsystemwillalso changeand theworld of advanced manufacturing will be confronted with many newchallenges includingimportantethicalquestions.

creativecommons.org/licenses/by-nc-nd/4.0/).

Introduction

Theoverallunderlyinghypothesisfortheworkdescribedinthis paper is as follows: “That Future Manufacturing Systems will incorporate Components, Features, Characteristics andCapabilities thatenabletheconvergencetowardsLivingSystems”.Thehypothesis testhasshownthatinterestandevidenceisgrowingamongstthe international community on the topic of Biologicalisation in

Manufacturing(BiM).Basedonthemostrecentdevelopmentsin biology coupled with those in advanced manufacturing it is evident that, following quickly on the heels of digitalisation, convergence between biology and engineering is accelerating.

There are very signiﬁcant implications of this convergence for futureengineeringsystems,withmanufacturingsystemsprovid- ingaverygoodandrepresentativeexampleforthis.

Thisnewemerging frontierinthenextphaseofevolutionof manufacturing digitalisation and the 4th industrial revolution (Industry4.0)hasbeentermed“BiologicalisationinManufactur- ing”. This has been deﬁned previously to be: “The use and integration of biological and bio-inspired principles, materials, functions, structuresand resources for intelligent and sustainable

*Correspondingauthor.

E-mailaddress:gerald.byrne@ucd.ie(G. Byrne).

1Deceased.

https://doi.org/10.1016/j.cirpj.2020.10.009

ContentslistsavailableatScienceDirect

CIRP Journal of Manufacturing Science and Technology

j o u r n a l h o m e p ag e : w w w . e l s e vi e r . c o m / l o c a t e / c i r p j

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manufacturingtechnologiesandsystemswiththeaimofachieving theirfullpotential”[1].Thereisaclearneedtoillustrateandassess theimplicationsoftheBiologicalTransformationinManufacturing moreclearlyinthecontextofthisnewbreakingfrontierofIndustry 4.0.

Manufacturing processes as deﬁned in DIN 8580 typically incorporateawidenumberofvariableswhichareoftenchalleng- ing to precisely control even with advanced process control capabilities. A real time response to changing manufacturing conditionsisoftenhighlychallenging,particularlyastheprocesses are driven towards theachievement of veryhigh toultra-high performance levels.Higherlevel biological systems are charac- terisedbya)theavailabilityofavarietyofsensordatacomingfrom partly redundant and failure tolerant sensor systems and b) intelligence that allows for continuous adaption and learning.

Furthermore,biologicalsystemscanself-maintaintheirhealthand canalsoexchangeinformationwithothersystemstoimprovetheir effectiveness.Hence,theadaptionofbasicconceptsofbiological systems totechnicalsystems is ofhigh importancein orderto overcome current shortcomings in modern manufacturingprocesses[2].

Theimplicationsfortheentiremanufacturinghierarchyfrom the discrete component level tothe integrated system, supply chain, and organisational level need to be given detailed consideration.

Historicallyconvergencehasbeenseentobeanintegralpartof manufacturing developmentsince the1st industrial revolution.

The mostrecentconvergencehasbeentheintegration ofcyber physicalsystemsandconnectivityintothemanufacturingsystems inthecontextofIndustry4.0.

The factors and driversinﬂuencing thekey elementsof the manufacturing value chain from an overall manufacturing technologyandsystemsperspectivearesummarisedinFig.1.

These includetheresources,thehardware,thesoftware,the humanandthemanufacturingprocessesasillustratedontheright handsideoftheﬁgure.

Thebiologicaltransformationhaswiderangingimplicationsfor keyareasinsocietysuchassustainabilityandmanufacturing.One sub-classiﬁcation of Biologicalisation in Manufacturing (BiM) is illustrated(Fig.2)asbeing: Bio-Inspiration,Bio-Integration and Bio-Intelligence.Itisessentialthatthesetermsbeclearlydeﬁned.

ProposeddeﬁnitionsareprovidedinSection4ofthispaper.

Themainpurposeoftheworkdescribedherewastoprovide practical demonstrators for the emerging paradigm shift of biologicalisation in discrete,advanced manufacturing.Thisnew paradigmshiftisanalysedinthecontextoftheimpactintermsof achievinghigherperformanceofoverallmanufacturingsystems.

Ofcourseitisessentialtoconsiderthemeaningofperformance keepinginmindthecurrentsituationinindustrialmanufacturing.

Clearlyperformance istobeinterpretedinrelationtotechnical efﬁciencies and capabilities in the context of sustainable manufacturing, whereby sustainability takes on a much more signiﬁcantroleinmoderndaymanufacturingthanithasdonein thepast. Theeconomic performance of manufacturing systems clearlyremainsfundamentallyimportant.

However,asthispaperwasinpreparation,anewperspectiveon theinterpretationofhighperformancestartedtoemerge.Theﬁrst andsecondstagesoftheCOVID19pandemicweresweepingthe worldandtheauthorswereworkingfromhomeinisolationand livingunderstrictlockdownconditions.Industrialmanufacturing suddenlycameundersevereandextremepressureandasaresult, new and unanticipated demands are now being placed on manufacturingsystems.Insomecases,companieshaveswitched frommanufacturingtheircoreproductstoproducingartefactsof value toﬁghting the virussuch as respirators and masks. The agility, resilience and robustness of manufacturing systems in crisissituationsthusalsorepresentakeyindicatorinrelationto performance.Insummary,itisnotadequatetoonlyconsiderthe technical/engineeringperformanceofmanufacturingsystems.Itis essential that perspectivesof sustainability,economics, society andlastbutbynomeansleastethicsbeincorporatedintothekey performanceindicatorsforassessingperformanceimprovements.

Earlystageevidenceofthebiologicaltransformationtowards the“LivingManufacturingSystem”wastobedemonstratedwithin theworkdescribedherethroughdetailedconsideration,evalua- tionand demonstrationof a) bio-inspiration, b)bio-integration and, perhaps most signiﬁcantly of all, c) bio-intelligence. The selectedrepresentativeareasofproductionengineeringindiscrete manufacturingare:

Designmethodologies(Demonstrator1),

Materials,equipment,sensorsandprocesses(Demonstrators2 and3)and

Manufacturingsystems(Demonstrator4).

ThisspecialeditionoftheCIRPJournalofManufacturingand Technology on Biologicalisation in Manufacturing contains ﬁve other directly related and strongly referenced papers (cited in

Fig.1.BiologicalTransformationandManufacturing(Source:R.Neugebauer,FraunhoferGesellschaft,2019).

Fig. 2.Classiﬁcation of BiM under Bio-Inspiration, Bio-Integration and Bio- Intelligence[1].

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Section5below)whichfocusonthespeciﬁcdetailsofthework undertakeninthesedemonstrators.Thesepapersshouldbereadin conjunctionwiththisoverarchingpaper.

Anhistoricalperspectiveonconvergencesinmanufacturing/

industrialengineering

ItisinterestingtoconsiderFig.3inthecontextofconvergence.

Thethreeearlierindustrialrevolutionsarepresentedinthefigure and include a brief commentary on some of the key aspects associated with each revolution [3]. In the first industrial revolution, mechanical production equipment was powered by steam and water. This may be interpreted as the first major convergence.Basedonmassproductionachievedbythedivisionof labour concept and the use of electrical energy, the second industrialrevolutionemergedinthemidtolate1800’s–asecond majorconvergence.

Theuseofelectronicsand ITtofurtherautomateproduction withnumericalcontrol(NC)andlatercomputernumericalcontrol (CNC) foundwidespread application. This moved tothe era of computerintegratedmanufacturing(CIM)andrepresentedathird majorconvergence.Finally,inrecenttimes,thedevelopmentsin computing,digitalisationandtelecommunicationssawtheintro- ductionofcyberphysicalsystemsintomanufacturingengineering [4].Thiscanbeseentobeafourthmajorconvergenceinindustrial andmanufacturingengineering.

During the courseof industrialdevelopment sincethe early days,materialsscienceandengineeringhaveplayedacentralrole inthedevelopmentofmanufacturingsystemsandprocesses.For example, thedevelopmentsin silicon hasdriven thefunctional capabilitiesofchipsfortheITsector.Areasanddisciplinesoutside of the physical sciences and engineering have also been fundamental toindustrialdevelopment,suchasthehumanities andthesocialsciences.

WithintheparadigmofIndustry4.0,ithasbecomepossibleto connectmanufacturingsystemsdirectlytotheInternet.Machine tomachinecommunicationandartificialintelligenceallowhigher levelsofperformanceandflexibility.Whereeconomyofscalehas been the paradigm of the past, flexibility, customisation and sustainabilityofindustriallyproducedgoodsisnowbecomingthe standard.Localmanufacturingondemandisbecomingtechnically andeconomicallyfeasible.Thishowever,requiresadifferentview onbusinessmodels,logisticsandimpactonthelabourmarket,on theeducationalsystemandalsoonpolitics[1].

There is still considerable ongoing discussion about the revolutionary or evolutionary character of Industry 4.0. The

proliferationofICTisclearlyanevolutionarydevelopment.Sensors andactuators,incombinationwith(big)dataanalytics,artiﬁcial intelligence,digitaltwins,largescale(realtime)simulation,data visualisation byvirtual and augmentedreality areall elements converging inan integratedwayofinstructing,monitoringand controllingourmanufacturingsystems[5,6].

Therealadditionalvalue-addinmovingfromIndustry3.0–4.0 has been primarily through the cyber physical systems (CPS) developments.Cyberphysicalsystemsaresystemsofcollaborating computationalentities,whichareconnectedwiththesurrounding physicalworldanditson-goingprocesses,providingandusing,at the same time, data-accessing and data-processing services available onthe internet[4].Inotherwords, CPS cangenerally be characterised as “physical and engineered systems whose operationsaremonitored,controlled,coordinated,andintegrated byacomputingandcommunicatingcore.Theinteractionbetween thephysicalandthecyberelementsisofkeyimportance.”[4].

Fig. 3 also illustrates the fact that throughout the various industrialrevolutions,thecomplexityofmanufacturingsystems has been increasing in a non-linear manner. The question of complexity in manufacturing systems is well addressed in the literature. The introduction of the human interface into manufacturingsystemsbringswithitanotherlevelofcomplexity whichisgivenconsiderationinaCIRPKeynotePaperonmodelling of manufacturing complexity. A methodology to systematically determine the product and process complexity for any manufacturingenvironmentwasintroduced[7,8].

Some of the current and emerging scenarios in relation to digitalisationandIndustry4.0are:

Theincreasingextentofdecentralisationofmanufacturing, Fundamentally new design paradigms e.g. through the rapid

developmentsinadditivemanufacturingofpolymeric,metallic andincreasinglybio-materials,

Emergenceofnewsupplychainarchitecturesandnetworksand newmethodsofsupplychain/networkintegration,

Cyber-securityasrelatedtomanufacturing, Diminishinglatency,

Muchhigherlevelsofautomationandself-optimisation[9,10], Newunprecedentedlevelsofconnectivity,

Increasinglevelsofartiﬁcialintelligence(AI)applicationsand associatedmachineintelligence,

More signiﬁcant focus on the sustainability of machining processesandequipment[11],

Crisisresponsivenessandnewlevelsofrobustnessand Bio-inspiration,bio-integrationandbio-intelligence.

Fig.3.BiologicalisationinManufacturingintheContextofIndustry4.0andConvergencesofthepast,(originalsource:Ref.[3]).

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Thenewlyemergingconvergenceofbiologyandmanufactur- ing engineering exhibits distinctly different characteristics to thoseofearlierconvergencesinmanufacturingengineering.The earlier convergences e.g. in Industry 1.0–4.0 mainly involved differentdisciplinesofengineering-mechanicaltoelectricalto controltoelectronicandthentosoftwareandtotelecommuni- cations. This new convergence is between engineering and sciencei.e.biologyandisthereforeconsiderablymorechalleng- ing forengineers,asthelanguage,thecultureandthesystems associated with biology are signiﬁcantly different to those of engineering.Considerableeffortisthusrequiredtoovercomethe naturalbarrierswhicharise.

Manufacturingengineeringandbiologyconverging- biologicalisationinmanufacturing

Inrecentyearsithasbecomeincreasinglyevidentthatanew convergencebetweenbiology,thephysicalsciencesandengineer- ingisdeveloping.Thebookpublishedin2019entitled“TheAgeof Living Machines” Hockfield [12] highlights the point that the overarchingtopicofbiologywillbeadriverforthenexttechnology revolution. Shereferstogrowingupin themidstof two major biologicalrevolutions,thefirstbeing“MolecularBiology”andthe secondbeing“Genomics”.ShepresentsfiveDemonstratorsinthe book,andalthoughtheyarenotspecifictomanufacturingscience and engineering, they do provide interesting and important insights. The Case Studies include: “Biology and Batteries”,

“BiologyandWater”,“BiologyandCancerﬁghtingNano-Particles”,

“Brain”and“Food”.

Some of the high-level aspects of the new Biologicalisation Frontierseentobeopeningupasanextphaseofthedigitalisation andIndustry4.0developmentsinclude:

Newdevelopmentsinchemistryandnewmaterials, Newproductsusingnewbio-materials[13],

A higher level of understanding of biological materials and processesthroughtheuseofmodellingandsimulationtools, Classicalindustrialprocessesbeinginﬂuenced,withthepoten-

tialforentirelynewbio-inspiredindustrialprocessestodevelop, Potential for new bio-inspired manufacturing processes and equipment, including robotics, machine tools and measuring equipment[14,15]and

New bio-inspired models for the production organisation includingmanufacturingsystemsandsupplychains.

Someofthesetrendswereevidentatthe2017Conferenceofthe International Academy for Production Engineering (CIRP) in Chicago whereMirkin[16] spoke of completelynewdirections

inchemistryandtheadventofnewmaterialswithcapabilitiesway beyondthepresentlimitations.

Theworkreportedinthispaperisaimedattheambitioustarget of exploring the effects of Biologicalisation on the entire manufacturinghierarchy:fromthediscretecomponentlevel,all thewaytotheintegratedsystem,supplychain,andorganisational levels.Indoingso,comparisonsaredrawnbetweenthehierarchies existing in manufacturing systems and those existing within biologicalsystems–wherethecellularlevelisequatedwiththe discretecomponents,organswithmachines,entireorganismsas equivalenttofactories,andecologiesorsocietiesasanalogousto completeorganisationsandsupplychains–seeFig.4.

Each demonstrator explored a different stratum of the manufacturing hierarchy, also illustrated in Fig. 4. However, it wasdeemedtobeveryimportantthatthedemonstratorsdonot exist solely in their own strata but that signiﬁcant cross- collaborationbetween each of them beexplored. The speciﬁed intentwas todeliver a comprehensive, overall and early stage representationofelementsoftheBiologicalisationTransformation inManufacturing.

Aswellascoveringthebroadspectrumofthemanufacturing hierarchy, thedemonstratorshad theobjectiveofexploring the transformationfromtheold,lifeless,non-connectedmanufactur- ingsystemsto“TheLivingManufacturingSystem”.Itishypothesised thatfuturemanufacturingsystemswilldevelopinthisdirectionin the contexts of a) bio-inspiration, b) bio-integration and c) bio- intelligence.

Fig.5presentsageneralblockdiagramshowingasystematicbi- directionalapproach–“Manufacturing-Driven”(blue)and“Biolo- gy-Driven” (green), to analyse and identify the potential and impact of biologicalisation in manufacturing. These two main directions are presented in 5 stages, either starting from the industrial/technical/manufacturingstageor from thebiological/

nature/scientiﬁc point of view, both aiming to improve manufacturingprocessesandproductquality.

Whenstartingfromthemanufacturingside,thefirststageisto define the problem, including setting targets and goals for material,surfaces,design,processes,control,system,organisation, functions, etc. In Stage 2, checking biological options requires searchingforand identification ofanalogicalsolutions, relevant options,and similarconceptsor tolookforavailable solutions.

Stage3startswiththeselectionofarelevantsolutionoraconcept, includinganalysisandabstractioninordertoexplainandmakeit understandablefortheengineeringcommunity.Theever-growing database,includingthat forbiology,is anexcellentbackground facilityfromwhichtosearchforrelevantalgorithmsandtoretrieve updatedinformation.InStage4,paralleltoaniterationprocess,the potential beneﬁt toand impacton manufacturing is evaluated

Fig.4.Therelationshipbetweenmanufacturingandbiologicalhierarchies,andtheindividualdemonstratorstrata(Source:earlyversionfrominspireAG).

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before transferring the “biological solution” to the relevant manufacturingtopic(s).

The block diagram also includes the second approach, the biology-driven or “Bottom Up” frombiology tomanufacturing.

Startingfromabiologicalsystemwithuniquecharacteristicsthen followed by identiﬁcation of these features, phenomena and functionsin Stage1 canbeidentiﬁed andin thenext stageby evaluation of possible implementation capability. The next 3 stages,startingfromselection,abstractionandanalysing,iteration and evaluation, followed by transferring the selected one to manufacturingarethenidenticaltothetopdownapproach.

To examine the potential applicationsof biologicalisation in manufacturing (BiM) systems, work was divided into four demonstrators.Thesewerechosentodemonstratetheprinciples

of bio-inspiration, bio-integration and bio-intelligence when appliedtokeyelementsofmanufacturing.Thesedemonstrators representan initialattempttogiveearly stageconfirmationof progress towards Biologicalisation in Manufacturing as well as early stage insight into the outstanding challenges and future direction of the field. The specific details of the chosen demonstratorsareincludedinSection4below.

Performanceimprovementinbiologicalisedadvanced manufacturingsystems

Themainobjectiveofthestudyreportedherewastoillustrate theroleof theBiologicalTransformationin Manufacturingasa new frontier of Digitalisation and Industry 4.0, leading to a Fig.5. Thebi-directionalsystematicapproachtoidentifypotentialandimpactofbiologicalisationinmanufacturing[1].

Fig.6.Relationshipbetweenbio-inspiration,bio-integrationandbio-intelligence.

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paradigmshiftinadvancedmanufacturing.Afurtherobjectivewas toevaluateandassessapotentiallynewemergingconceptofthe

“LivingManufacturingSystem”.Forthatpurpose,fourdemonstra- tors,eachrepresentingsigniﬁcantareasofdiscretemanufacturing, were selected. These demonstrators form the basis for which variousscenariosandtechnicalaspectsofIndustry4.0fromthis newBiologicalisationinManufacturingperspectivewereanalysed andtested.

Buildingonthepreviouswork oftheauthors[1] andonthe workofotherresearcherse.g.[17–20],itwasassumedthatfuture biologicalised manufacturing systems will continue to develop alongthethreedirectionsof:a)bio-inspiration,b)bio-integration andc)bio-intelligence.

Theworkundertakeninvolvedassessmentofthechosenareas fromtheperspective ofperformance enhancementthroughthe incorporationofeachofthethreeelements.Fig.6illustratesthe relationship between bio-inspiration, bio-integration and bio- intelligence.Theﬁgurereportsonaschemewhichindicatesthat both bio-inspiration and bio-integration solutions can develop towardsbio-intelligentsolutionswiththemerging/convergenceof ICT enabled intelligent paradigms. The six aspects: learning, decision making, reasoning, symbiosis, co-existence and co- evolutionallrelatetotheattainmentofbio-intelligence.

ProposeddeﬁnitionofBio-InspiredManufacturing:

Bio-inspired manufacturing isrealised by transferring concepts concerningprinciples,functions,structuresand/orsolutionsfromthe biospheretothemanufacturingtechnosphere.

Inregardtobio-inspiration,thelowerpartofFig.6showstwo circlestoillustrate theseparate areasof thebiosphereandthe technosphere,wherebyinspirationis drawnfromthebiosphere intothetechnosphere.Thegreycirclerepresentsthetechnosphere andthegreencirclerepresentsthebiosphere.

ProposeddeﬁnitionofBio-IntegratedManufacturing:

Bio-integratedmanufacturingisrealisedbyintegratingelements fromboththebiosphereandthetechnospherewithinthemanufactur- ingenvironment.

In Fig. 6 (lower part, right hand side) bio-integration is illustrated by showing an overlap of the biosphere and the technospherecirclestohighlightthefact thatelementsof both spheres are integrated within the technical manufacturing solution/system.

ProposeddeﬁnitionofBio-IntelligentManufacturing:

Bio-intelligent manufacturing is realised through merging ICT- enabledintelligentparadigmswithbio-inspiredand/orbio-integrated manufacturingsolutions,incorporatinginformationchannels,sensor andactuatorsystems.Aspecialformofbio-intelligentmanufacturing

is when co-existence, mutual interactions and co-evolution of technical, informational and biological elements (or sub-systems) takeplace,withthepotentialofconvergingtowardslivingsystems.

Finally,inFig.6itisshownthatinordertoreachthestageof bio-intelligence in technical manufacturing solutions/systems (illustrated by the blue circle at the top of the diagram), ICT enabledintelligentparadigms,suchasArtiﬁcialIntelligence(AI), MachineLearning(ML),DeepLearning(DL),EvolutionaryCompu- tation(EC)etc.,arerequired.Additionally,itisshownthatthereis alsoarequirementformergingwitheitherbio-inspirationorbio- integrationorwithboth.

ThehorizontaldoublearrowinFig.6referstothepossibilityof interaction between bio-inspiration and bio-integration in manufacturing solutions that can also be developed through convergence.In relationtothetwoslantedarrowstowardsbio- intelligence(bluecircle),inadditiontotheinteractionillustrated, theinclusionofICTenabledintelligentparadigmsisarequirement.

It is noted that the above deﬁnitions are proposed by the authorsandthattheyrequirefurtherconsiderationbeforebeing recommended for adoption by the community involved in manufacturing research. In this regard, a joint activity was establishedbetweentheCollaborativeWorkingGroup(CWG)on BiologicalisationinManufacturingoftheInternationalAcademyfor Production Engineering (CIRP) and the CIRP Committee on Terminology.Theaimofthejointactivityistoprovideproposals and solutions to address terminology issues concerning the BiologicalTransformationinManufacturing.

Manufacturing system performance improvement is consid- eredinthelightofbroaderperspectivesandhassustainabilityasa corefocus.Besidesthetechnicalprocessefﬁciencyaspects(suchas higher degrees of automation, increased output, part quality, knowledge in the company and retained skills within the manufacturing system) also ethical questions, economic issues andinparticularsustainabilityissuesareallhighlyrelevant[21].

Fourdemonstratorsweredevelopedtogatherevidenceshowing a performance improvement from the Biologicalisation in Manufacturing(BiM)perspective.Theareasofthedemonstrators selectedandtheassociatedprimaryfocusofeachwereasfollows:

Demonstrator1:Bio-basedDesignMethodologiesforProducts, MachineToolsandProcesses(primaryfocus:bio-inspiration), Demonstrator 2:Microbial-basedCuttingFluids in Machining

(primaryfocus:bio-integration),

Demonstrator 3: Bio-Inspired, Self-Learning Additive ManufacturingSystems,MachinesandProcesses(primaryfocus:

bio-inspirationandbio-intelligence)and

Fig.7.Demonstrator1:Mobileandﬂexiblemachinetool.

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Demonstrator 4: Robust, Cooperative Manufacturing Systems (primaryfocus:bio-intelligence)

The work undertaken in each demonstrator is described in detailbelow.

Demonstrator1:bio-baseddesignmethodologiesforproducts, machinetools,processesandsystems

Theemphasis withinthis demonstratorwasonthedevelop- mentofdesignmethodologiesforcomplexproductsandproduc- tionsystems[22].SuchaconcepthasalsobeenproposedbyZhang et al., whereby innovative design methodologies have been implemented in the optimisation of advanced manufacturing systemsforthecaseofadditivemanufacturing[23].In orderto allowbiologicalprinciplestobeimplementedintothebio-inspired designoftechnicalcomponents,approacheshavebeendeveloped todeterminethebestsetsofsolutions.Thedemonstratorusesa scientiﬁcally based method for the systematic collection and storage/retrieval of bio-inspired design principles (bio-design base) as aninputfor set-baseddesign. Acomputational design synthesis algorithm then evaluates the design principles and develops possible design solutions for technical components/

systems.

Thecomputationaldesignsynthesismethodsdevelopedwere applied to the design of the kinematic layout and adaptive clampingdevices offlexibleandmobilemachinetools.Further- more, thedesign methodologycouldbe demonstratedon light weight clamping jaws and surface structures with biological integrated layers to protect mechanical components of the machine tool. The overall workflow involved in this design demonstrator isillustratedindetail inFig.7.Depending onthe goals of a design solution, functional surfaces, lightweight componentsandintegratedsensorscansystematicallyimplement differentbiologicalprinciplesasinputdatabyinspirationand/or integrationforeffectiveresults.Theinspiringbiologicalphenom- enonandprinciplesrelatedtotheflexiblemachinetool,discussed in thepaperarelegandbody structures,sensors,movementof animalsandinsects,aswellaslegadheringmechanismtosmooth surfaces.Fromthebiologicalinvestigations,itcouldbeconcluded that legged locomotion(bio-inspired)is themostadvantageous meansoflocomotion.Six-leggedlocomotionexcelsespeciallyon uneven,unexpectedterrainandsurfaces.

Theprimaryfocusof thisdesign demonstratorrelatestothe potential of biological systems as sources of inspiration in the design of manufacturing systems. In nature, an abundance of

versatile solutions exists for design problems that may be encountered in a contemporary engineering environment. By carefullyobserving,collecting,sorting,cataloguing,examiningand thus understanding the solutions to these problems that have evolvedinthenaturalworld,itispossibletosolvetheanalogous engineering problems in efﬁcient and effective ways. Another methodfor being inspiredby nature consistsof emulating the methodbywhichnaturehasarrivedatthesesolutions:evolution.

Bymakinguseofiterativedesignmethodologiesandselection criteria,amoderncomputationalsimulationcanalgorithmically optimise a design to generate a new solution to the problem encountered. As a demonstration of this bio-inspired design methodology,theworkreportedheresoughttodesignamobile machinetoolusingbio-inspiredmethods,attemptingtoimprove existingdesignsfromtheperspectiveofanumberofkeymetrics.

The overall workﬂow involved in this design demonstrator is illustratedindetailinFig.7.

Demonstrator2:microbial-basedcuttingfluidsinmachining This demonstrator refers to physical classical machining processes(turningandmilling)operatingwithnovel,sustainable microbial-basedcutting fluids and endowed withsmart sensor monitoring.It providesa tangibleexampleof bio-integrationin advanced manufacturing. It is easy to visualise the integration aspect as living organisms are supplied directly into the manufacturing system. This demonstrator had the objective of early stage testing to evaluate whether the mineral oil in conventional cutting fluids can be substituted with suitable microorganismsfor thelubrication functionwithoutnegatively affecting performance and manufacturing process efficiency, thereby eliminating the former’s negative environmental and societal impact. The work to validate this hypothesis element focused on systematic metal cutting tests using cutting fluids basedonamicroalgaestrainandtwostrainsofyeast,respectively.

The fundamental feasibilityof using cutting ﬂuids based on microalgaeandyeastfordifferentmachiningoperations(turning of carbon steel and milling of titanium Ti6Al4V alloy) was demonstrated.Threemicrobial-basedcuttingﬂuidsweresuccess- fully produced with a microalgae strain (Spirulina arthrospira maxima)andtwostrainsofyeast(Saccharomycescerevisiaeand Metschnikowiapulcherrima).

Micrographsofthespirulinaplatensiscellsfollowingmachin- ingatdifferentcuttingspeedsareshowninFig.8.Images(a)and (b)intheﬁgurerefertosamplesrecoveredafterturningwithlower cutting speed (vc=130m/min) and (c) and (d) imagesrefer to

Fig.8.Microscopicviewofmicroalgae-basedcuttingﬂuid(notethatthebarlengthshownrepresents100mm).

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samples recovered after turningwith higher cutting speed (vc

=230m/min). [(a) Red circle: undamaged cells. (b) Red circles:

undamagedcells(left)andfragmentedcells(right).(c)Redcircles:

cell fragments (left) and cell lysates(right). (d)Red circle: cell lysates(cellmembranedestruction)].

Demonstrator3:bio-inspired,self-learningadditivemanufacturing systems,machinesandprocesses

The new paradigm associated with this demonstrator is to employand implementmorefunctionalitiesof biological,living systems in new technical systems, i.e. a biologicalisation of technology. The basic hypothesis is that for manufacturing machines, using the example of selective laser melting (SLM) machines,there isa significantbenefitthroughtheadoptionof artificialintelligence,wherebyartificialintelligenceisunderstood tobeadeeplyintegratedcombinationofalargenumberofrelated technologies. Manufacturing processesrequire skilledoperators and these are a scarce resource. The basic concept of the demonstrator thus revolves around the integration of the operator’sskillstothegreatestpossibleextentintothemachine.

This atthe sametime means anincrease in thecapabilities of process observation combinedwith on-lineclosed loop control strategies for the repair of imperfections induced during manufacturing, along with self-learning cycles. An integrated expertsystem storesdataonoperatorexperienceandgivesthe reasoning for the provision of useful information for new manufacturing tasks. Furthermore, besides state monitoring, channelsformachinecommunicationneedtobedesignedtosuit thesourceofinformation,othermachines,operators,manufacturing tasks and/or manufacturing execution systems (MES). An overviewontheconceptisprovidedinFig.9.Twocriticalaspects oftheconceptareelaboratedoninmoredetail:

a.)Utilisationofacameraasbroadbandsensoryforthein-situ recognition of faults developing on individual layers, as a prerequisiteforrepairstrategiesand

b.)Featuresegmentationofproductstobebuiltandsynthesisof featureprogramstotheproductprogramasaprerequisitefor

self-learning of manufacturing strategies and process para- metrisation.

Amachinelearningapproach(pre-traineddeepconvolutional Neural Network-based image processing) was tuned for the identiﬁcationofdeviationsinaSLMlayer.Thiswasimplemented foron-linefaultrecognitionbasedonautomaticimageprocessing duringselectivelasermelting ofmetallic powders.The concept allowstheidentiﬁcationoftheonsetofdefectsduetoprocessnon- conformitiesandtosubsequentlycounteractorcorrectsuchfaults in thenext layer.This canlead toimprovedpartqualityand a reductionofscrap.

VariousArtiﬁcialIntelligence(AI)toolswereinvestigatedfor different tasks within the SLM process chain, namely image recognition for correction of faults and irregularities, nesting optimisation, support structure optimisation, build strategy evolutionand processparametergenerationfornewalloysand powder speciﬁcations. Communication, learning from skilled personsandinformationprovisiontounskilledoperatorsrepre- sentbasicelementsofabio-intelligentsystemofthisnature.Fault detectionwithfullyintegratedsensorsystemsandthequestionof how to deal with the outer learning cycle (namely feature segmentation, layer-wise correction to enhance part quality) formedamajorpartoftheworkofthisdemonstrator.Ageneral layout of the bio-intelligent manufacturing system with all required functionalities was investigated. Fig. 9 illustrates the overallconceptassociatedwiththisdemonstrator.

Demonstrator4:robust,cooperativemanufacturingsystems Thisdemonstratorrelatesto“StemCellDiscovery”,anautomat- edstemcellproductionplatformdevelopedattheLaboratoryfor MachineToolsandProductionEngineering(WZL),RWTHAachen University and at the Fraunhofer Institute for Production Technology(IPT),Aachen(illustratedinFig.10).

Here,theproductisnotapassiveelementoftheproduction processbutratheralivingorganism.Thisfactinduceschallenges suchas:1)thereisinherentdiversityoftheproducts(stemcells), 2)thereisvaryinggrowingspeedsandprocesstimes,3)thereisa

Fig.9.Conceptofabio-intelligentAMsystem.

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need for theirregularobservation andprocess adaptation, and, therefore,4)aneedformixed-initiativeproductioncontrol.

Adistinctivefeatureofthedomainisthesymbioticco-existence andco-evolutionofthetechnical,ICTandbiologicalelementsin production structures. According to the understanding of the participants involved with this demonstrator, this represents anotherlevelofbiologicalisationinmanufacturing,referredtoby someasbio-intelligence(seedeﬁnitionabove).

Thenovelsolutiondevelopedinthisdemonstrator,proposedby theInstituteofComputerScienceandControl.Budapest,involves the use of reinforcement learning (RL) technics, a biologically inspiredmachinelearningapproach,i.e.learningfrominteractions forcontrollingmanufacturingcellsproducingbiologicalmaterial [24].Forthispurpose,asimulationmodeloftheautomatedstem cellproductionplatformStemCellDiscoverydevelopedinAachen wasprogressed.Thesimulationsystemwasused,ontheonehand, fortestingdifferentscenariosofthepresentcelltype,andonthe otherhand,forimprovingtheplatformperformancebyreinforce- mentlearningbasedalgorithms.

Impactanalysisofbiologicalisation–Convergencetowardsthe LivingManufacturingSystem

Theresearchhypothesisassociatedwiththeworkoutlinedhere was “that FutureManufacturingSystems will incorporate components, features, characteristics and capabilities that enable the convergencetowardslivingsystems”.Thedemonstratorsdescribed in high level summary form in Section 4 above assessed this hypothesis, proactively sought concrete evidence to show as clearly as possible that a convergence of biology with the engineering and physicalsciences is taking place and that this convergencewillhaveasigniﬁcantimpactontheperformanceof advanced manufacturing systems and in turn on industrial developmentbeyondIndustry4.0.

Abiological systemcanbedeﬁnedasa complexnetworkof biologicallyrelevantentities,suchasgenes,proteins,metabolites etc.interactinginanon-linearfashionandactingasanetworkto performdifferenttasks/functions.Thenewparadigmbeingtested in the study reported here is the employment of more functionalities of biological, living systems and implementing them in new technical systems. Such new systems will be differentiatedfromcurrentsystemsbytheirlife-likenature,hence theemergenceofthenewconceptof the“LivingManufacturing System” in relation to discrete component manufacture. The expectationthereforeisthatadoptingbiologicalfunctionalitiesfor machinesandmanufacturingsystemsincreasestheirsustainabili- ty and optimises their behaviour within their operational and environmentalconditions.

PrimaryFocus-Bio-Inspiration:

Demonstrator1–Bio-basedDesignMethodologies forProd- ucts,MachineToolsandProcesses

Fig. 11 highlights the fact that the primary focus of this demonstratorwasonbio-inspiredmanufacturing(greyandgreen circlesseparated,thesolidredlinedboxintheﬁgurerepresents theprimaryfocusofthedemonstratorandthedottedlinedboxes thesecondaryfocus).Italsoindicatesthat furtherdevelopment can take place towards bio-integration (grey and green circles overlapping).Furtherdevelopmentcan alsotake placetowards Fig.10.TheStemCellDiscoveryplatforminAachen(WZL/IPT).

Fig.11.Relationshipbetweenbio-inspiration,bio-integrationandbio-intelligenceinregardtoDemonstrator1.

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bio-intelligentmanufacturing(bluecircle)throughincorporating ICT-enabled intelligent paradigms such as smart sensors and intelligentprocessoptimisationinthebio-integratedmanufacturingsolution.

Itwasshowninthisworkthatthebenefitsofbio-inspirationin manufacturing can be substantially strengthened by applying systematic mapping of scientific publications of both domains (biologyandmanufacturingengineering).Itwas concludedthat the biological principle of “Effectiveness” in achieving goals by meansofredundancycanbemimickedbyapplyingmethodsof Computational Design Synthesis (CDS) enabled by (massive) digitalisation.Thecombination ofbio-inspired,shape optimised additive manufacturing enables lighter and functionally optimised/integratedparts.Inoneexampleitwasshownthattopology optimisationofabio-inspiredgripperledtoweightreductionfrom 312gto50g.Configurationdesignimprovedmobilemachinetools - towards autonomous mobility. It was concluded that new innovativewaystodealwithchangingdesignconstraintsemerge whenaproactivefocusisplacedonbio-inspiration.Theresultsof theworkonthisdemonstratoraresummarisedinTable1.

This result can have signiﬁcantimplications for the perfor- manceof manufacturingprocesses,machines,systemsandnet- works.Ensuingmethodsofcomputationaldesignsynthesiscanbe applied at all levels within the manufacturing systems and networks.Eachoftheotherdemonstratorsreportedinthisstudy embodiesvery speciﬁccharacteristicsandchallenges relatedto thisdesign-focuseddemonstratorincluding:

Developingdesignsupportsystemsthatsystematicallymakeuse ofprinciplesinspiredbybiology,

Delivering integrated interdisciplinary support for complex systemdesign,

Creatingawarenessamongdesignersthat(classical)optimisa- tionstrategiesmaynolongerbefullyeffective,

Realisingaparadigmshiftfromtoday’ssplitofmanufacturing intointelligentpreparatoryworkandexecutionworktowardsan integratedsystemembodyingbothand

Deploymentofcoordinationandcooperationstrategiesthatlead to adaptive, ﬂexible and coherent collective behaviour in manufacturingsystemsandsupplychains.

Theconclusionisthatsystematic andstructuredapproaches canbeadoptedwhichcanmoreeffectivelysearchforthehighest potentialofbio-inspiration.Theapproachofcomputationaldesign synthesiswillpermitthedevelopmentofnewmethodologiesfor enhancingtheperformancelevelsofbio-inspiredmanufacturing systems. New and exciting opportunities will emerge for the industriesinvolvedinadvancedmanufacturingasthecomputa- tionaldesignsynthesisapproachmatures[22].

PrimaryFocus-Bio-Integration:

Demonstrator2–Microbial-basedCuttingFluidsinMachining Demonstrator2hadtheobjectiveoftestingwhetherthemineral oilinconventionalcuttingﬂuidscanbesubstitutedwithsuitable microorganisms without negatively affecting performance and manufacturingprocessefﬁciency,therebyeliminatingtheformer’s negativeenvironmentalandsocialimpact.Fig. 12highlightsthefact thattheprimaryfocusofthisdemonstrator isonbio-integrated manufacturing(greyandgreencirclesoverlapping,thesolidred linedboxrepresentstheprimaryfocusofthedemonstratorandthe dottedlinedboxthesecondaryfocus).Italsoindicatesthatfurther developmentcantakeplacetowardsbio-intelligentmanufacturing (blue circle) through incorporation of ICT enabled intelligent paradigmssuchassmartsensorsandintelligentprocessoptimisa- tioninthebio-integratedmanufacturingsolution

Twosub-demonstrators(oneinItaly,R.TetiandoneinSouth Africa, O.Damm) weresuccessfully developed which showthe benefits of bio-based cuttingfluids for theturning and milling processes. For each of the microbial-based cutting fluids, the cutting forces, tool wear and workpiece surface finishes were foundtobecomparabletoorbetterthanforconventionalcutting fluidinturning(microalgae-basedcuttingfluid)andinhigh-speed milling (yeast-based cutting fluids). The microbial cells in the cutting fluid were severely damaged during the turning and millingtests, withonly 5–10% of cells surviving. Theextent of damagewasfoundtoincreasewithincreasingcuttingspeeds.

Theimpactoftheresultsofthisdemonstratoronmanufactur- ingsystemsin thecontextofbiologicalisationissummarisedin Table2.

It is concluded that although these are very early stage demonstratorsand that highlevelsof complexityarise in such investigations, the potential exists to significantly reduce the considerablevolumesofmineraloilrequiredforthemachiningof metallicmaterials.Indeed,theeliminationoftheuseofmineral oilsincuttingprocessesmaybepossibleatsometimeinthefuture -achievedthroughtheintegrationofbiologicalmaterialsintothe cuttingfluid.Theimplicationsofthisbecomeevermoresignificant asthecriticalquestionofsustainabilityofmanufacturingsystems moves centre stage in relation to the achievement of the UN SustainabilityGoals.

It is very clear however, that these demonstrators require further research and analyses. Besides the open technological questions,therearealsoopenissuesregardingtheintroductionof microorganismculturesintothemanufacturingenvironment.In thisregard,itistobenotedhowever,thatthereareexamplesfrom othersectorse.g.thefoodsector,wherelivingcells/cultureshave beeninanindustrial,scaled-upmanufacturingenvironmentfor manydecades.Theoveralleconomicsoftheuseofmicrobial-based ﬂuidsclearlyneedsdetailedinvestigation.

Thisdemonstratorprovidesaverygoodandrelativelyeasyto understandexampleofbio-integrationinadvancedmanufacturing systems[25,26].

Table1

Bio-inspirationandtheimpactofDemonstrator1.

ImpactofDemonstrator1:Bio-basedDesignMethodologiesforProducts, MachineToolsandProcesses

Impactondesignmethodology:

Showsthevalueofamethodicalapproachofbiology-inspireddesignand manufacturingtechnologies

Showsthevalueofsystematiccomputationalanalysisofliteraturefromthe biologydomain

Demonstratestheapplicabilityofbiology-inspireddesignmethodologies Conﬁrmscomputationaldesignsynthesisasatooltosupport“Manufacturing

forDesign”

Presentsinnovativewaystodealwithchangingdesignconstraints Dynamicdatastructuresfordesignprocessesbasedonontologies Constrainedset-baseddesign

Impactonmanufacturingsystemsandbiologicaltransformation:

Showstheviabilityofthecombinationofbiology-inspiredgeometry, materialandsurfaceproperties,sensorsandcontrolstrategies

Explorestherelationshipbetweenbio-inspiration,bio-integrationandbio- intelligence

Capturingofbiologicalknowledge

Incorporatingnewmanufacturingmethodsandtailoredmaterials(including additivemanufacturing)

Impactonsustainability:

Weightreductionofproductsandsystemsleadingtodiminishedenergy utilisation

Higherlevelofadaptability,leadingtoshorterdesigncyclesandfewerwasted resources

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PrimaryFocus:Bio-InspirationandBio-Intelligence:

Demonstrator 3 - Bio-Inspired, Self-Learning Additive ManufacturingSystems,MachinesandProcesses

Thebasichypothesisassociatedwiththisdemonstratoristhat formanufacturingmachinesusingthespecificexampleofselective lasermelting(SLM)machines,thereisasignificantbenefitthrough theadoption ofbio-intelligenceasfurtherelaboration ofAI.An attemptwasmadetouseacontrolsystemthatintegratesthetasks anddecisionmakingoftheoperator/expert.He/sheiscapableof observingtheprocessandinteractionbasedonhis/herexperience, which he/she in turn gained from operating the machine and theoretical knowledge of the physics of the process. The functionalityoftheoperatorasabiologicalsystemwasintegrated intothemanufacturingsystemtoincreasetheautonomy.Besides this, an artificial operator might acquire some additional capabilities,asforinstanceobservingtheprocessbyitsinfrared emissions.Inthedemonstrator,thenecessityofabio-intelligent approach was shown. Thisis in accordance withthedefinition abovewhichisunderstoodinthiscontextasadeeplyintegrated combinationofalargernumberofdifferenttechnologies,sensors

andinformationchannelswithalearningontologybasedexpert system.Theworkonthisdemonstratorhashighlightedtheneed forverycarefulassessmentwhencomparingnaturalandtechnical solutionsinordertosuccessfullyadoptbio-inspiredprinciplesand integratethemintomanufacturingsystems.

Thedemonstratorpresentsthehighlychallengingambitionof benchmarking against the human brain and the associated intelligencelevel.Thehumanbraincansignificantlyoutperform technicalsolutionsforenhancedquality,efficiency,robustnessand sustainabilityofadditivemanufacturingandothermanufacturing processes.Thedemonstratorsuccessfullyshowedthatincorporat- ing highlevel (towardshuman level) intelligencefor technical systems can improve the quality, robustness and efficiency of additive manufacturing and other manufacturing processes.

Adopting biological functionalities for machinesincreases their sustainability and optimises their behaviour within their own operatingandenvironmentalconditions.

Fig.13showsthattheprimary focusofthis demonstratoris both on bio-inspired manufacturing (grey and green circles separated, boxeswith solid red lines) and bio-intelligent (blue circle)manufacturing.

Theworkundertakenhasshownthatmanufacturingmachines (e.g.additivemanufacturingmachines)equippedwithAIwillbe capableofpermanentlyacquiringnewknowledgeandcontinually seekingself-optimisation.

Careful estimation of technological consequences of the investigationundertakenraisestheexpectationof:

a)fault reduction and increase in the dynamic strength of componentstothelevelofthatofforgedparts,

b)faultcorrectiontoreducethescrapduetoprematurelyaborted buildjobsbyafactorof10duetoearlyin-situcounteraction afterdetectionofdeviations,

c)Reductionofaveragesetuptimesintoday’sproductionmixin SLMbyafactorof2–4duetotheinputofoperatorexperience andnewlygeneratedknowledge.

It is concluded that a useful and highly autonomous manufacturing machine (example SLM) can be designed with Fig.12.Relationshipbetweenbio-inspiration,bio-integrationandbio-intelligenceinregardtoDemonstrator2.

Table2

Bio-integration and the impact of Demonstrator 2 – technical/engineering perspective.

ImpactofDemonstrator2:Microbial-basedCuttinginFluidsinMachining Impactonmachiningprocess:

Cleaner,healthierworkingenvironment Costreductionofmachiningprocess

Impactonmanufacturingsystemsandthebiologicaltransformation:

Costreductionofmachiningprocesscanalterorleadtoprocesssubstitution inmanufacturingsystems

Bridgebuildingbetweenmanufacturing/technologyandnatural/biological sciences,leadingtonewsolutions

Demonstratorshowsapplicableapproachforbio-integration Simpliﬁedsupplychainsthroughdecentralised,localproduction Impactonsustainability:

Useofrenewablematerialsandsimplerend-of-lifedisposal

Improvedhealthandsafety–benignmaterialscompatiblewithhumans Reductionofenvironmentalfootprint(notoxicwaste,highlyreducedCO2

footprint)

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methodsavailabletodayincludingAItechnologiesandcomputa- tional power. It is also recognised that simply using Artiﬁcial NeuralNetworks(NN)forindividualtasksalonedoesnotallowthe full exploitation ofthe potential ofAI. It is thecombination of differentAItechnologiesandtheirintegrationintotheinformation streamsgatheredfrombio-inspiredsensoryhuman-likecommu- nicationchannelswithpeople,machines,manufacturingexecu- tion systems and the internet that hasthe majorimpact.To a certainextentthismeanstheintegrationofthecapabilitiesofthe skilledoperatorintothemachine.

The impact of the Demonstrator 3 in the context of biologicalisation in manufacturing is summarised in the Table 3 below.

The underlying principles can be applied to other machine types in advanced manufacturing systems. The demonstrator clearlymovesthethinkingforwardintotherealmofautonomous, bio-intelligentsystems.Developmentsaretakingplaceatsucha rapid rate that deeper levels of bio-intelligent manufacturing systemswillberealisable,albeitinanincrementalmanner,inthe nearfuture[2].

PrimaryFocus:Bio-Intelligence:

Demonstrator4-Robust,CooperativeManufacturingSystems Thisdemonstratorrelatestothe“StemCellDiscovery”automat- ed stem cell production platform developed at the Fraunhofer InstituteforProductionTechnologies(IPT),Aachen(Fig.10).The primary focus ofthis demonstrator was onthebio-intelligence aspectofbiologicalisationinmanufacturingandthedevelopment of bio-inspired algorithms such as reinforcement learning for control.Theresultsachievedinthisdemonstratorwere:optimised process parameters, better resource usage, higher throughput, increased and moreuniformquality, cost reduction and higher robustness/resilience.

Fig. 14 highlights the fact that the primary focus of this demonstratorisonbio-intelligentmanufacturing(bluecircle,solid redlinedbox).Italsoindicatesthatbio-inspired(greyandgreen circles separated) and bio-integrated (grey and green circles overlapping)arealsoincorporated(illustratedbythedottedred linedboxes).

The two scenarios which included theconﬂuence threshold resultedinapprox.15–20%increaseincellproductionandupto 30% in the largest system. A newbiologically inspired control algorithmcannowbedemonstratedbyusingthissimulation.A newconcept of co-existence,co-evolutionand mixedinitiative controlwasconsidered.

Bridge building was demonstrated between discrete part manufacturing science and technology, on the one hand, and biological/medicalsciences,ontheother.Suchbridgebuildingisa critically important component of the convergence of biology/

medical science with theengineering physicalsciences. It was concludedthattheautomatedproductionofbiologicalmaterials (such as stem cells) represents perhaps the highest level of biologicaltransformationinmanufacturing,whereasymbioticco- existenceandco-evolutionofthetechnical,ICTandbiologicalsub- systemsaremanifested.

Table 4 summarises the impactof this demonstrator in the contextofbiologicalisation.

This demonstrator provides early stage evidence of the signiﬁcanceof the“biologicalisationinmanufacturing”strategic approach tothe developmentof future manufacturingsystems.

Theearlyresultsclearlyunderlinethesignificantbenefitswhich canbederived.Thephysicalstem celldemonstratorelementis availableonthesystemintheFraunhoferInstituteforProduction Technologies (IPT), Aachen, Germany. The simulation of the functioning of the present StemCellDiscovery platform can be demonstrated by the software developed at the Institute of ComputerScienceand Control, Budapest,Hungary.Thebiologi- callyinspiredcontrolalgorithmcanalsobedemonstratedbyusing thissimulation.Thisresultishighlysignificantinthecontextof productivityimprovementsandthebiologicalisedmanufacturing system[27].

Table 5 provides an overall summary of some of the key evidencewhichwascollatedfromtheeachofthedemonstrators described abovefor whicha focused biologicalisationapproach wasadopted.Itwasshownthatforeachdemonstrator,signiﬁcant beneﬁts in the performance of the relevant element of the manufacturingsystemcouldbedemonstrated.

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It is concluded for all four demonstrators, that due to the significantperformancebenefitsthatcanbeachievedthroughthe incorporationofbio-inspired,bio-integratedand/orbio-intelligent aspects,thatfuturemanufacturingsystemswillconvergetowards living systems. This conclusion has implications which are of enormous consequence as it has the potential to reshape the design,functioningandperformancelevelsoffuturemanufactur- ing systems and to have a highly positive impact on global sustainabilitygoals.Basedonthedepthofanalysisundertakenin each demonstrator,it is concluded that an early stage body of scientificallybasedevidencehasnowbecomeavailablethrough thisworkwhichprovidesinitialvalidationoftheappropriateness togivefurtherandmoredetailedconsiderationtotheconceptof the “LivingManufacturing System” (LMS). This evidenceencom- passesforexample;higherperformanceanddecreasedweightof componentsthroughbio-inspireddesignmethodologies,mineral oil reduction in manufacturing processes, optimised process parametersandincreasedprocessquality,stabilityandrobustness through theuse ofdiffering levelsof ArtificialIntelligence and

Machine Learning along with the application of biologically inspiredcontrolalgorithmsetc.

When considering manufacturing system performance, it is importantthattheengineeringandtechnicalperformanceaspects notbeconsideredinisolation.Wemustincorporateareassuchas ethics,sustainability,economics,systemrobustnessetc.Inregard toethicswhereparticularattentionisrequired,itisclearthatitis veryearlydaysinthenewconvergenceofbiologyandengineering andonlyverylimitedresearchworkhasbeenundertakenonthis topicasrelatedtotheﬁeldofthebiologicaltransformation.The biologicalisation of manufacturing provokes several speciﬁc ethical challenges including those pertaining to unintended consequencese.g.moralhazard,responsibilityetc.[28].Incorpo- rationofdetailedconsiderationofethicalquestionsintoengineer- ing/technology research is particularly important in order to achieveincreasedawareness,openness,andcourse-correction,as wellasaclearervisionforfuturedevelopments.

The implications of biologicalisation in manufacturing are wide-rangingandthereisahighlikelihoodthatamajorparadigm Table3

Bio-InspirationandBio-Intelligence–impactinthecontextofbiologicalisationin manufacturing–Demonstrator3.

ImpactofDemonstrator3:Bio-Inspired,Self-LearningAdditiveManufacturing Systems,MachinesandProcesses

Impactonadditivemanufacturingprocesses:

Highrequirementsofindustrialprocessescanonlybemetbyadditive manufacturingprocessesthroughtheintegrationofAI

GenerationofnewknowledgeinSLM

EnhancedqualityandproductivityofAMprocesses

EnhancedqualityandproductivityofAM,thusleadingtocostreductionand thereforeawideradaptationofAMinmanufacturingsupplychains DemonstrationofbeneﬁtofBio-IntelligenceinAMwithrelativelyeasy adaptiontoothermanufacturingprocesses

Demonstratorpavesthewaytoabio-inspiredintelligentmanufacturing system

Demonstratormakesimplicitknowledgeexplicite.g.makingtheknowledge ofskilledoperatorsavailabletothecompany

Reductionofenvironmentalfootprint(reducedscrap,reducedset-upand processtimes,increasedthroughput)

Table4

ImpactofDemonstrator4.

ImpactofDemonstrator4:Robust,CooperativeManufacturingSystemsand ProductionNetworks

Impactonmanufacturingprocess:

Minimisationofhumanerrors,enhancementofreproducibility

Enhancedqualityandproductivitythroughhigherthroughputofthesystem, leadingtocostreduction

Improvedrobustness(resilience,ﬂexibility,changeability,agility, responsiveness,adaptabilityetc.)

Enhancedpredictabilityandincreasedplanningability

DemonstrationofbeneﬁtofBio-Intelligence

Bridgebuildingbetweendiscretepartmanufacturingscienceandtechnology ontheonehandandbiological/medicalsciencesontheotherhand Co-existenceandco-evolution,aswellasmixed-initiativecontrol Improvedrobustness(resilience,ﬂexibility,changeability,agility, responsiveness,adaptabilityetc.)

Balancingbetweenrobustness,complexityandefﬁciency