- Email: [email protected]
Machine tool capability profile for intelligent process planning
CIRP Annals - Manufacturing Technology 58 (2009) 421–424
Contents lists available at ScienceDirect
CIRP Annals - Manufacturing Technology journal homepage: http://ees.elsevier.com/cirp/default.asp
Machine tool capability profile for intelligent process planning S.T. Newman *, A. Nassehi Mechanical Engineering Department, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom Submitted by A.N. Bramley (1), Bath, United Kingdom.
A R T I C L E I N F O
A B S T R A C T
Keywords: CAPP CAM Resource modelling
An optimized metal cutting process plan can only be developed with an accurate capability profile of a machine tool. Based on the information within this profile, a manufacturing decision making process can ascertain the time and production cost of parts. In this paper, a standardized methodology for modelling manufacturing resources is realized to enable accurate representation of actual resources and custom constraints. An example case study demonstrates the application of the machine tool capability profile through the selection of available cutting tools rather than using nominal cutting tools. ß 2009 CIRP.
1. Introduction
2. Resource for CAPP
Process planning for metal cutting is among the most knowledge-intensive activities in manufacturing. In this activity the product information is mapped on to the available information for the various existing manufacturing resources to determine a plan of action to convert the raw material into the final product [1]. This activity is currently supported by information technology in the form of computer aided process planning [2]. A considerable amount of research has been carried out to introduce more intelligence into process planning [3]. Process planning relies on manufacturing resource models to supply the necessary information regarding the physical devices used for manufacturing [4]. In the state of the art, the nominal model of manufacturing resources is used for process planning. The information contained within this model, neither reflects the actual state of the resources, nor does it reflect any policy constraints that have been implemented in the manufacturing enterprise. This results in generation of process plans that might not be entirely effective in the context of the available resources and policies. In this paper a STEP-compliant methodology for representing resources is extended to allow the representation of machine tool capability profiles in a time-based manner while enabling users to define custom rules and policies. The extended data model can support the information requirements for profiling techniques such as prediction and online monitoring of resources as chosen by the user. The advantages of the methodology and the augmented model are then presented through the use of a case study. A discussion on the possible implementations of the methodology is then presented together with the conclusions derived from the work.
Process planning in metal cutting is the consolidation of activities that seek to define the necessary steps to change the shape of the raw stock to the desired product [1]. The activities range from the selection of technologies and manufacturability analysis at the high level [5] to sequencing operations and generating numerical control codes at the low level. At all levels, process planning relies on the availability of production resource information [6]. Human planners utilize their knowledge of technological capability of various types of machines while computer aided systems rely on resource models. CAM systems require users to select a machine type before generating tool paths and post-processors rely on internal models of machines to achieve their purpose. Fig. 1 provides an IDEF-0 representation of process planning. Vichare et al. [7] presented a review of resource in the domain of metal cutting computer numerically controlled (CNC) machines and concluded that a universally applicable representation of such devices can only be realized through the development of a standardized manufacturing resource model. Efforts for developing this standardized model as part of the ISO14649 (STEP-NC) [8] standard are well under way at the time of writing of this paper. This representation provides an accurate representation of machine tool functionality. The functionality is determined through modelling the mechanical elements building the machine tool together with the kinematic relationships between these elements. Combining this information with the placement of cutting tools and the workpiece on individual machine elements, allows the capability of the machine tool in achieving the controlled motion of the tool with respect to the workpiece to be ascertained. Fig. 2 shows an EXPRESS-G [9] representation of the high-level elements from the proposed standardized model. Also by solving the inverse kinematic chain from the workpiece to the tool-tip it is possible to determine the position
* Corresponding author. 0007-8506/$ – see front matter ß 2009 CIRP. doi:10.1016/j.cirp.2009.03.031
422
S.T. Newman, A. Nassehi / CIRP Annals - Manufacturing Technology 58 (2009) 421–424
Fig. 1. IDEF-0 diagram for the process planning activity, adapted from [1].
of the individual axes on the machine tool to achieve a specific tool position with a pre-determined orientation in relation to the workpiece. While the model can provide the necessary information for the process planning activity to proceed, the information relates to the nominal state of the manufacturing resources. 3. Capturing capability profiles of manufacturing resources Any manufacturing resource that is used continuously changes over time. Using nominal models of manufacturing resources when planning the process of production, results in the generation of non-optimal plans. This is illustrated as an IDEF-0 diagram in Fig. 3. Often in industry, specific constraints are imposed on manufacturing resources by management. For example, a specific CNC machine might be allocated to a specific part family and not be used for other parts, notwithstanding the fact that the machine has the capability to produce other parts. These production policies are not always considered when the process plan for a component is being generated. Due to these considerations, automatically generated process plans, often require further input from the engineers on the shop floor to ensure their appropriateness to the actual status of the resources and production policies as dictated by the manufacturing enterprise. In order to enable the process planning system to determine the most effective plan with respect to the actual available resources, it is necessary to provide resource information that reflects the status of the physical devices at the time that they will be utilized for manufacturing the part. This time-sensitive image of the resource, called by the authors a capability profile, is a representation of the capabilities that a specific machine tool will be able to provide in a specific time on a specific product.
Fig. 3. IDEF-0 representation of the state of the art in process planning.
An IDEF-0 diagram is used in Fig. 4 to illustrate how the existence of the capability profile affects the process planning and production activities. The capability profile can be generated by many techniques including prediction and online monitoring of resources and is subject to policies set by the user. Regardless of the generation technique, it is imperative that the profile is associated to the representation of the resource within the manufacturing information. The CAPP system can then utilize this resource capability profile instead of the nominal resource information to create a capability adjusted process plan. The quantity, intensity and effectiveness of the adjustments depend on the adaptability of the process planning system. A CAPP system capable of accessing the integrated body of manufacturing knowledge existing in the enterprise such as that suggested in the universal manufacturing platform [10] would provide the highest flexibility. This is due to the fact that in such an environment the process planning system would have access, not only to the process data, but also an integrated representation of the entire body of the manufacturing information including resource and product data. 4. Process planning supported by capability profiles The manufacturing resource capability profile contains all items of information that a nominal resource model contains. Individual items of information are then replaced by the input gathered from the actual status of manufacturing devices on the shop floor or predictive models. Fig. 5 illustrates an example implementation of a computer aided resource profiling system.
Fig. 2. EXPRESS-G diagram of the manufacturing resource model.
S.T. Newman, A. Nassehi / CIRP Annals - Manufacturing Technology 58 (2009) 421–424
423
Fig. 4. IDEF-0 representation of process planning with manufacturing capability profiles.
In this system the available tools are matched against a tool library to create a tooling profile for the machine tool at the time of manufacturing. By obtaining the tool wear information from sensors on the shop floor, or utilising a tool wear estimation algorithm [11,12], the tools that are too worn for use in manufacturing are deleted from the profile. Similarly the available fixtures are mapped on to a nominal library of fixtures to determine the fixturing capability available at the time of manufacturing. The accuracy of axes in a CNC machine tool changes over time and compensating against geometric errors is an important aspect of precision machining [13]. In addition, for certain types of jobs, machines that have an axis accuracy lower than a pre-defined threshold are not appropriate for use. In the computer aided resource profiling system the nominal axis accuracy information of resources are combined with the axis accuracy results from geometric tests on the CNC machine and extrapolated using a predictive model to determine the axis accuracy profile at the time of manufacture. Furthermore, the production policies set in the manufacturing enterprise such as the assignment of specific part families to specific machines or placement of limitations on the use of certain features on the CNC machine tool due to external considerations are taken into account. These policies are mapped on to the mechanical and technological capabilities provided in the nominal resource information model to construct a technical capability profile for the resource at the time of machining.
Fig. 5. An example implementation of a computer aided resource profiling system.
5. Case study A prototype implementation of the computer aided resource profiling system has been used to demonstrate the effects of tool wear on process planning when a machine tool capability profile is used. The part illustrated in Fig. 6 is adapted from ISO14649 part 11 [14] and used to generate the process plan. An excerpt of the nominal STEP-NC [8] process plan for this component is shown in Fig. 7. As indicated in lines #30 and #39 of the process plan, a 20 mm tapered endmill is the nominal cutting tool for manufacturing the rectangular pocket as, ceteris paribus, the quickest way to produce the pocket is using one that has the same radius as the planar radius on the rectangular pocket. In order to demonstrate resource capability profiling, the tool wear rate has been predicted according to the model in [15]. The wear model for the 20 mm tapered endmill is illustrated in Fig. 8. Within the prototype, a threshold is specified to consider tools with more than a certain amount of wear as unusable. In a production environment online tool wear monitoring could supplement or replace this prediction.
Fig. 6. Example production part [adapted from 14].
424
S.T. Newman, A. Nassehi / CIRP Annals - Manufacturing Technology 58 (2009) 421–424
The machining time for the rectangular pocket is 180 s. Consequently after machining 33 components the resource capability profiling system, considers the 20 mm endmill as unusable and adjusts the process plan so that an alternative tool is utilized. The next usable cutting tool in the nominal tool library is an 18 mm endmill and thus, the program is modified to use this cutting tool. The abbreviated capability adjusted STEP-NC process plan is shown in Fig. 9. The tool change can be seen in lines #30 and #39. 6. Conclusions
Fig. 7. Excerpt from the STEP-NC process plan for the test component (for complete listing refer to [14]).
Through the use of manufacturing resource capability profiles, it is possible to optimize the generation of the process plan to develop solutions that are appropriate for the actual available hardware and software rather than the nominal values. Capability profiles are generated by combining the nominal resource models with actual values obtained from sensors on the shop floor and predictive models. Although any nominal resource model can be utilized for this purpose, the use of a standardized resource model would substantially increase the breadth of the applicability of the resource capability profiles. With capability profiles, it is also possible to implement production policies during the generation of process plans, thus saving time and effort in comparison to implementing them afterwards on the shop floor. This results in an increase in trust throughout the process planning activity and leads to the generation of verified process plans. References
Fig. 8. Tool wear diagram for the 20 mm tapered endmill.
Fig. 9. The excerpt from the adjusted STEP-NC process plan for the test component highlighting the changes.
[1] ElMaraghy HA (1993) Evolution and Future Perspectives of CAPP. Annals of the CIRP 42(2):739–751. [2] Denkena B, Shpitalni M, Kowalski P, Molcho G, Zipori Y (2007) Knowledge Management in Process Planning. Annals of the CIRP 56(1):175–180. [3] Xu XW, Wang H, Mao J, Newman ST, Kramer TR, Proctor FM, Michaloski JL (2005) STEP-compliant NC Research: The Search for Intelligent CAD/CAPP/CAM/CNC Integration. International Journal of Production Research 43(17):3703–3743. [4] Maropoulos PG, McKay KR, Bramall DG (2002) Resource-Aware Aggregate Planning for the Distributed Manufacturing Enterprise. Annals of the CIRP 51(1):363–366. [5] Molcho G, Zipori Y, Schneor R, Rosen O, Goldstein D, Shpitalni M (2008) Computer Aided Manufacturability Analysis: Closing the Knowledge Gap between the Designer and the Manufacturer. Annals of the CIRP 57(1):153–158. [6] Paris H, Brissaud D (2000) Modelling for Process Planning: The Links between Process Planning Entities. Robotics & Computer Integrated Manufacturing 16(4): 259–266. [7] Vichare P, Nassehi A, Newman ST (2007) Towards a STEP-NC Compliant Model for Representation of Machine Tools. Proceedings of 4th International CIRP Sponsored Conference on Digital Enterprise Technology (DET2007), Bath, UK, 721–723. [8] ISO14649-1, 2003, Data Model for Computerized Numerical Controllers. Part 1. Overview and fundamental principles. [9] ISO10303-1, 1994, Industrial Automation Systems and Integration-Product Data Representation and Exchange. Part 1. Overview and fundamental principles. [10] Newman ST, Nassehi A (2007) Universal Manufacturing Platform for CNC Machining. Annals of the CIRP 56(1):459–462. [11] Wang X, Wang W, Huang Y, Nguyen N, Krishnakumal K (2008) Design of Neural Network-based Estimator for Tool Wear Modelling in Hard Turning. Journal of Intelligent Manufacturing 19(4):383–396. [12] Attanasio A, Ceretti E, Rizzuti S, Umbrello D, Micari F (2008) 3D Finite Element Analysis of Tool Wear in Machining. Annals of the CIRP 57(1):61–64. [13] Schwenke H, Knapp W, Haitjema H, Weckenmann A, Schmitt R, Delbressine F (2008) Geometric Error Measurement and Compensation of Machines—An Update. Annals of the CIRP 57(2):660–675. [14] ISO14649-11, Data Model for Computerized Numerical Controllers. Part 11. Process Data for Milling, 2004. [15] Kramer BM (1986) A Comprehensive Tool Wear Model. Annals of the CIRP 35(1):67–70.
Contents lists available at ScienceDirect
CIRP Annals - Manufacturing Technology journal homepage: http://ees.elsevier.com/cirp/default.asp
Machine tool capability profile for intelligent process planning S.T. Newman *, A. Nassehi Mechanical Engineering Department, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom Submitted by A.N. Bramley (1), Bath, United Kingdom.
A R T I C L E I N F O
A B S T R A C T
Keywords: CAPP CAM Resource modelling
An optimized metal cutting process plan can only be developed with an accurate capability profile of a machine tool. Based on the information within this profile, a manufacturing decision making process can ascertain the time and production cost of parts. In this paper, a standardized methodology for modelling manufacturing resources is realized to enable accurate representation of actual resources and custom constraints. An example case study demonstrates the application of the machine tool capability profile through the selection of available cutting tools rather than using nominal cutting tools. ß 2009 CIRP.
1. Introduction
2. Resource for CAPP
Process planning for metal cutting is among the most knowledge-intensive activities in manufacturing. In this activity the product information is mapped on to the available information for the various existing manufacturing resources to determine a plan of action to convert the raw material into the final product [1]. This activity is currently supported by information technology in the form of computer aided process planning [2]. A considerable amount of research has been carried out to introduce more intelligence into process planning [3]. Process planning relies on manufacturing resource models to supply the necessary information regarding the physical devices used for manufacturing [4]. In the state of the art, the nominal model of manufacturing resources is used for process planning. The information contained within this model, neither reflects the actual state of the resources, nor does it reflect any policy constraints that have been implemented in the manufacturing enterprise. This results in generation of process plans that might not be entirely effective in the context of the available resources and policies. In this paper a STEP-compliant methodology for representing resources is extended to allow the representation of machine tool capability profiles in a time-based manner while enabling users to define custom rules and policies. The extended data model can support the information requirements for profiling techniques such as prediction and online monitoring of resources as chosen by the user. The advantages of the methodology and the augmented model are then presented through the use of a case study. A discussion on the possible implementations of the methodology is then presented together with the conclusions derived from the work.
Process planning in metal cutting is the consolidation of activities that seek to define the necessary steps to change the shape of the raw stock to the desired product [1]. The activities range from the selection of technologies and manufacturability analysis at the high level [5] to sequencing operations and generating numerical control codes at the low level. At all levels, process planning relies on the availability of production resource information [6]. Human planners utilize their knowledge of technological capability of various types of machines while computer aided systems rely on resource models. CAM systems require users to select a machine type before generating tool paths and post-processors rely on internal models of machines to achieve their purpose. Fig. 1 provides an IDEF-0 representation of process planning. Vichare et al. [7] presented a review of resource in the domain of metal cutting computer numerically controlled (CNC) machines and concluded that a universally applicable representation of such devices can only be realized through the development of a standardized manufacturing resource model. Efforts for developing this standardized model as part of the ISO14649 (STEP-NC) [8] standard are well under way at the time of writing of this paper. This representation provides an accurate representation of machine tool functionality. The functionality is determined through modelling the mechanical elements building the machine tool together with the kinematic relationships between these elements. Combining this information with the placement of cutting tools and the workpiece on individual machine elements, allows the capability of the machine tool in achieving the controlled motion of the tool with respect to the workpiece to be ascertained. Fig. 2 shows an EXPRESS-G [9] representation of the high-level elements from the proposed standardized model. Also by solving the inverse kinematic chain from the workpiece to the tool-tip it is possible to determine the position
* Corresponding author. 0007-8506/$ – see front matter ß 2009 CIRP. doi:10.1016/j.cirp.2009.03.031
422
S.T. Newman, A. Nassehi / CIRP Annals - Manufacturing Technology 58 (2009) 421–424
Fig. 1. IDEF-0 diagram for the process planning activity, adapted from [1].
of the individual axes on the machine tool to achieve a specific tool position with a pre-determined orientation in relation to the workpiece. While the model can provide the necessary information for the process planning activity to proceed, the information relates to the nominal state of the manufacturing resources. 3. Capturing capability profiles of manufacturing resources Any manufacturing resource that is used continuously changes over time. Using nominal models of manufacturing resources when planning the process of production, results in the generation of non-optimal plans. This is illustrated as an IDEF-0 diagram in Fig. 3. Often in industry, specific constraints are imposed on manufacturing resources by management. For example, a specific CNC machine might be allocated to a specific part family and not be used for other parts, notwithstanding the fact that the machine has the capability to produce other parts. These production policies are not always considered when the process plan for a component is being generated. Due to these considerations, automatically generated process plans, often require further input from the engineers on the shop floor to ensure their appropriateness to the actual status of the resources and production policies as dictated by the manufacturing enterprise. In order to enable the process planning system to determine the most effective plan with respect to the actual available resources, it is necessary to provide resource information that reflects the status of the physical devices at the time that they will be utilized for manufacturing the part. This time-sensitive image of the resource, called by the authors a capability profile, is a representation of the capabilities that a specific machine tool will be able to provide in a specific time on a specific product.
Fig. 3. IDEF-0 representation of the state of the art in process planning.
An IDEF-0 diagram is used in Fig. 4 to illustrate how the existence of the capability profile affects the process planning and production activities. The capability profile can be generated by many techniques including prediction and online monitoring of resources and is subject to policies set by the user. Regardless of the generation technique, it is imperative that the profile is associated to the representation of the resource within the manufacturing information. The CAPP system can then utilize this resource capability profile instead of the nominal resource information to create a capability adjusted process plan. The quantity, intensity and effectiveness of the adjustments depend on the adaptability of the process planning system. A CAPP system capable of accessing the integrated body of manufacturing knowledge existing in the enterprise such as that suggested in the universal manufacturing platform [10] would provide the highest flexibility. This is due to the fact that in such an environment the process planning system would have access, not only to the process data, but also an integrated representation of the entire body of the manufacturing information including resource and product data. 4. Process planning supported by capability profiles The manufacturing resource capability profile contains all items of information that a nominal resource model contains. Individual items of information are then replaced by the input gathered from the actual status of manufacturing devices on the shop floor or predictive models. Fig. 5 illustrates an example implementation of a computer aided resource profiling system.
Fig. 2. EXPRESS-G diagram of the manufacturing resource model.
S.T. Newman, A. Nassehi / CIRP Annals - Manufacturing Technology 58 (2009) 421–424
423
Fig. 4. IDEF-0 representation of process planning with manufacturing capability profiles.
In this system the available tools are matched against a tool library to create a tooling profile for the machine tool at the time of manufacturing. By obtaining the tool wear information from sensors on the shop floor, or utilising a tool wear estimation algorithm [11,12], the tools that are too worn for use in manufacturing are deleted from the profile. Similarly the available fixtures are mapped on to a nominal library of fixtures to determine the fixturing capability available at the time of manufacturing. The accuracy of axes in a CNC machine tool changes over time and compensating against geometric errors is an important aspect of precision machining [13]. In addition, for certain types of jobs, machines that have an axis accuracy lower than a pre-defined threshold are not appropriate for use. In the computer aided resource profiling system the nominal axis accuracy information of resources are combined with the axis accuracy results from geometric tests on the CNC machine and extrapolated using a predictive model to determine the axis accuracy profile at the time of manufacture. Furthermore, the production policies set in the manufacturing enterprise such as the assignment of specific part families to specific machines or placement of limitations on the use of certain features on the CNC machine tool due to external considerations are taken into account. These policies are mapped on to the mechanical and technological capabilities provided in the nominal resource information model to construct a technical capability profile for the resource at the time of machining.
Fig. 5. An example implementation of a computer aided resource profiling system.
5. Case study A prototype implementation of the computer aided resource profiling system has been used to demonstrate the effects of tool wear on process planning when a machine tool capability profile is used. The part illustrated in Fig. 6 is adapted from ISO14649 part 11 [14] and used to generate the process plan. An excerpt of the nominal STEP-NC [8] process plan for this component is shown in Fig. 7. As indicated in lines #30 and #39 of the process plan, a 20 mm tapered endmill is the nominal cutting tool for manufacturing the rectangular pocket as, ceteris paribus, the quickest way to produce the pocket is using one that has the same radius as the planar radius on the rectangular pocket. In order to demonstrate resource capability profiling, the tool wear rate has been predicted according to the model in [15]. The wear model for the 20 mm tapered endmill is illustrated in Fig. 8. Within the prototype, a threshold is specified to consider tools with more than a certain amount of wear as unusable. In a production environment online tool wear monitoring could supplement or replace this prediction.
Fig. 6. Example production part [adapted from 14].
424
S.T. Newman, A. Nassehi / CIRP Annals - Manufacturing Technology 58 (2009) 421–424
The machining time for the rectangular pocket is 180 s. Consequently after machining 33 components the resource capability profiling system, considers the 20 mm endmill as unusable and adjusts the process plan so that an alternative tool is utilized. The next usable cutting tool in the nominal tool library is an 18 mm endmill and thus, the program is modified to use this cutting tool. The abbreviated capability adjusted STEP-NC process plan is shown in Fig. 9. The tool change can be seen in lines #30 and #39. 6. Conclusions
Fig. 7. Excerpt from the STEP-NC process plan for the test component (for complete listing refer to [14]).
Through the use of manufacturing resource capability profiles, it is possible to optimize the generation of the process plan to develop solutions that are appropriate for the actual available hardware and software rather than the nominal values. Capability profiles are generated by combining the nominal resource models with actual values obtained from sensors on the shop floor and predictive models. Although any nominal resource model can be utilized for this purpose, the use of a standardized resource model would substantially increase the breadth of the applicability of the resource capability profiles. With capability profiles, it is also possible to implement production policies during the generation of process plans, thus saving time and effort in comparison to implementing them afterwards on the shop floor. This results in an increase in trust throughout the process planning activity and leads to the generation of verified process plans. References
Fig. 8. Tool wear diagram for the 20 mm tapered endmill.
Fig. 9. The excerpt from the adjusted STEP-NC process plan for the test component highlighting the changes.
[1] ElMaraghy HA (1993) Evolution and Future Perspectives of CAPP. Annals of the CIRP 42(2):739–751. [2] Denkena B, Shpitalni M, Kowalski P, Molcho G, Zipori Y (2007) Knowledge Management in Process Planning. Annals of the CIRP 56(1):175–180. [3] Xu XW, Wang H, Mao J, Newman ST, Kramer TR, Proctor FM, Michaloski JL (2005) STEP-compliant NC Research: The Search for Intelligent CAD/CAPP/CAM/CNC Integration. International Journal of Production Research 43(17):3703–3743. [4] Maropoulos PG, McKay KR, Bramall DG (2002) Resource-Aware Aggregate Planning for the Distributed Manufacturing Enterprise. Annals of the CIRP 51(1):363–366. [5] Molcho G, Zipori Y, Schneor R, Rosen O, Goldstein D, Shpitalni M (2008) Computer Aided Manufacturability Analysis: Closing the Knowledge Gap between the Designer and the Manufacturer. Annals of the CIRP 57(1):153–158. [6] Paris H, Brissaud D (2000) Modelling for Process Planning: The Links between Process Planning Entities. Robotics & Computer Integrated Manufacturing 16(4): 259–266. [7] Vichare P, Nassehi A, Newman ST (2007) Towards a STEP-NC Compliant Model for Representation of Machine Tools. Proceedings of 4th International CIRP Sponsored Conference on Digital Enterprise Technology (DET2007), Bath, UK, 721–723. [8] ISO14649-1, 2003, Data Model for Computerized Numerical Controllers. Part 1. Overview and fundamental principles. [9] ISO10303-1, 1994, Industrial Automation Systems and Integration-Product Data Representation and Exchange. Part 1. Overview and fundamental principles. [10] Newman ST, Nassehi A (2007) Universal Manufacturing Platform for CNC Machining. Annals of the CIRP 56(1):459–462. [11] Wang X, Wang W, Huang Y, Nguyen N, Krishnakumal K (2008) Design of Neural Network-based Estimator for Tool Wear Modelling in Hard Turning. Journal of Intelligent Manufacturing 19(4):383–396. [12] Attanasio A, Ceretti E, Rizzuti S, Umbrello D, Micari F (2008) 3D Finite Element Analysis of Tool Wear in Machining. Annals of the CIRP 57(1):61–64. [13] Schwenke H, Knapp W, Haitjema H, Weckenmann A, Schmitt R, Delbressine F (2008) Geometric Error Measurement and Compensation of Machines—An Update. Annals of the CIRP 57(2):660–675. [14] ISO14649-11, Data Model for Computerized Numerical Controllers. Part 11. Process Data for Milling, 2004. [15] Kramer BM (1986) A Comprehensive Tool Wear Model. Annals of the CIRP 35(1):67–70.