- Email: [email protected]
Providing product use knowledge for the design of improved product generations
CIRP Annals - Manufacturing Technology 60 (2011) 211–214
Contents lists available at ScienceDirect
CIRP Annals - Manufacturing Technology jou rnal homep age : ht t p: // ees .e lse vi er. com/ci rp/ def a ult . asp
Providing product use knowledge for the design of improved product generations§ M. Abramovici *, A. Lindner Information Technology in Mechanical Engineering (ITM), Ruhr University of Bochum, Germany Submitted by F.-L. Krause (1), Berlin, Germany.
A R T I C L E I N F O
A B S T R A C T
Keywords: Feedback Product development Knowledge based system
Current design of improved product generations does not exploit use information from previous products systematically. The emerging shift of manufacturing companies from selling products to providing product service systems, the miniaturization of product-embedded sensors, as well as advances in information technology facilitate a product providers access to operation information of current products, which can be used to improve the development and the quality of follower product generations. The paper presents a framework for the acquisition, aggregation and analysis of product use information as well as for the generation and provision of knowledge for the development of improved product generations. The described approach employs knowledge discovery methods like Bayesian Networks and is supported by an IT prototype of a design assistant system. This prototype has been validated in a use case considering the improvement of a rotary spindle for micro machining. ß 2011 CIRP.
1. Introduction The majority of current industrial products are modular, masscustomizable systems, based on standard components like bearings, spindles, belts, pumps or gearboxes. These mature standard components are subject to periodical design improvements (new releases or generations of a basic product/component type). This design of improved product generations uses only isolated and unsystematic feedback from customers, retailers or service partners, which mainly refer to warranty cases, complaints or product recalls. As product use information is not exploited systematically, new product generations are still suboptimal or over-engineered. Product use feedback for the design of follower product generations could be either subjective or objective. Some marketing-driven approaches (i.e. customer surveys, Kano method or Quality Function Deployment) facilitate a systematic acquisition and analysis of retrospective subjective customer data [1]. However, objective field data (i.e. sensor, operation or service data) is hardly available, collected or used [2]. Driven by progresses and a price degression of micro product embedded sensors, an increasing number of companies use product field data for condition monitoring solutions to facilitate preventive maintenance of critical parts [2]. Some research approaches addressed additional uses of this data for optimizing product operation planning as well as for extending product lifetime and for improving the ecological impact of the product use [3–5]. The solution proposed in this paper considers the exploitation of a large number of individual, similar product use information of the previous product generation n for the design of the follower product generation n + 1 (Fig. 1). The new methodical framework has been implemented as a Product Use Information (PUI)
§
Submitted by F.-L. Krause (1), Berlin, Germany. * Corresponding author.
0007-8506/$ – see front matter ß 2011 CIRP. doi:10.1016/j.cirp.2011.03.103
Feedback Design Assistant (PUI FDA) supporting the product designers in various decisions regarding the development of the follower product generation. 2. Methodical framework The methodical framework describes the considered PUI as well as the different components of the PUI Feedback Design Assistant (PUI FDA): 2.1. Considered product use information The presented PUI FDA Solution considers the following designrelevant information (Fig. 1): product instance use information, like use incidents (i.e. faults, breakdowns, cracks, leaks), operation parameters (i.e. operation duration and cycles, rotation speed, temperature, vibrations) or resource consumption (i.e. energy, material) product instance workspace information, like parent assembly or neighbor influencing parts product instance operation environment information (i.e. temperature, noise, humidity, vibration) product service data (i.e. repair, maintenance, overhaul events, replacement of parts) product user/operator data (i.e. personnel data, qualification, workload) 2.2. Architecture of the PUI Feedback Design Assistant The architecture of the PUI FDA Solution is inspired by the architecture of a Data Warehouse System and considers four layers (Fig. 2): Operative Information Layer Data Extraction and Filtering Layer
[()TD$FIG]
M. Abramovici, A. Lindner / CIRP Annals - Manufacturing Technology 60 (2011) 211–214
212
operator, service or customer databases. Some of these databases are part of operative transaction-oriented commercial software systems (i.e. ERP or CRM databases), others are individual, company-customized databases. 2.4. Data extraction and filtering layer On this layer of the PUI FDA relevant information to the product development is extracted from the operative databases of the operative information layer. This information is filtered and cleansed of semantic and syntactic defects and finally stored within the PUI FDA. The data extraction is performed by an interface between the PUI FDA and the considered external source databases either periodically (e.g. daily, weekly), driven by the operation time (e.g. every 100 h of operation) or by incidences (e.g. repairs, breakdowns). 2.5. Data harmonization/aggregation layer
Fig. 1. PUI Feedback Design Assistant as an interface between product designer and customers/users.
[()TD$FIG]
Fig. 2. Architecture of the PUI Feedback Design Assistant.
Data Harmonization/Aggregation Layer Data Analysis/Diagnosis Layer The access of the product designer to the information and functions provided by the PUI FDA is supported by an appropriate user interface. In addition to the previously mentioned layers, an administration and customization environment as well as an interface to external operative product-related systems, like PLM, ERP or CRM systems, have been considered. All relevant native documents and aggregated models generated by the PUI FDA are stored in a central data archive (data vault) linked with and controlled by the central assistant database. The PUI FDA is a Decision Support System using a redundant, offload database for analysis and diagnostic purposes. The following sections describe the main solution layers in more detail: 2.3. Operative information layer This external layer considers the heterogeneous operative source databases (containing the information described in Section 2.1), like condition monitoring, product operation, environmental,
The data harmonization and aggregation layer is the kernel of the PUI FDA, where the data extracted from the different heterogeneous databases is transformed to match the PUI FDA data and knowledge models. The first task of this layer is the harmonization of the heterogeneous data, such as attribute names, domains and attributes values of the extracted product instance data, according to an integrated data model developed for the PUI FDA. The second task of the data harmonization and aggregation layer is to merge the harmonized product instance data to product type data and to enrich it by additionally calculated metrics or performance indicators. The core of this layer and thus of the PUI FDA is an integrated product data model. The backbone of this data model is the structure of the considered product type (i.e. a spindle unit). For the representation of product type and instance structures, generic product models described by the STEP standard have been extended with product instance data as well as with product use specific objects (i.e. service event, product operator) and attributes (i.e. operation parameters, environmental parameters). The UML class diagram of this data model has been described in detail in [6]. It constitutes the central source of information search, distribution and for common analysis tasks. For the representation and analysis of the product fault diagnosis, additional knowledge-based models for product instances and product types are required. The data for these models is extracted from the described integrated product data model. For the representation of fault diagnosis models, various knowledge representation approaches and knowledge discovery algorithms (i.e. rule-based, tree-oriented, Artificial Neural and Bayesian Networks) have been evaluated. This evaluation is described in [7]. Due to their ability to represent uncertain, nondeterministic information and to aggregate individual information to knowledge, Bayesian Networks have been chosen as the best representation method for the developed solution. Bayesian Networks allow a good visualization and interpretation of causal relationships between faults and their potential influence factors and they offer a variety of useful knowledge discovery algorithms and supporting tools [8]. Bayesian Networks (BN) are acyclic graphs, modeling, i.e. faults and related influence factors as nodes and their dependencies as directed edges. Each node represents a variable and has an associated conditional probability table describing a priori the probabilistic distribution of different variable values. The probabilistic distribution of the target node variable values (i.e. the probability of a fault occurrence) could be calculated from the probabilistic distribution of the parent nodes variable values (i.e. influence factors variables). For the generation of product instance fault diagnosis models (individual BNs) and for the aggregation of individual diagnosis models (aggregated BN) the machine-learning algorithms LAGD Hill Climbing and LinOP have been chosen [9]. An example of a BN used in the validation of the PUI FDA is described in Section 4.
[()TD$FIG]
M. Abramovici, A. Lindner / CIRP Annals - Manufacturing Technology 60 (2011) 211–214
213
2.6. Data analysis/diagnosis layer This PUI FDA Layer includes the main traditional data analysis methods using the aggregated PUI data model and the knowledgebased diagnosis functions based on the aggregated knowledge diagnosis models. Main data analysis methods chosen for the PUI FDA include data searching and retrieval, compression and statistical distribution of different data values, as well as result visualization and report generation. Typical data diagnosis functions supported by knowledge discovery algorithms within the PUI FDA are: discovery of product use profiles, automatic clustering, segmentation or classification of product use patterns, discovery of associations between different objects and attribute variables. In addition, the PUI FDA allows a simulation of the influence of input variables on the state of another component (what-if analysis, behavior prediction). For the fulfillment of these tasks, different machine-learning algorithms for Bayesian networks have been adapted for the use in the frame of this solution [9]. 2.7. User interface The user interface (UI) of the PUI FDA provides all information as well as analysis, diagnosis and simulation functions required by the product developer for the improvement of an existing product generation. The use of these functions is fully integrated in the existing working environment of the product designer (PLM- and CAD-systems). The rough layout of this UI is described in Section 4. 3. Prototypical implementation After a detailed evaluation of various implementation approaches and basic software systems, a software prototype of the described PUI FDA has been realized by extending and customizing a commercial PDM system (Teamcenter Engineering by Siemens, based on the relational database system Oracle) and integrating it with an open source knowledge representation and analysis environment for Bayesian Networks (WEKA) developed by the University of Waikato, New Zealand. 4. Validation and use of the PUI Feedback Design Assistant
[()TD$FIG]
The software prototype has been validated by using rotary spindle units within Wire Electrical Discharge Grinding (WEDG) machines (Fig. 3). The rotary spindle units are part of an availability-oriented business model, in which the product service system provider guarantees a 99% availability of the rotary spindle. The considered rotary spindle unit consists of an electric engine that drives a shaft with a drive spindle via a drive belt [10]. This rotary spindle has already been built into thousands of similar WEDG machines. During the WEDG machine use, product instance information is monitored for the sake of preventive maintenance, required by the high availability guaranteed by the business model. Product operation parameters (e.g. spindle rotation speed, ambient temperature, operation time) are monitored using
Fig. 3. Rotary spindle unit of a Wire Electrical Discharge Grinding (WEDG) machine.
Fig. 4. User interface of the PUI Feedback Design Assistant and its use for improving product components.
embedded micro sensors and product service information about faults or unexpected use incidents (e.g. crack of the drive belt) is taken by using service reports. To improve the rotary spindle unit of the follower product generation, product designers use the PUI FDA to analyze the aggregated product data of the previous product generations. The backbone of the PUI FDA is the product structure stored in the PLMsystem with associated CAD models, attributes and PUI (see center of Fig. 4). By using the statistical analysis functions provided by the PUI FDA the product designer can analyze, i.e. the breakdowns and fault distributions of each module of the previous product generation (see upper side of Fig. 4). In the validation example the drive belt has been identified as the most critical part within the rotary spindle unit. Therefore the product designer has to focus on the design of the improved drive belt. The next step of the designer is to identify the influence factors and their impact on the drive belt crack. For this task the product designer starts the knowledge-based diagnosis module of the PUI FDA associated with the drive belt component within the whole product structure (see lower side of Fig. 4). This module analyses the PUI of the previous product generations by using knowledge discovery algorithms (see Section 2.5), identifies the main influence factors on a fault (incident) and generates a BN, which calculates and visualizes the impact. In the considered validation example the identified influence factors on the drive belt crack were: the spindle rotation speed, the spindle running time, the ambient temperature and the time after last maintenance (see lower side of Fig. 4). These influence
214
M. Abramovici, A. Lindner / CIRP Annals - Manufacturing Technology 60 (2011) 211–214
factors (variables) are characterized by different variable values and distributions. The distributions of the variables values within the PUI FDA have been determined from the empirical analysis of PUI of previous product generations (Fig. 4). The quantitative algorithms of the BN can calculate the impact of the influence factors on the probability of a drive belt crack (variable values ‘‘true’’ or ‘‘false’’). The generated BN can be used by the product designer not only for diagnosis but also for simulation purposes. The product designer can assume different variable value distributions of the influence factors and investigate their impact on the drive belt crack probability (‘‘What-If’’ analysis). By using the analysis, diagnosis and simulation functions provided by the PUI FDA the product designer can better understand the reasons for operation faults, breakdowns and incidents and improve the design of the next product generation effectively. The use of the assistant prototype for the improvement of the rotary spindle unit has shown the feasibility of the developed methods and tools. The main problem in validating the software assistant has been the lack of a large amount of real product instance use data sets, which had to be generated by multiplying one set of existing data by changing attribute values using random algorithms. Another weakness of the validation has been the limitation of analysis capabilities of the PUI FDA prototype. 5. Conclusion and outlook The realized PUI Feedback Design Assistant showed the high potential of using product use information and knowledge for improving future product generations. The proliferation of low-cost micro sensors as well as future advances in information and communication technology will foster a broader monitoring of PUI. A promising example for an advanced product-embedded operation data acquisition device is the Virtual Lifecycle Unit (VLCU) [11]. In addition to these expected developments, the shift of manufacturing companies from selling products to offering customer-specific product service systems will expand the responsibility of the producers to the whole product life and will facilitate easier access to product operation and use data [12] which could be used not only for product operation optimization but also as feedback for the design of follower product generation.
Future research work could improve the PUI Feedback Design Assistant by extending the monitored product data types, by enhancing data analysis functions, by incorporating new diagnosis models and algorithms and by considering better result visualization methods. Acknowledgements We express our sincere thanks to the German Research Foundation (DFG) for financing this research within the project ‘‘Product Lifecycle Management Extension through KnowledgeBased Product Use Information Feedback into Product Development’’.
References [1] Kotler P, Keller K (2009) Marketing Management. 13th ed. Pearson. ISBN: 9780-136r-r00998-6. [2] Teti R, Jemeniak K, O’Donnell G, Dornfeld D (2010) Advanced Monitoring of Modern Operation. Annals of the CIRP 59:717–739. [3] Krause F-L, Jungk H, Kind C (2007) Product Lifecycle Planning and Tracking. in Seliger G, (Ed.) Sustainability in Manufacturing. Springer, pp. 183–192. [4] Van Houten F, Kimura F (2000) The Virtual Maintenance System: A Computerbased Support Tool for Robust Design, Product Monitoring, Fault Diagnosis and Maintenance Planning. Annals of the CIRP 49(1):91–94. [5] Kara S, Mazhar M, Kaebernik H, Ahmed A (2005) Determining the Reuse Potential of Components based on Lifecycle Data. Annals of the CIRP 55:1–20. [6] Abramovici M, et al, (2009) Metadata Reference Model for IPS2 Lifecycle Management. Proceedings of the CIRP IPS2 Conference, Cranfield, 268–272. [7] Abramovici M, et al, (2009) Knowledge-based Feedback of Product Use Information into Product Development. Proceedings of the 17th International Conference on Engineering Design (ICED’09), vol. 8, Design Information and Knowledge, Stanford, USA, August, 227–238. [8] Jensen F, Graven-Nielsen T (2007) Bayesian Networks and Decision Graphs. 2nd ed. Springer-Verlag978-0-387-68281-5. [9] Abramovici M, et al, (2008) Competing Fusion for Bayesian Applications. 12th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2008), Malaga, Spain, June, 378–385. [10] Uhlmann E, Langmack M, Geisert C, Stelzer C (2008) Herstellung von Rotationsbauteilen fu¨r die Mikroproduktion, wt Werkstattstechnik online, Jahrgang 2008 (11-12/2008). Springer-VDI-Verlag GmbH & Co. KG, Du¨sseldorf. pp. 950– 954. [11] Gegusch R, Gestrich K, Seliger G (2010) Software Agents for Automated Knowledge Generation in IPS2. Proceedings of the 2nd CIRP IPS2 Conference, Linko¨ping, 387–391. [12] Meier H, Roy R, Seliger G (2010) Industrial Product-Service-Systems—IPS2. Annals of the CIRP 59:607–627.
Contents lists available at ScienceDirect
CIRP Annals - Manufacturing Technology jou rnal homep age : ht t p: // ees .e lse vi er. com/ci rp/ def a ult . asp
Providing product use knowledge for the design of improved product generations§ M. Abramovici *, A. Lindner Information Technology in Mechanical Engineering (ITM), Ruhr University of Bochum, Germany Submitted by F.-L. Krause (1), Berlin, Germany.
A R T I C L E I N F O
A B S T R A C T
Keywords: Feedback Product development Knowledge based system
Current design of improved product generations does not exploit use information from previous products systematically. The emerging shift of manufacturing companies from selling products to providing product service systems, the miniaturization of product-embedded sensors, as well as advances in information technology facilitate a product providers access to operation information of current products, which can be used to improve the development and the quality of follower product generations. The paper presents a framework for the acquisition, aggregation and analysis of product use information as well as for the generation and provision of knowledge for the development of improved product generations. The described approach employs knowledge discovery methods like Bayesian Networks and is supported by an IT prototype of a design assistant system. This prototype has been validated in a use case considering the improvement of a rotary spindle for micro machining. ß 2011 CIRP.
1. Introduction The majority of current industrial products are modular, masscustomizable systems, based on standard components like bearings, spindles, belts, pumps or gearboxes. These mature standard components are subject to periodical design improvements (new releases or generations of a basic product/component type). This design of improved product generations uses only isolated and unsystematic feedback from customers, retailers or service partners, which mainly refer to warranty cases, complaints or product recalls. As product use information is not exploited systematically, new product generations are still suboptimal or over-engineered. Product use feedback for the design of follower product generations could be either subjective or objective. Some marketing-driven approaches (i.e. customer surveys, Kano method or Quality Function Deployment) facilitate a systematic acquisition and analysis of retrospective subjective customer data [1]. However, objective field data (i.e. sensor, operation or service data) is hardly available, collected or used [2]. Driven by progresses and a price degression of micro product embedded sensors, an increasing number of companies use product field data for condition monitoring solutions to facilitate preventive maintenance of critical parts [2]. Some research approaches addressed additional uses of this data for optimizing product operation planning as well as for extending product lifetime and for improving the ecological impact of the product use [3–5]. The solution proposed in this paper considers the exploitation of a large number of individual, similar product use information of the previous product generation n for the design of the follower product generation n + 1 (Fig. 1). The new methodical framework has been implemented as a Product Use Information (PUI)
§
Submitted by F.-L. Krause (1), Berlin, Germany. * Corresponding author.
0007-8506/$ – see front matter ß 2011 CIRP. doi:10.1016/j.cirp.2011.03.103
Feedback Design Assistant (PUI FDA) supporting the product designers in various decisions regarding the development of the follower product generation. 2. Methodical framework The methodical framework describes the considered PUI as well as the different components of the PUI Feedback Design Assistant (PUI FDA): 2.1. Considered product use information The presented PUI FDA Solution considers the following designrelevant information (Fig. 1): product instance use information, like use incidents (i.e. faults, breakdowns, cracks, leaks), operation parameters (i.e. operation duration and cycles, rotation speed, temperature, vibrations) or resource consumption (i.e. energy, material) product instance workspace information, like parent assembly or neighbor influencing parts product instance operation environment information (i.e. temperature, noise, humidity, vibration) product service data (i.e. repair, maintenance, overhaul events, replacement of parts) product user/operator data (i.e. personnel data, qualification, workload) 2.2. Architecture of the PUI Feedback Design Assistant The architecture of the PUI FDA Solution is inspired by the architecture of a Data Warehouse System and considers four layers (Fig. 2): Operative Information Layer Data Extraction and Filtering Layer
[()TD$FIG]
M. Abramovici, A. Lindner / CIRP Annals - Manufacturing Technology 60 (2011) 211–214
212
operator, service or customer databases. Some of these databases are part of operative transaction-oriented commercial software systems (i.e. ERP or CRM databases), others are individual, company-customized databases. 2.4. Data extraction and filtering layer On this layer of the PUI FDA relevant information to the product development is extracted from the operative databases of the operative information layer. This information is filtered and cleansed of semantic and syntactic defects and finally stored within the PUI FDA. The data extraction is performed by an interface between the PUI FDA and the considered external source databases either periodically (e.g. daily, weekly), driven by the operation time (e.g. every 100 h of operation) or by incidences (e.g. repairs, breakdowns). 2.5. Data harmonization/aggregation layer
Fig. 1. PUI Feedback Design Assistant as an interface between product designer and customers/users.
[()TD$FIG]
Fig. 2. Architecture of the PUI Feedback Design Assistant.
Data Harmonization/Aggregation Layer Data Analysis/Diagnosis Layer The access of the product designer to the information and functions provided by the PUI FDA is supported by an appropriate user interface. In addition to the previously mentioned layers, an administration and customization environment as well as an interface to external operative product-related systems, like PLM, ERP or CRM systems, have been considered. All relevant native documents and aggregated models generated by the PUI FDA are stored in a central data archive (data vault) linked with and controlled by the central assistant database. The PUI FDA is a Decision Support System using a redundant, offload database for analysis and diagnostic purposes. The following sections describe the main solution layers in more detail: 2.3. Operative information layer This external layer considers the heterogeneous operative source databases (containing the information described in Section 2.1), like condition monitoring, product operation, environmental,
The data harmonization and aggregation layer is the kernel of the PUI FDA, where the data extracted from the different heterogeneous databases is transformed to match the PUI FDA data and knowledge models. The first task of this layer is the harmonization of the heterogeneous data, such as attribute names, domains and attributes values of the extracted product instance data, according to an integrated data model developed for the PUI FDA. The second task of the data harmonization and aggregation layer is to merge the harmonized product instance data to product type data and to enrich it by additionally calculated metrics or performance indicators. The core of this layer and thus of the PUI FDA is an integrated product data model. The backbone of this data model is the structure of the considered product type (i.e. a spindle unit). For the representation of product type and instance structures, generic product models described by the STEP standard have been extended with product instance data as well as with product use specific objects (i.e. service event, product operator) and attributes (i.e. operation parameters, environmental parameters). The UML class diagram of this data model has been described in detail in [6]. It constitutes the central source of information search, distribution and for common analysis tasks. For the representation and analysis of the product fault diagnosis, additional knowledge-based models for product instances and product types are required. The data for these models is extracted from the described integrated product data model. For the representation of fault diagnosis models, various knowledge representation approaches and knowledge discovery algorithms (i.e. rule-based, tree-oriented, Artificial Neural and Bayesian Networks) have been evaluated. This evaluation is described in [7]. Due to their ability to represent uncertain, nondeterministic information and to aggregate individual information to knowledge, Bayesian Networks have been chosen as the best representation method for the developed solution. Bayesian Networks allow a good visualization and interpretation of causal relationships between faults and their potential influence factors and they offer a variety of useful knowledge discovery algorithms and supporting tools [8]. Bayesian Networks (BN) are acyclic graphs, modeling, i.e. faults and related influence factors as nodes and their dependencies as directed edges. Each node represents a variable and has an associated conditional probability table describing a priori the probabilistic distribution of different variable values. The probabilistic distribution of the target node variable values (i.e. the probability of a fault occurrence) could be calculated from the probabilistic distribution of the parent nodes variable values (i.e. influence factors variables). For the generation of product instance fault diagnosis models (individual BNs) and for the aggregation of individual diagnosis models (aggregated BN) the machine-learning algorithms LAGD Hill Climbing and LinOP have been chosen [9]. An example of a BN used in the validation of the PUI FDA is described in Section 4.
[()TD$FIG]
M. Abramovici, A. Lindner / CIRP Annals - Manufacturing Technology 60 (2011) 211–214
213
2.6. Data analysis/diagnosis layer This PUI FDA Layer includes the main traditional data analysis methods using the aggregated PUI data model and the knowledgebased diagnosis functions based on the aggregated knowledge diagnosis models. Main data analysis methods chosen for the PUI FDA include data searching and retrieval, compression and statistical distribution of different data values, as well as result visualization and report generation. Typical data diagnosis functions supported by knowledge discovery algorithms within the PUI FDA are: discovery of product use profiles, automatic clustering, segmentation or classification of product use patterns, discovery of associations between different objects and attribute variables. In addition, the PUI FDA allows a simulation of the influence of input variables on the state of another component (what-if analysis, behavior prediction). For the fulfillment of these tasks, different machine-learning algorithms for Bayesian networks have been adapted for the use in the frame of this solution [9]. 2.7. User interface The user interface (UI) of the PUI FDA provides all information as well as analysis, diagnosis and simulation functions required by the product developer for the improvement of an existing product generation. The use of these functions is fully integrated in the existing working environment of the product designer (PLM- and CAD-systems). The rough layout of this UI is described in Section 4. 3. Prototypical implementation After a detailed evaluation of various implementation approaches and basic software systems, a software prototype of the described PUI FDA has been realized by extending and customizing a commercial PDM system (Teamcenter Engineering by Siemens, based on the relational database system Oracle) and integrating it with an open source knowledge representation and analysis environment for Bayesian Networks (WEKA) developed by the University of Waikato, New Zealand. 4. Validation and use of the PUI Feedback Design Assistant
[()TD$FIG]
The software prototype has been validated by using rotary spindle units within Wire Electrical Discharge Grinding (WEDG) machines (Fig. 3). The rotary spindle units are part of an availability-oriented business model, in which the product service system provider guarantees a 99% availability of the rotary spindle. The considered rotary spindle unit consists of an electric engine that drives a shaft with a drive spindle via a drive belt [10]. This rotary spindle has already been built into thousands of similar WEDG machines. During the WEDG machine use, product instance information is monitored for the sake of preventive maintenance, required by the high availability guaranteed by the business model. Product operation parameters (e.g. spindle rotation speed, ambient temperature, operation time) are monitored using
Fig. 3. Rotary spindle unit of a Wire Electrical Discharge Grinding (WEDG) machine.
Fig. 4. User interface of the PUI Feedback Design Assistant and its use for improving product components.
embedded micro sensors and product service information about faults or unexpected use incidents (e.g. crack of the drive belt) is taken by using service reports. To improve the rotary spindle unit of the follower product generation, product designers use the PUI FDA to analyze the aggregated product data of the previous product generations. The backbone of the PUI FDA is the product structure stored in the PLMsystem with associated CAD models, attributes and PUI (see center of Fig. 4). By using the statistical analysis functions provided by the PUI FDA the product designer can analyze, i.e. the breakdowns and fault distributions of each module of the previous product generation (see upper side of Fig. 4). In the validation example the drive belt has been identified as the most critical part within the rotary spindle unit. Therefore the product designer has to focus on the design of the improved drive belt. The next step of the designer is to identify the influence factors and their impact on the drive belt crack. For this task the product designer starts the knowledge-based diagnosis module of the PUI FDA associated with the drive belt component within the whole product structure (see lower side of Fig. 4). This module analyses the PUI of the previous product generations by using knowledge discovery algorithms (see Section 2.5), identifies the main influence factors on a fault (incident) and generates a BN, which calculates and visualizes the impact. In the considered validation example the identified influence factors on the drive belt crack were: the spindle rotation speed, the spindle running time, the ambient temperature and the time after last maintenance (see lower side of Fig. 4). These influence
214
M. Abramovici, A. Lindner / CIRP Annals - Manufacturing Technology 60 (2011) 211–214
factors (variables) are characterized by different variable values and distributions. The distributions of the variables values within the PUI FDA have been determined from the empirical analysis of PUI of previous product generations (Fig. 4). The quantitative algorithms of the BN can calculate the impact of the influence factors on the probability of a drive belt crack (variable values ‘‘true’’ or ‘‘false’’). The generated BN can be used by the product designer not only for diagnosis but also for simulation purposes. The product designer can assume different variable value distributions of the influence factors and investigate their impact on the drive belt crack probability (‘‘What-If’’ analysis). By using the analysis, diagnosis and simulation functions provided by the PUI FDA the product designer can better understand the reasons for operation faults, breakdowns and incidents and improve the design of the next product generation effectively. The use of the assistant prototype for the improvement of the rotary spindle unit has shown the feasibility of the developed methods and tools. The main problem in validating the software assistant has been the lack of a large amount of real product instance use data sets, which had to be generated by multiplying one set of existing data by changing attribute values using random algorithms. Another weakness of the validation has been the limitation of analysis capabilities of the PUI FDA prototype. 5. Conclusion and outlook The realized PUI Feedback Design Assistant showed the high potential of using product use information and knowledge for improving future product generations. The proliferation of low-cost micro sensors as well as future advances in information and communication technology will foster a broader monitoring of PUI. A promising example for an advanced product-embedded operation data acquisition device is the Virtual Lifecycle Unit (VLCU) [11]. In addition to these expected developments, the shift of manufacturing companies from selling products to offering customer-specific product service systems will expand the responsibility of the producers to the whole product life and will facilitate easier access to product operation and use data [12] which could be used not only for product operation optimization but also as feedback for the design of follower product generation.
Future research work could improve the PUI Feedback Design Assistant by extending the monitored product data types, by enhancing data analysis functions, by incorporating new diagnosis models and algorithms and by considering better result visualization methods. Acknowledgements We express our sincere thanks to the German Research Foundation (DFG) for financing this research within the project ‘‘Product Lifecycle Management Extension through KnowledgeBased Product Use Information Feedback into Product Development’’.
References [1] Kotler P, Keller K (2009) Marketing Management. 13th ed. Pearson. ISBN: 9780-136r-r00998-6. [2] Teti R, Jemeniak K, O’Donnell G, Dornfeld D (2010) Advanced Monitoring of Modern Operation. Annals of the CIRP 59:717–739. [3] Krause F-L, Jungk H, Kind C (2007) Product Lifecycle Planning and Tracking. in Seliger G, (Ed.) Sustainability in Manufacturing. Springer, pp. 183–192. [4] Van Houten F, Kimura F (2000) The Virtual Maintenance System: A Computerbased Support Tool for Robust Design, Product Monitoring, Fault Diagnosis and Maintenance Planning. Annals of the CIRP 49(1):91–94. [5] Kara S, Mazhar M, Kaebernik H, Ahmed A (2005) Determining the Reuse Potential of Components based on Lifecycle Data. Annals of the CIRP 55:1–20. [6] Abramovici M, et al, (2009) Metadata Reference Model for IPS2 Lifecycle Management. Proceedings of the CIRP IPS2 Conference, Cranfield, 268–272. [7] Abramovici M, et al, (2009) Knowledge-based Feedback of Product Use Information into Product Development. Proceedings of the 17th International Conference on Engineering Design (ICED’09), vol. 8, Design Information and Knowledge, Stanford, USA, August, 227–238. [8] Jensen F, Graven-Nielsen T (2007) Bayesian Networks and Decision Graphs. 2nd ed. Springer-Verlag978-0-387-68281-5. [9] Abramovici M, et al, (2008) Competing Fusion for Bayesian Applications. 12th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2008), Malaga, Spain, June, 378–385. [10] Uhlmann E, Langmack M, Geisert C, Stelzer C (2008) Herstellung von Rotationsbauteilen fu¨r die Mikroproduktion, wt Werkstattstechnik online, Jahrgang 2008 (11-12/2008). Springer-VDI-Verlag GmbH & Co. KG, Du¨sseldorf. pp. 950– 954. [11] Gegusch R, Gestrich K, Seliger G (2010) Software Agents for Automated Knowledge Generation in IPS2. Proceedings of the 2nd CIRP IPS2 Conference, Linko¨ping, 387–391. [12] Meier H, Roy R, Seliger G (2010) Industrial Product-Service-Systems—IPS2. Annals of the CIRP 59:607–627.