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The uninterrupted operation of induction motors is crucial for industries, ensuring reliability and continuous functionality. To achieve this, we propose an innovative approach that utilizes an efficiency model-based digital shadow system for in situ failure detection and diagnosis (FDD) in induction motors (IMs). The shadow model accurately estimates IM losses and efficiency across various operational conditions. Our proposed method utilizes efficiency as the primary indicator for fault detection, while losses serve as condition indicators for fault diagnosis based on real-time motor parameters and loss sources. We introduce a bond graph as a fault diagnosis network, linking loss sources, motor parameters, and faults. This interconnected approach is the key aspect of our proposed diagnostic method and aims to be used in fault diagnosis as a general method. A case study of a broken rotor bar is used to validate the proposed method using a dataset of five motors. Among these, one motor operates without failure, while the remaining four exhibit broken rotor faults categorized as 1, 2, 3, and 4. The proposed method achieves 99.99% precision in identifying one to four defective rotor bars in IMs. Comparative analysis demonstrates good performance compared to vibration-based FDD approaches. Moreover, our methodology is computationally efficient and aligned with Industry 4.0 requirements. [ABSTRACT FROM AUTHOR]

Implementing the IIoT concept in industry involves the development and implementation of online systems monitoring the technical state of electromechanical equipment. This is achieved through the use of digital twins and digital shadows (object state observers). The tasks of mastering new rolling profiles and optimizing plate mill rolling programs require improved methods for calculating equivalent motor currents and torques. Known methods are generally based on calculations using smoothed load diagrams, which are assumed to be identical for the upper and lower main drive (UMD and LMD) rolls. These methods do not consider the differences in actual loads (currents or torques) in steady rolling states. Experiments performed on the 5000 plate mill have shown that due to speed mismatches, the UMD and LMD torques differ three times or more. This causes overheating of the more heavily loaded motor, insulation life reduction, and premature failure. Therefore, the problem of developing and implementing techniques for monitoring the load and thermal regimes of motors using digital observers is relevant. The paper's contribution is the first justification of the concept of object-oriented digital shadows. They are developed for specific classes of industrial units using open-source software. This research justifies a methodology for assessing motor load and temperature by processing arrays of motor currents or torques generated during rolling. An equivalent load observer and a temperature observer were proposed and implemented using Matlab-Simulink resources. The algorithm was implemented on the mill 5000 and tuned using an earlier-developed virtual commissioning methodology with digital twins. Thermal regimes were studied, proving that torque alignment ensures equal motor temperatures. The proposed considerations contribute to the development of the theory and practice for creating digital systems to monitor the technical condition of electromechanical and mechatronic systems and implementing the Industry 4.0 concept at industrial enterprises. [ABSTRACT FROM AUTHOR]

In this study, the Digital Shadow (DS) of the Shell and Tube Heat Exchanger (STHE) is designed and analyzed for numerous disturbances that occur when the system is in a running condition. The disruptive segregation of the heat exchanger is related to the DS for its operation, and thus a realistic DS was developed for the STHE. Fuzzy Logic (FL) was used to identify and segregate the disturbance signals from the process output. The Response Optimization (RO) algorithm was adopted and modified to work on the STHE. The observer-based residual generator design was implemented to prevent system failure and defective conditions. Model Predictive Controller (MPC), Transposed System Controller (TSC), and a looping-based control technique called Unity Response Loop (URL) were also implemented, and the results are discussed. The findings of this study contribute to the improvement of the overall performance of nonlinear systems in industrial processes and the avoidance of defects. [ABSTRACT FROM AUTHOR]

Open die forging is one of the oldest manufacturing methods known to remove defects in the ingot resulting from the casting process. The improved properties of the final component are highly dependent on the strain distribution. Although sinusoidal equations and empirical formulations have been already used to estimate the strain, they have been applied only to the core of the workpiece. In this work, a novel approach is presented to model the equivalent strain distribution in 2D cross-sections, in the direction of the press, of open die forged components using neural networks. The proposed method efficiently combines a parametric sinusoidal function with a neural network to learn the complex relationships between the process parameters and the resulting local strain. The neural network is trained on a dataset of finite element (FE) simulations of rectangular geometries that cover a wide range of aspect ratios, bite ratios, and height reductions. The presented methodology with near real-time prediction capabilities shows good agreement with FE results. Moreover, the parametric function captures the characteristic pattern of the strain distribution and reveals certain physical relationships affecting the deformation of the material. These patterns are later examined by analyzing the parameters identified in the parametric sinusoidal function.

ABSTRACTProduction Planning and Control involves combinatorial optimization problems subject to domain-related constraints. Hence, decision-support systems are required to support operators in finding suitable production scheduling models that match the conditions on the real shopfloor. Digital Shadows were introduced to solve this task by automatically gathering suitable models and the required input data to provide decision-support. Therefore, a standard definition of production scheduling models and their grouping within a model catalog is required in the virtual world. In this contribution, we introduce an Asset Administration Shell submodel that defines production scheduling models based on Graham’s three-field notation, which is independent of the underlying production process. We consider metadata, which ensures those models are FAIR, i.e. findable, accessible, interoperable, and reusable. In addition, we build a model catalog for production scheduling models in the injection molding domain as the foundation for implementing a decision-support system based on the Digital Shadows.

In the dynamic landscape of modern manufacturing, the pursuit of efficiency, reliability, and optimal performance has prompted the integration of cutting-edge technologies. Among these, digital twins (DT) have emerged as transformative tools, offering a virtual representation of physical processes, systems, and equipment. This paper delves into the pivotal role of digital twins in advancing the monitoring and supervision of manufacturing processes, focusing specifically on process digital twin (PDT) and its application in the domain of froth flotation in minerals processing. Our data-driven digital twin, replicating the behavior of a flotation cell, was developed using a combination of industrial and simulation data anchored by Artificial Neural Networks. This approach provides precise process emulation of the flotation process. Industrial evaluations of the AI model within the Digital Shadow demonstrated an overall 94% accuracy in estimating insightful information regarding the flotation cell operations with 2 s in response time. This research significantly contributes to the practical implementation of digital twins in industrial processes, highlighting their potential to revolutionize process control and enhance efficiency in the industrial sector. [ABSTRACT FROM AUTHOR]

In the drive towards sustainable design, the push for products with greater longevity, reparability, and recyclability has never been more crucial. Central to this is the integration of eco-design principles within manufacturing processes. However, there is a gap: manufacturers lack both standardized processes and digital tools to support them, even though the promising digital product passport largely focuses on product lifespan. Key Performance Indicators (KPIs) are paramount, serving as benchmarks for both the manufacturing process and environmental sustainability of a product. These KPIs encompass factors like energy, water, compressed air, and material resource consumption. To emphasize the importance of these metrics, Europe is vulnerable to supply disruptions due to its high dependence on raw materials from non-EU countries. This paper discusses the state of the art of digital twins and presents a digital shadow—a comprehensive digital tool design to support manufacturers during the product design phase. Drawing from a case study in the automotive sector, this tool not only aligns with recycling and sustainability objectives but also mitigates risks associated with raw material dependencies. [ABSTRACT FROM AUTHOR]

Technological development is largely determined by technology transfer, which is conditioned by the absorption capacity of new technologies, and new technologies are deeply digitized technologies. Success depends on creating basic digital skills, both technical and human, developing digital infrastructure, ensuring accessibility to digital technologies. In the context of Industry 4.0 technologies, the efforts to design products with pronounced physical-cybernetic elements are important, but especially those aimed at creating digital twins, gradually moving from intermediate variants of digital model and digital shadow. Digital twin creation models refer to the life cycle with emphasis on its stages. The paper proposes a digital twin model based on the technological function, in which the modification of the properties is manifested by restructuring the internal functions of the operator, operand and interface. The central place in this model belongs to the interface, which reflects the multitude of physical-technical processes at different scales of macro, meso, micro analysis characteristic of objects and processes in machine building.

The digital transformation process in organizations has accelerated significantly in recent years; the COVID-19 pandemic was a catalyst that highlighted the need for digitalization in all sectors. In the case of the health sector, this process is complex due to the processes inherent in health care as well as the integration of multiple sectors that allow the provision of health services. A first approach towards the construction of a Digital Twin in health organizations is a Digital Shadow that allows an orderly transition towards digital operation in real time. This paper presents a first approach to the design of a Digital Shadow for the health insurance sector and specifically for the care of patients diagnosed with COVID-19 through the implementation of an analytical intelligence system based on machine learning models to forecast and monitor to patients who represent catastrophic cases for the insurer. [ABSTRACT FROM AUTHOR]

The value stream method, a key tool in industry to analyze and visualize value streams in production, aims to holistically optimize process steps, reduce waste, and achieve continuous material flow. However, this method primarily relies on data from a single on-site inspection, which is subjective and represents just a snapshot of the process. This limitation can lead to uncertainty and potentially incorrect decisions, especially in industries producing customer-specific products. The increasing digitization in production offers a solution to this limitation by supporting the method through data provision. The concept of the digital shadow emerges as a key tool that systematically captures, processes, and integrates necessary data into a model to enhance traditional value stream mapping. This addresses the method's shortcomings, especially in heterogeneous IT landscapes and complex value streams. To effectively implement the digital shadow this study identifies concepts of digital shadows and their key components and evaluates them for their relevance in industrial environments using an expert study. Based on the results, a design model is defined. This model entails guidelines to support companies with the practical implementation of the digital shadow of a value stream. Lastly, the model is evaluated on a realistic value stream in a learning factory. [ABSTRACT FROM AUTHOR]

Digital twin (DT) is a research topic that gained momentum in the Industry 4.0 era. The goal of DT is to create a virtual real-time intelligent system that is a typical twin of the physical system. DT provides analysis, prognosis, planning, and rapid response when needed. Digital shadow (DS) is an artifact concept of DT that provides a real-time replica of the physical system. Information in a DS is passed in one direction only, from the physical system to the virtual one. While in DT, the information goes in both directions. The definition and roles of DS and DT are overlapping. However, DS can be defined as the main component of a DT system. In this paper, DS roles are specified. Based on these roles, A DS framework architecture is proposed, and the communication system between its components. The proposed framework design is built using distributed system concepts such as event-driven architecture, microservices, and containerization. These concepts are well defined and utilized in the software engineering domain. The originality of the proposed framework is the definition of a systematic approach for designing and integrating digital models (DMs) from different vendors and domains. An experiment is designed to prove the framework's ability to shadow a physical system in real-time. Multiple DMs are implemented and deployed on the proposed DS framework. These DMs are used to shadow a natural gas compressor system. Experimental results prove the practicality of our proposed DS framework to operate in real-time. [ABSTRACT FROM AUTHOR]

Implementing the IIoT concept in industry involves the development and implementation of online systems monitoring the technical state of electromechanical equipment. This is achieved through the use of digital twins and digital shadows (object state observers). The tasks of mastering new rolling profiles and optimizing plate mill rolling programs require improved methods for calculating equivalent motor currents and torques. Known methods are generally based on calculations using smoothed load diagrams, which are assumed to be identical for the upper and lower main drive (UMD and LMD) rolls. These methods do not consider the differences in actual loads (currents or torques) in steady rolling states. Experiments performed on the 5000 plate mill have shown that due to speed mismatches, the UMD and LMD torques differ three times or more. This causes overheating of the more heavily loaded motor, insulation life reduction, and premature failure. Therefore, the problem of developing and implementing techniques for monitoring the load and thermal regimes of motors using digital observers is relevant. The paper’s contribution is the first justification of the concept of object-oriented digital shadows. They are developed for specific classes of industrial units using open-source software. This research justifies a methodology for assessing motor load and temperature by processing arrays of motor currents or torques generated during rolling. An equivalent load observer and a temperature observer were proposed and implemented using Matlab-Simulink resources. The algorithm was implemented on the mill 5000 and tuned using an earlier-developed virtual commissioning methodology with digital twins. Thermal regimes were studied, proving that torque alignment ensures equal motor temperatures. The proposed considerations contribute to the development of the theory and practice for creating digital systems to monitor the technical condition of electromechanical and mechatronic systems and implementing the Industry 4.0 concept at industrial enterprises.

In the past, plant layouts were regarded as highly static structures. With increasing internal and external factors causing turbulence in operations, it has become more necessary for companies to adapt to new conditions in order to maintain optimal performance. One possible way for such an adaptation is the adjustment of the plant layout by rearranging the individual facilities within the plant. Since the information about the plant layout is considered as master data and changes have a considerable impact on interconnected processes in production, it is essential that this data remains accurate and up-to-date. This paper presents a novel approach to create a digital shadow of the plant layout, which allows the actual state of the physical layout to be continuously represented in virtual space. To capture the spatial positions and orientations of the individual facilities, a pan-tilt-zoom camera in combination with fiducial markers is used. With the help of a prototypically implemented system, the real plant layout was captured and converted into different data formats for further use in exemplary external software systems. This enabled the automatic updating of the plant layout for simulation, analysis and routing tasks in a case study and showed the benefits of using the proposed system for layout capturing in terms of accuracy and effort reduction. [ABSTRACT FROM AUTHOR]

Monitoring on road bridges in Germany is rare and mostly designed to deal with existing damages and deficits, as a survey of the Federal Ministry of Digital and Transport in Germany has shown in 2020. Other use cases are monitoring to support structural inspection, monitoring to monitor significant structures, or impact monitoring. The objectives, benefits and examples of the various use cases are described in the present article. Monitoring results are most useful when they are combined with other information about the structure, such as repair measures or damage development, in a common data environment. The Digital Twin is a possibility to merge the existing information und represents the highest expression of digital implementation. The Digital Shadow with an automatic data link from the physical to the digital object and the manual data link from the digital to the physical object represents a preliminary stage of the Digital Twin. The implementation of a Digital Shadow is already taking place at a demonstrator structure of the BASt. The basis for the Digital Shadow is the parametric BIM model. In a selected BIM application, the available information was compiled for this bridge and visualized in a common data environment. [ABSTRACT FROM AUTHOR]

In today's industry, the fourth industrial revolution is underway, characterized by the integration of advanced technologies such as artificial intelligence, the Internet of Things, and big data. One of the key pillars of this revolution is the technology of digital twin, which is rapidly gaining importance in various industries. However, the concept of digital twins is often misunderstood or misused as a buzzword, leading to confusion in its definition and applications. This observation inspired the authors of this paper to create their own demonstration applications that allow the control of both the real and virtual systems through automatic two-way communication and mutual influence in context of digital twins. The paper aims to demonstrate the use of digital twin technology aimed at discrete manufacturing events in two case studies. In order to create the digital twins for these case studies, the authors used technologies as Unity, Game4Automation, Siemens TIA portal, and Fishertechnik models. The first case study involves the creation of a digital twin for a production line model, while the second case study involves the virtual extension of a warehouse stacker using a digital twin. These case studies will form the basis for the creation of pilot courses for Industry 4.0 education and can be further modified for the development of Industry 4.0 educational materials and technical practice. In conclusion, selected technologies are affordable, which makes the presented methodologies and educational studies accessible to a wide range of researchers and solution developers tackling the issue of digital twins, with a focus on discrete manufacturing events. [ABSTRACT FROM AUTHOR]

While innovations in metal forming technology are closely linked to material science, automation, and mechatronization in recent decades, nowadays, and in the upcoming years, they are supposed to be driven by digital transformation and virtualization. Motivated by this expectation and the fact that the dynamic, process‐relevant thermophysical state variables in plates during rolling are difficult or even impossible to measure, a modularly structured 2D/3D model, integrated into a digital shadow, is built and validated against a metrologically accessible process data. The decisive innovation of the presented modeling strategy is the investigation of the state during longitudinal rolling in the fillet, depending on the width direction. The fillet marks the heavy plate without head and tail crops, developed during longitudinal rolling. Application of the simulation tool reveals significant temperature gradients near the plate surfaces, both horizontally and vertically, but also a region of nearly constant temperature near the core. The development of the fillet cross‐section shape, including the emergence of bulges or contractions at the side surfaces, is predicted within the simulation. The digital shadow is scalable and has a parameterized structure so that rolling schedules of different compositions can be simulated. [ABSTRACT FROM AUTHOR]

The growth of goods transported by ship affects the entire maritime industry, which is central to the global supply chain. Faced with an increased pressure from the industry, ports need greater visibility on the flow of goods and logistics operations. To tackle this challenge, improvement opportunities as well as weaknesses must be identified in order to define an efficient data acquisition strategy that will allow port authorities to enhance their current visibility within their port ecosystem. This article presents the simulation of a digital shadow based on a Canadian port case study. The study uses the simulation of a digital shadow to diagnose a Canadian port authority's current visibility, with an accent on data accuracy, in order to identify improvement opportunities aimed at overcoming weaknesses identified in a current practice scenario and then compare the performance of the current system with said improvements in a second scenario. To our knowledge, this is the first simulation study on visibility performance assessment in a port's digital shadow. [ABSTRACT FROM AUTHOR]

In the era of digital transformation, industry is facing multiple challenges due to the need for implementation of the Industry 4.0 standards, as well as the volatility of customer demands. The latter has created the need for the design and operation of more complex manufacturing systems and networks. A case study derived from Process Industries (PIs) is adopted in this research work in order to design a framework for flexible design of production lines, automation of quality control points, and improvement of the performance of the manufacturing system. Therefore, a Digital Shadow of a production line is developed to collect, analyze and identify potential issues (bottlenecks). An edge computing system for reliable and low-latency communications is also implemented. The digital model is validated using statistical Design Of Experiments (DOE) and ANalysis Of VAriance (ANOVA). For the assessment of what-if scenarios, the Digital Shadow model will be used in order to evaluate and find the desired solution. Ultimately, the goal of this research work is to improve the design and performance of the industry's production section, as well as to increase the production rate and the product mix. [ABSTRACT FROM AUTHOR]

The digitalization of processes is one of the currently dominating topics and missions in both industrial production and scientific research. In biotechnology, these efforts have enormous potential to optimize existing bioprocesses or to develop new bioprocesses for upcoming challenges. In this review, the state‐of‐art of digitalization in bioprocess engineering is considered, the terms digital twin and digital shadow are characterized, and an outlook on future Lab 4.0 concepts is given. [ABSTRACT FROM AUTHOR]

Collecting and utilizing data in industrial production are becoming increasingly important. One promising approach to utilize data is the concept of digital twin (DT). DTs are virtual representations of physical assets, updated by real data and enhanced by models. This paper provides an overview of DTs for cutting processes. After giving a definition, we discuss requirements derived from representative use cases. As process models are central for DT creation, we present an overview of the latest research as well as conditions for how it can be implemented in industrial environments. The paper concludes with main challenges for future research. [ABSTRACT FROM AUTHOR]

One vision of industry 4.0 is an autonomous shopfloor configuration. A possibility to transfer this vision to reality is realizable with Digital Shadows. As Digital Shadows are well defined in research, challenges for seamless integration in production are still an open research topic. The main challenge is the enrichment of semantics on the domain in a standardized format that enables multilateral communication between all assets. To overcome these challenge, tools have to be developed to support domain experts in building and integrating Digital Shadows without knowledge of modeling and programming. This article demonstrates the main challenges of this objective and proposes a research agenda to close this research gap. [ABSTRACT FROM AUTHOR]

The growing relevance of Industry 4.0-related technologies and the transformation toward service-oriented business models in manufacturing requires relevant, high-qualitative data. The digital representation of operation and manufacturing under a specific purpose is called a digital shadow (DS). Although this purpose represents the decisive factor between different digital shadows, its systematic determination has been addressed insufficiently. Therefore, this paper presents a systematic, purpose-driven modeling approach for the digital shadow supporting manufacturing companies in developing a data basis for applications. This approach is applied to a crimping machine as an example and will be optimized in future evaluations. [ABSTRACT FROM AUTHOR]

The 4th industrial revolution requires the tracking and optimization of energy consumption using an intelligent energy management system (IEMS). Such a system needs accurate real-time energy information to operate industrial machines, where Induction Motors (IMs) represent 42.2% of the global energy consumption. This paper addresses the problems of data acquisition for Induction motors in the context of Industry 4.0 (I4.0) where precision, real-time constraints, and optimal operations play a major role in decision-making. A new vision of Digital Shadow (DS) is proposed for the energy efficiency (EE) of IMs. A hybrid model consisting of a data-driven model and a physics-based model is developed to represent the machine efficiency information in real-time (RT). The proposed method is developed based on a two-stage procedure. Firstly, the IM EE model is established by considering Stator joule losses, core losses, rotor joule losses, friction, and windage losses, in addition to the stray losses to create an improved model. This was established based on the double cage model where core loss resistance is added. Secondly, the machine’s losses and efficiency are visualized using the 8 electrical circuit parameters (ECP) from the double cage model in addition to the rated speed and test temperature. The parameters estimation in RT incurred complexities and required the use of Adaptive Neuro-Fuzzy Inference System (ANFIS)-based modeling. The proposed model was developed using Fuzzy Logic Toolbox and Neuro-Fuzzy Designer app from MATLAB by training Sugeno systems. 8 ANFIS MATLAB models are trained to estimate each of the 8 ECP from the double cage model by using standard inputs. The training and testing dataset is constituted by the experimental data of the ECPs were calculated using the FSOLVE function to solve the nonlinear system formed by 60 motors from 8 manufacturers’ data such as voltage, number of pole-pairs, rated output power, rated torque, current, starting current, maximum torque and the power factor. Finally, the proposed method is validated experimentally using a 1.5 KW, 400/230 V, 50 Hz squirrel cage induction motor (SCIM) for linear speed control. The EE, Torque, and losses are visualized through the proposed model using MATLAB/SIMULINK. The mean value of the RMSE for the eight estimated ECPs are 7.57e-05, and 5.73e-01 respectively for training and testing while the mean values of the MAE are 2.06e-05, and 3.79e-01 respectively for training and testing. The errors of the EE estimation w.r.t the measured EE at rated condition, are RMSE = 0.205, and MAE = 0.1671. These results show that the proposed model can be implemented in the industry to monitor the machine EE and its losses.

The value stream method, a key tool in industry to analyze and visualize value streams in production, aims to holistically optimize process steps, reduce waste, and achieve continuous material flow. However, this method primarily relies on data from a single on-site inspection, which is subjective and represents just a snapshot of the process. This limitation can lead to uncertainty and potentially incorrect decisions, especially in industries producing customer-specific products. The increasing digitization in production offers a solution to this limitation by supporting the method through data provision. The concept of the digital shadow emerges as a key tool that systematically captures, processes, and integrates necessary data into a model to enhance traditional value stream mapping. This addresses the method’s shortcomings, especially in heterogeneous IT landscapes and complex value streams. To effectively implement the digital shadow this study identifies concepts of digital shadows and their key components and evaluates them for their relevance in industrial environments using an expert study. Based on the results, a design model is defined. This model entails guidelines to support companies with the practical implementation of the digital shadow of a value stream. Lastly, the model is evaluated on a realistic value stream in a learning factory.

Abstract One vision of industry 4.0 is an autonomous shopfloor configuration. A possibility to transfer this vision to reality is realizable with Digital Shadows. As Digital Shadows are well defined in research, challenges for seamless integration in production are still an open research topic. The main challenge is the enrichment of semantics on the domain in a standardized format that enables multilateral communication between all assets. To overcome these challenge, tools have to be developed to support domain experts in building and integrating Digital Shadows without knowledge of modeling and programming. This article demonstrates the main challenges of this objective and proposes a research agenda to close this research gap.

In multi-step production processes with countless information sources, it is vital to accompany process data with meta-information to enable an efficient aggregation of the collected data. The relationships of process- and meta-data has been described through a previously presented flexible and process-independent meta-model, which is especially suitable for predictive quality applications. We propose an enhancement for the attribution of process parameters and quality characteristics. The two novel meta-classes provide information about tolerances and hence increase the process and product model. Our results show how the refined meta-model enhances the mapping of an additive manufacturing process. Further, we introduce an industry-ready implementation and make the code accessible on Github. [ABSTRACT FROM AUTHOR]

Tool flank wear monitoring can minimize machining downtime costs while increasing productivity and product quality. In some industrial applications, only a limited level of tool wear is allowed to attain necessary tolerances. It may become challenging to monitor a limited level of tool wear in the data collected from the machine due to the other components, such as the flexible vibrations of the machine, dominating the measurement signals. In this study, a tool wear monitoring technique to predict limited levels of tool wear from the spindle motor current and dynamometer measurements is presented. High-frequency spindle motor current data is collected with an industrial edge device while the cutting forces and torque are measured with a rotary dynamometer in drilling tests for a selected number of holes. Feature engineering is conducted to identify the statistical features of the measurement signals that are most sensitive to small changes in tool wear. A neural network based on the long short-term memory (LSTM) architecture is developed to predict tool flank wear from the measured spindle motor current and dynamometer signals. It is demonstrated that the proposed technique predicts tool flank wear with good accuracy and high computational efficiency. The proposed technique can easily be implemented in an industrial edge device as a real-time predictive maintenance application to minimize the costs due to manufacturing downtime and tool underuse or overuse. [ABSTRACT FROM AUTHOR]