Chaplin, Jack C.

de Silva, Lavindra

Felli, Paolo

Logan, Brian

Ratchev, Svetan

Sanderson, David

Name not available

Synthesising industry-standard manufacturing process controllers

Nottingham ePrints

de Silva, Lavindra and Felli, Paolo and Chaplin, Jack C. and Logan, Brian and Sanderson, David and Ratchev, Svetan (2017) Synthesising industry-standard manufacturing process controllers. In: Sixteenth International Conference on Autonomous Agents and Multiagent Systems, 8-12 May, 2017, São Paulo, Brazil. Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/41489/1/aamas-paper.pdfCopyright and reuse: The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions.This article is made available under the University of Nottingham End User licence and may be reused according to the conditions of the licence.  For more details see: http://eprints.nottingham.ac.uk/end_user_agreement.pdfA note on versions: The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher’s version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.ukSynthesising Industry-StandardManufacturing Process Controllers†(Demonstration)Lavindra de Silva∗ Paolo Felli∗ Jack C. Chaplin∗Brian Logan∗∗ David Sanderson∗ Svetan Ratchev∗∗Institute for Advanced Manufacturing, University of Nottingham, Nottingham, UK∗∗School of Computer Science, University of Nottingham, Nottingham, UKfirst-name.last-name@nottingham.ac.ukKeywordsManufacturability; controller synthesis; manufacturing as a service1. INTRODUCTIONDetermining the most suitable means of producing a given prod-uct and their ordering, or process planning [6], is traditionally doneby manufacturing engineers who are experts in the internal pro-cesses and layout of a specific factory, and, with the exception ofsome limited support by Computer-Aided Process Planning tools[6], is largely a manual process. From the perspective of “manu-facturing as a service”, where the customer’s product is not knownin advance, the traditional approach has drawbacks: it requires hu-man expertise to determine whether the product can be manufac-tured using the resources of a given service provider, and the smallbatch sizes (perhaps a single item) make the manual production ofprocess plans uneconomic. To fully realise the manufacturing as aservice vision, process planning must be automated, allowing ser-vice providers to ‘bid’ to manufacture products in real time.In [4], an approach was proposed to determine both whether aparticular product is manufacturable given a set of available man-ufacturing resources, and how the product should be manufacturedusing those resources. In this paper, we present a tool that imple-ments the definitions and algorithms in [4].1 We also link the ab-stract representations of [4] to concrete ISA-95 standards [2], andsynthesise process plans suitable for execution by industrial manu-facturing systems. The core of our tool is a new reasoner for orches-trating the activities of agents in the Evolvable Assembly Systems(EAS) architecture, an agent-based architecture for manufacturingcontrol software designed to address rapidly changing product andprocess requirements [3].2. RESOURCES AND TOPOLOGYIn EAS, each resource agent represents and controls a manu-facturing resource via a Programmable Logic Controller (PLC). Aresource is either a production resource (e.g. a robot) which per-forms manufacturing operations on parts, or a transport resource(e.g. a conveyor or shuttle system) which moves parts betweenproduction resources. Both types of resource are modelled as la-belled transition systems (LTS): states represent resource (and part)configurations, and edges represent operations that are possible atthe relevant states. Special edges—called synchronisations and de-†This work was supported by the Engineering and Physical Sci-ences Research Council [grants EP/K018205/1 & EP/K014161/1].1A video of the tool is available at https://youtu.be/SEveuikI3p8.noted IN: and OUT: —indicate the transfer of parts into and out ofresources, and nop edges denote an “idling” operation.We have used our tool to model the resources of the PrecisionAssembly Demonstrator (PAD), a real-world manufacturing systemthat manufactures detent hinges for the cab interiors of commercialtrucks [1]. To produce a hinge, paired interior and exterior plasticleaves are attached with a metal hinge pin. Glue is applied to securethe hinge pin, and serial numbers are then engraved onto the leaves.The PAD is modelled as five resources. Resource R1 can load anew pallet-fixture, remove a product for disposal or rework, or storeit for delivery. Resource R2 is a robotic arm that can take as inputa fixture that is separated into the hinge and pin or it can take thehinge and pin as separate inputs, insert the pin into the hinge barrel,and output a single assembled part. Robot R3 can engrave a serialnumber and apply glue to a given part, and R4, the testing station,can gather and analyse image and force data. R5 is a transportresource whose transitions represent the ‘allowable’ routes betweenproduction resources. Thus, while a newly loaded fixture can movefrom R1 to R2, a freshly glued part cannot move from R3 to R4 inorder to prevent it becoming affixed to the force testing equipment.Screenshots of some LTSs modelled in our tool are shown in Fig. 1.Together the resources form the production topology represent-ing the layout of the manufacturing system. The tool computesthis by taking the cross product of the LTSs representing the re-sources, and removing transitions with no matching ‘in’ and ‘out’synchronisations (e.g. OUT:2 in R5 and IN:2 in R2). Generatingthe topology is exponential in the number of resources and parts inthe recipe [4], but is computed only once for a given manufacturingsystem layout.3. PRODUCTION RECIPESThe product to be manufactured is modelled as a productionrecipe that specifies the constituent parts, the operations required toprocess and assemble them into the final product, any tests that areneeded to verify the product is being correctly manufactured, andhow to respond to the outcome of each test, e.g., whether a prod-uct should be reworked or discarded. Recipes specify how, but notwhere these operations and checks should be performed. Recipesare also modelled as LTSs: edges represent composite operationsthat consume and produce parts, and states represent the state ofparts in the assembly. A composite operation consists of a guardthat tests some property of the product (e.g., what colour it shouldbe, or whether two parts have been assembled correctly), followedby sequential and/or parallel compositions of primitive operationsas in [4].Fig. 1 shows a recipe for a hinge. The transition from the ini-Figure 1: A recipe, and some of the simplified LTSs (R2, R3, R4 and R5) corresponding to the PAD’s resources, respectively.tial state s0 creates a new fixture f (without consuming any parts),separates the pin p and hinge-leaves h, applies glue to p, engravesa serial number on h, and inserts p into h to form h2, which is thenvisually examined, generating test data. Based on an analysis ofthe test data (not shown), h2 is either removed from the system orhinge-force data is collected. Similarly, h2 is either removed orsent to storage.4. MANUFACTURABILITY & CONTROLOur tool determines if a recipe can be manufactured on a pro-duction topology by checking if there exists a ‘task simulation re-lation’ [4] between the recipe and topology. Intuitively, task simu-lation associates states in the topology to states in the recipe withrespect to the current allocation of parts to resources, such that eachtransition (composite operation) in the recipe can be executed bytransitions (operations) in the topology, and the same is possiblefor the entire recipe, irrespective of the outcome of tests and therecipe trace that might be followed at execution-time. If a recipe ismanufacturable, the tool can synthesise a controller that specifieshow resources should be orchestrated in order to execute the recipe.Checking manufacturability and controller synthesis both involve adepth-first search of the topology to ascertain which sequence oftopology transitions can execute each recipe transition.Figure 2: A controller (solution) for producing the hinge recipe.Intuitively, a controller is an annotation of each recipe state withthe corresponding resource states (i.e., topology state) and the partsbeing worked on in those states, and of each recipe transition withthe sequence of topology transitions that need to be executed toperform the recipe transition. The screenshot in Fig. 2 shows acontroller for the hinge recipe. Transition labels are displayed fromtop to bottom, with each row (e.g., ‘[load,-,-,-]’) showing the opera-tion assigned to each resourceR1, . . . , R5 (dashes represent nops).5. INDUSTRY STANDARD CONTROLIn this section, we explain how the controller generated by ourtool is converted to Business to Manufacturing Markup Language(B2MML) [2] format. B2MML implements the ANSI/ISA-95 stan-dards in XML, and is being increasingly widely adopted in manu-facturing [5]. It defines the data and process models that form theinterface to manufacturing execution systems (MESs) [7] that man-age and monitor the work-in-progress on the shop floor (e.g., viaPLCs), and provides a convenient hardware interface for informa-tion systems, including the agents comprising the EAS.In B2MML, each operation is specified as an operations requestthat encodes the parameters to be passed to the equipment con-trollers (e.g., the specific tool to be used for engraving), the material(part) requirements for each step, and any particular requirementsfor the resource to be used (e.g., tolerances on the operation to beperformed). While some of this information must be manually en-tered into “operations templates” when resources are modelled asLTSs, the tool fills in the rest automatically after a controller is syn-thesised.Given a controller, the tool generates its corresponding opera-tions schedule, which is an AND-OR tree representing an “unfold-ing” of the LTS (graph) that defines the controller. This requiresextracting certain details from the controller, such as: (i) the equip-ment that was assigned to perform particular operations; (ii) thematerials consumed and produced by them; and (iii) when partsneed to be transferred and which pairs of resources need to work to-gether in order to achieve this; this ‘transfer’ information is insertedinto the operations schedule as new operations requests. On reach-ing a choice point during the execution of an operations schedule,the first operations request, i.e., the test on each outgoing branch,is executed until all branches have been tried or one of the opera-tions returns ‘success’. In the latter case, the rest of that branch isexecuted until the next choice point is reached.6. CONCLUSION & FUTURE WORKWe have presented a tool that implements the formalism in [4],but also synthesises B2MML controllers for real-world MESs. Weplan to extend our tool (and the approach in [4]) to allow multi-ple resources to perform parallel operations on the same part, andto account for nondeterministic (uncontrollable) outcomes duringproduction. We also plan to make topology generation more effi-cient, by adding the relevant outgoing topology transitions incre-mentally, starting from the initial states of the resources.REFERENCES[1] N. Antzoulatos, E. Castro, L. de Silva, A. D. Rocha,S. Ratchev, and J. Barata. A multi-agent framework forcapability-based reconfiguration of industrial assemblysystems. International Journal of Production Research(IJPR), 2016.[2] Business To Manufacturing Markup Language (B2MML),2015 (accessed February 11, 2017).https://isa-95.com/b2mml.[3] J. C. Chaplin, O. J. Bakker, L. de Silva, D. Sanderson,E. Kelly, B. Logan, and S. M. Ratchev. Evolvable AssemblySystems: A distribted architecture for intelligentmanufacturing. In A. Dolgui, J. Sasiadek, and M. Zaremba,editors, Proceedings of the 15th IFAC Symposium onInformation Control Problems in Manufacturing (INCOM2015), volume 48 of IFAC-PapersOnLine, pages 2065–2070,Ottawa, Canada, May 2015. Elsevier.[4] L. de Silva, P. Felli, J. C. Chaplin, B. Logan, D. Sanderson,and S. Ratchev. Realisability of production recipes. In G. A.Kaminka, M. Fox, P. Bouquet, H. E., D. F., D. V., and vanHarmalen F., editors, Proceedings of the 22nd EuropeanConference on Artificial Intelligence (ECAI-2016), pages1449–1457, The Hague, The Netherlands, August 2016.ECCAI, IOS Press.[5] D. Emerson, H. Kawamura, and W. Matthews.Plant-to-Business (P2B) interoperability using the ISA-95standard. Yokogawa Technical Report, 43:17, 2007.[6] M. P. Groover. Automation, production systems, andcomputer-integrated manufacturing. Prentice Hall Press,2007.[7] B. Scholten. MES Guide for Executives: Why and how toSelect, Implement, and Maintain a Manufacturing ExecutionSystem. International Society of Automation, 2009.

Nottingham eTheses

Synthesising industry-standard manufacturing process controllers

Abstract

Similar works

Full text

Available Versions

Name not available

Name not available

Nottingham ePrints

Nottingham eTheses