A data-driven two-stage maintenance framework for degradation prediction in semiconductor manufacturing industries

To reduce the production costs and breakdown risks in industrial manufacturing systems, condition-based maintenance has been actively pursued for prediction of equipment degradation and optimization of maintenance schedules. In this paper, a two-stage maintenance framework using data-driven techniques under two training types will be developed to predict the degradation status in industrial applications. The proposed framework consists of three main blocks, namely, Primary Maintenance Block (PMB), Secondary Maintenance Block (SMB), and degradation status determination block. As the popular methods with deterministic training, back-propagation Neural Network (NN) and evolvable NN are employed in PMB for the degradation prediction. Another two data-driven methods with probabilistic training, namely, restricted Boltzmann machine and deep belief network are applied in SMB as the backup of PMB to model non-stationary processes with the complicated underlying characteristics. Finally, the multiple regression forecasting is adopted in both blocks to check prediction accuracies. The effectiveness of our proposed two-stage maintenance framework is testified with extensive computation and experimental studies on an industrial case of the wafer fabrication plant in semiconductor manufactories, achieving up to 74.1% in testing accuracies for equipment degradation prediction.     

A subsumptive, hierarchical, and distributed vision-based architecture for smart robotics

We present a distributed vision-based architecture for smart robotics that is composed of multiple control loops, each with a specialized level of competence. Our architecture is subsumptive and hierarchical, in the sense that each control loop can add to the competence level of the loops below, and in the sense that the loops can present a coarse-to-fine gradation with respect to vision sensing. At the coarsest level, the processing of sensory information enables a robot to become aware of the approximate location of an object in its field of view. On the other hand, at the finest end, the processing of stereo information enables a robot to determine more precisely the position and orientation of an object in the coordinate frame of the robot. The processing in each module of the control loops is completely independent and it can be performed at its own rate. A control Arbitrator ranks the results of each loop according to certain confidence indices, which are derived solely from the sensory information. This architecture has clear advantages regarding overall performance of the system, which is not affected by the "slowest link," and regarding fault tolerance, since faults in one module does not affect the other modules. At this time we are able to demonstrate the utility of the architecture for stereoscopic visual servoing. The architecture has also been applied to mobile robot navigation and can easily be extended to tasks such as "assembly-on-the-fly."     

Ubiquitous manufacturing system based on Cloud: A robotics application

Modern manufacturing industry calls for a new generation of production system with better interoperability and new business models. As a novel information technology, Cloud provides new service models and business opportunities for manufacturing industry. In this research, recent Cloud manufacturing and Cloud robotics approaches are reviewed. Function block-based integration mechanisms are developed to integrate various types of manufacturing facilities. A Cloud-based manufacturing system is developed to support ubiquitous manufacturing, which provides a service pool maintaining physical facilities in terms of manufacturing services. The proposed framework and mechanisms are evaluated by both machining and robotics applications. In practice, it is possible to establish an integrated manufacturing environment across multiple levels with the support of manufacturing Cloud and function blocks. It provides a flexible architecture as well as ubiquitous and integrated methodologies for the Cloud manufacturing system.     

Towards a balanced trade-off between speed and accuracy in unsupervised data-driven image segmentation

When it comes to image segmentation in the megapixel domain, most state-of-the-art algorithms use sampling to reduce the amount of data to be processed to reach a lower running time. Random patterns and equidistant sampling usually result in a suboptimal result because, in general, the distribution of image content is not homogeneous. The segmentation framework we propose in this paper, employs a content-adaptive technique that samples homogeneous and inhomogeneous regions sparsely and densely, respectively, thus it preserves information content in a computationally efficient way. Both the sampling procedure and the pixel-cluster assignment are guided by the same nonlinear confidence value, calculated for each image pixel with no overhead, which describes the strength of the pixel-cluster bond. Building on this confidence scheme, each pixel is associated with the most similar class with respect to its spatial position and color. We compare the performance of our framework to other segmentation algorithms on publicly available segmentation databases and using a set of 10-megapixel images, we show that it provides similar segmentation quality to a mean shift-based reference in an order of magnitude shorter time, the speedup being proportional to the amount of details in the input image. Based on our findings, we also sketch up novel design aspects to be taken into account when designing a high resolution evaluation framework.     

Editorial: Intelligence in robotics and manufacturing

Mr Mojamshidiis is the Director, CAD Laboratory Systems/Robotics, Professor of Electrical &; Computer Engineering, AT &; T Professor of Manufacturing Engineering, The University of New Mexico, Albuquerque, NM 87131 U.S.A.     

A big data-driven framework for sustainable and smart additive manufacturing

From the last decade, additive manufacturing (AM) has been evolving speedily and has revealed the great potential for energy-saving and cleaner environmental production due to a reduction in material and resource consumption and other tooling requirements. In this modern era, with the advancements in manufacturing technologies, academia and industry have been given more interest in smart manufacturing for taking benefits for making their production more sustainable and effective. In the present study, the significant techniques of smart manufacturing, sustainable manufacturing, and additive manufacturing are combined to make a unified term of sustainable and smart additive manufacturing (SSAM). The paper aims to develop framework by combining big data analytics, additive manufacturing, and sustainable smart manufacturing technologies which is beneficial to the additive manufacturing enterprises. So, a framework of big data-driven sustainable and smart additive manufacturing (BD-SSAM) is proposed which helped AM industry leaders to make better decisions for the beginning of life (BOL) stage of product life cycle. Finally, an application scenario of the additive manufacturing industry was presented to demonstrate the proposed framework. The proposed framework is implemented on the BOL stage of product lifecycle due to limitation of available resources and for fabrication of AlSi10Mg alloy components by using selective laser melting (SLM) technique of AM. The results indicate that energy consumption and quality of the product are adequately controlled which is helpful for smart sustainable manufacturing, emission reduction, and cleaner production.     

A data-driven generic simulation model for logistics-embedded assembly manufacturing lines

Simulation has been used to evaluate various aspects of manufacturing systems. However, building a simulation model of a manufacturing system is time-consuming and error-prone because of the complexity of the systems. This paper introduces a generic simulation modeling framework to reduce the simulation model build time. The framework consists of layout modeling software and a data-driven generic simulation model. The generic simulation model was developed considering the processing as well as the logistics aspects of assembly manufacturing systems. The framework can be used to quickly develop an integrated simulation model of the production schedule, operation processes and logistics of a system. The framework was validated by developing simulation models of cellular and conveyor manufacturing systems.     

Ethics of healthcare robotics: Towards responsible research and innovation

How can we best identify, understand, and deal with ethical and societal issues raised by healthcare robotics? This paper argues that next to ethical analysis, classic technology assessment, and philosophical speculation we need forms of reflection, dialogue, and experiment that come, quite literally, much closer to innovation practices and contexts of use. The authors discuss a number of ways how to achieve that. Informed by their experience with “embedded” ethics in technical projects and with various tools and methods of responsible research and innovation, the paper identifies “internal” and “external” forms of dialogical research and innovation, reflections on the possibilities and limitations of these forms of ethical–technological innovation, and explores a number of ways how they can be supported by policy at national and supranational level.     

A lab-scale manufacturing system environment to investigate data-driven production control approaches

Controlling production and release of material into a manufacturing system effectively can lower work-in-progress inventory and cycle time while ensuring the desired throughput. With the extensive data collected from manufacturing systems, developing an effective real-time control policy helps achieving this goal. Validating new control methods using the real manufacturing systems may not be possible before implementation. Similarly, using simulation models can result in overlooking critical aspects of the performance of a new control method. In order to overcome these shortcomings, using a lab-scale physical model of a given manufacturing system can be beneficial. We discuss the construction and the usage of a lab-scale physical model to investigate the implementation of a data-driven production control policy in a production/inventory system. As a data-driven production control policy, the marking-dependent threshold policy is used. This policy leverages the partial information gathered from the demand and production processes by using joint simulation and optimization to determine the optimal thresholds. We illustrate the construction of the lab-scale model by using LEGO Technic parts and controlling the model with the marking-dependent policy with the data collected from the system. By collecting data directly from the lab-scale production/inventory system, we show how and why the analytical modeling of the system can be erroneous in predicting the dynamics of the system and how it can be improved. These errors affect optimization of the system using these models adversely. In comparison, the data-driven method presented in this study is considerably less prone to be affected by the differences between the physical system and its analytical representation. These experiments show that using a lab-scale manufacturing system environment is very useful to investigate different data-driven control policies before their implementation and the marking-dependent threshold policy is an effective data-driven policy to optimize material flow in manufacturing systems.     

A real-time data-driven collaborative mechanism in fixed-position assembly systems for smart manufacturing

Assembly stations are important hubs that connect massive material, information, human labor, etc. The fixed-position assembly systems for complex products may deal with hundreds of thousands of processes, making them vulnerable to manufacturing exceptions. Many scheduling problems were described and solved in the past decades, however, the gap between theoretical models and industrial practices still exist. To achieve a practical method for the dynamic scheduling in case of exceptions while reducing the impact brought by the exceptions, an Intelligent Collaborative Mechanism (ICM) was proposed where negotiations on resource configuration may happen among tasks (i.e. assembly processes). The intercommunication among resources was guaranteed by the data-driven ICM framework. The Petri-net-based workflow analysis and the constraint matrix can pick out the tasks that are currently not bound by other ones. The dynamic priority of the processes was defined and obtained using grey relational analysis. The matching strategy among the selected tasks and operators can provide a scheduling plan that is close to the initial plan, so the assembly systems may remain effective even when exceptions occur. The proposed models were analyzed in a case scenario, where the impact brought by exceptions can decrease by 44.3% in terms of the operators’ utilization rate, and by 60.26% in terms of the assembly time. This research has provided a practical strategy to improve the flexibility and effectiveness of assembly systems for complex products.     

Connotative land degradation mapping: A knowledge-based approach to land degradation assessment

Land degradation mapping is a problem-solving task that aims to provide information for allocating budgets and materials to counter the deterioration of land resources. Typically, it entails the implementation of a set of indicators in a GIS to appraise the severity of land degradation across a territory. Nevertheless, the selection of these indicators has proved to be challenging in practice and often this selection reflects one particular and thus limited perspective of land degradation. Because land degradation is intrinsically complex and involves decisions by many agencies and individuals, land degradation mapping should be used as a learning tool through which managers, experts and stakeholders can re-examine their views within a wider semantic context. In this paper, we introduce an analytical framework, called Connotative Land Degradation Mapping, which aims to depict the meaning of a multiplicity of interacting drivers and effects The CLDM entails the implementation of (1) geographic information systems and multicriteria decision analysis (GIS-MCDA), and (2) geo-visualization. The approach is illustrated through a case study of two urban watersheds in central Mexico. Results showed that the main land degradation drivers in the study area were related to natural processes, which were exacerbated by human activities. The output of the CLDM enabled a better communication of the land degradation issues and concerns in a way relevant for policymakers.     

A comprehensive review on deploying robotics application in telecom network tower's field maintenance: Challenges with current practices and feasibility analysis for robotics implementation

This survey article highlights the difficulties in the field maintenance of telecommunication towers. It critically analyses the main features of the deployment of robots to maintain telecommunication towers. The growing demand for mobile connectivity poses the need for more towers, and the subsequent problem of network maintenance becomes more critical. Most tower maintenance is required work at height; therefore, height-related risks are more frequent. A rigorous review is conducted, and the growth of the telecommunications network and key on-site maintenance challenges are analyzed. Despite numerous challenges, these towers are maintained manually by riggers (certified climbers) worldwide. It raises the question, Is it possible to implement automation by robots for the maintenance of telecommunications towers? The feasibility analysis to deploy the robots is conducted systematically. To access the tower through a robot, detailed information on the type of towers, the climbing arrangements available on the existing towers, and the necessary operations to be carried out at the height is collected. A critical analysis of the climbing robots currently available in the literature, their grasping technology, and control algorithms is performed. The opinion of experts in the telecommunication industry is very helpful in identifying the requirements of robotic systems. The design attributes especially needed for the climbing robot, and the execution of the maintenance in height are highlighted. Due justification is given for deploying robots for field maintenance of telecom towers. The recommended methodology for designing an automation system helps research in the field of maintenance of telecom towers through robots, which could bring a remarkable solution to the telecom sector.     

Digital twin-based industrial cloud robotics: Framework,control approach and implementation

Industrial cloud robotics (ICR) integrates cloud computing with industrial robots (IRs). The capabilities of industrial robots can be encapsulated as cloud services and used for ubiquitous manufacturing. Currently, the digital models for process simulation, path simulation, etc. are encapsulated as cloud services. The digital models in the cloud may not reflect the real state of the physical robotic manufacturing systems due to inaccurate or delayed condition update and therefore result in inaccurate simulation and robotic control. Digital twin can be used to realize fine sensing control of the physical manufacturing systems by a combination of high-fidelity digital model and sensory data. In this paper, we propose a framework of digital twin-based industrial cloud robotics (DTICR) for industrial robotic control and its key methodologies. The DTICR is divided into physical IR, digital IR, robotic control services, and digital twin data. First, the robotic control capabilities are encapsulated as Robot Control as-a-Service (RCaaS) based on manufacturing features and feature-level robotic capability model. Then the available RCaaSs are ranked and parsed. After manufacturing process simulation with digital IR models, RCaaSs are mapped to physical robots for robotic control. The digital IR models are connected to the physical robots and updated by sensory data. A case is implemented to demonstrate the workflow of DTICR. The results show that DTICR is capable to synchronize and merge digital IRs and physical IRs effectively. The bidirectional interaction between digital IRs and physical IRs enables fine sensing control of IRs. The proposed DTICR is also flexible and extensible by using ontology models.     

Activation pattern controlled rules: Towards an integration of data-driven and command-driven programming

The attractions and drawbacks of data-driven programming are discussed in the context of rule-based forward chaining systems. The relationships between data-driven and command-driven programming are analyzed in the context of a course-registration example. A new form of production rule, called an activation pattern controlled rule, that generalizes classical forward chaining rules is introduced. Activation pattern controlled rules are triggered by calls of commands; that is, by the intension to perform a command but not necessarily by the result of applying the command itself. We demonstrate that activation pattern controlled rules facilitate the integration of data-driven and command-driven programming, support preventive programming as well, and allow for writing rule-based programs more transparently. We also survey our experiences in implementing an inference engine for activation pattern controlled rules.     

Microsoft robotics studio: A technical introduction

Microsoft robotics studio (MSRS) was publicly released in December 2006 with the explicit goal of providing an industry software standard for robot control. To become a viable standard, several technical challenges needed to be solved. In this article, we examine the composition of MSRS, looking generally at its architecture and specifically at its solutions for concurrency, distribution, abstraction, simulation, and programmer interaction. We also examine briefly the emerging industry and academic adoption of the robotics studio.     

Testbeds for ubiquitous robotics: A survey

The growing interest in ubiquitous robotics has originated in the last years the development of a high variety of testbeds. This paper presents a survey on existing ubiquitous robotics testbeds comprising networked mobile robots and networks of distributed sensors, cameras and smartphones, among others. The survey provides an insight into the testbed design, internal behavior and use, identifying trends and existing gaps and proposing guidelines for testbed developers. The level of interoperability among different ubiquitous robotics technologies is used as the main conducting criterion of the survey. Other features analyzed include testbed architectures, target experiments and usability tools.     

Neural networks in robotics: A survey

The purpose of this paper is to provide an overview of the research being done in neural network approaches to robotics, outline the strengths and weaknesses of current approaches, and predict future trends in this area.This work was supported, in part, by Sandia National Laboratories under contract No. 06-1977, Albuquerque, New Mexico.     

A data-driven decision support system for sustainable supplier evaluation in the Industry 5.0 era: A case study for medical equipment manufacturing

Effective supplier management is critical for an enterprise’s success, as supplier procurement accounts for up to approximately 70% to 80% of total manufacturing costs. Correct supplier selection can ensure the competitiveness of the enterprise and contribute to supply chain integration and innovation. In recent years, supplier evaluation frameworks based on Industry 4.0 concepts have contributed to the development of the industry. However, the novel concepts of Industry 5.0 require examination from a people-oriented and sustainable perspective. Unfortunately, at the present time, supplier evaluation frameworks based on Industry 5.0 are lacking. Therefore, the primary task of this study is to develop a novel and comprehensive supplier evaluation framework for the Industry 5.0 era. This study proposes a data-driven decision support system to execute the supplier evaluation process. First, variable precision- dominance-based rough set approach (VC-DRSA) is applied to extract the core criteria, to remove the noise factors and to generate decision rules for the decision-makers’ reference. Second, the criterion importance through intercriteria correlation (CRITIC) approach is adopted to obtain the dependency weights of the core criteria and their ranking. Finally, a modified classifiable technique for order preference by similarity to ideal solution (CTOPSIS) is used to integrate the final performance values of suppliers when new alternative suppliers are added. The research concept is in line with the conception of data-driven decision support in business intelligence and does not rely on the subjective judgments and opinions of experts. Data provided by a multinational medical equipment manufacturer are used as an example to demonstrate the proposed model. VC-DRSA retains nine core criteria from the original twenty criteria, which greatly reduces the labor and cost of supplier audits. In addition, the CRITIC results show that digital transformation, real-time information sharing, and organizational culture transformation are the three main factors affecting the development of enterprises towards Industry 5.0. The results show that CTOPSIS can be used to quickly assess the ratings of new alternative suppliers are listed.