An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

The industrial manufacturing sector is undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer static equipment but an active workforce to the factory production alongside human operators. Safety becomes crucial for humans and robots to ensure a smooth production run in such environments. The loss of operating moving robots in plant evacuation can be avoided with the adequate safety induction for them. Operators are subject to frequent safety inductions to react in emergencies, but very little is done for robots. Our research proposes an experimental safety response mechanism for a small manufacturing plant, through which an autonomous robot learns the obstacle-free trajectory to the closest safety exit in emergencies. We implement a reinforcement learning (RL) algorithm, Q-learning, to enable the path learning abilities of the robot. After obtaining the robot optimal path selection options with Q-learning, we code the outcome as a rule-based system for the safety response. We also program a speech recognition system for operators to react timeously, with a voice command, to an emergency that requires stopping all plant activities even when they are far away from the emergency stops (ESTOPs) button. An ESTOP or a voice command sent directly to the factory central controller can give the factory an emergency signal. We tested this functionality on real hardware from an S7-1200 Siemens programmable logic controller (PLC). We simulate a simple and small manufacturing environment overview to test our safety procedure. Our results show that the safety response mechanism successfully generates paths without obstacles to the closest safety exits from all the factory locations. Our research benefits any manufacturing SME intending to implement the initial and primary use of autonomous moving robots (AMR) in their factories. It also impacts manufacturing SMEs using legacy devices such as traditional PLCs by offering them intelligent strategies to incorporate current state-of-the-art technologies such as speech recognition to improve their performances. Our research empowers SMEs to adopt advanced and innovative technological concepts within their operations.

Robust Assembly Assistance Using Informed Tree Search with Markov Chains

Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment participants, 111 factory workers, and 68 students, were used to evaluate different prediction methods. From our analysis, Markov chains fail in new scenarios and, therefore, by using an informed tree search to predict the possible next assembly step in such situations, the prediction capability of the hybrid algorithm increases significantly while providing robust solutions to unseen scenarios. The proposed method proved to be the most efficient for next assembly step prediction among all the evaluated predictors and, thus, the most suitable method for an adaptive assembly support system such as for manual operations in industry.

A Layered Middleware for OT/IT Convergence to Empower Industry 5.0 Applications

We are still in the midst of Industry 4.0 (I4.0), with more manufacturing lines being labeled as smart thanks to the integration of advanced ICT in Cyber-Physical Systems (CPS). While I4.0 aims to provision cognitive CPS systems, the nascent Industry 5.0 (I5.0) era goes a step beyond, aiming to build cross-border, sustainable, and circular value chains benefiting society as a whole. An enabler of this vision is the integration of data and AI in the industrial decision-making process, which does not exhibit yet a coordination between the Operation and Information Technology domains (OT/IT). This work proposes an architectural approach and an accompanying software prototype addressing the OT/IT convergence problem. The approach is based on a two-layered middleware solution, where each layer aims to better serve the specific differentiated requirements of the OT and IT layers. The proposal is validated in a real testbed, employing actual machine data, showing the capacity of the components to gracefully scale and serve increasing data volumes.

Integrated Circuit Board Object Detection and Image Augmentation Fusion Model Based on YOLO

Industry 4.0 has been a hot topic in recent years. The process of integrating Cyber-Physical Systems (CPS), Artificial Intelligence (AI), and Internet of Things (IoT) technology, will become the trend in future construction of smart factories. In the past, smart factories were developed around the concept of the Flexible Manufacturing System (FMS). Most parts of the quality management process still needed to be implemented by Automated Optical Inspection (AOI) methods which required human resources and time to perform second stage testing. Screening standards also resulted in the elimination of about 30% of the products. In this study, we sort and analyze several Region-based Convolutional Neural Network (R-CNN) and YOLO models that are currently more advanced and widely used, analyze the methods and development problems of the various models, and propose a suitable real-time image recognition model and architecture suitable for Integrated Circuit Board (ICB) in manufacturing process. The goal of the first stage of this study is to collect and use different types of ICBs as model training data sets, and establish a preliminary image recognition model that can classify and predict different types of ICBs based on different feature points. The second stage explores image augmentation fusion and optimization methods. The data augmentation method used in this study can reach an average accuracy of 96.53%. In the final stage, there is discussion of the applicability of the model to detect and recognize the ICB directionality in <1 s with a 98% accuracy rate to meet the real-time requirements of smart manufacturing. Accurate and instant object image recognition in the smart manufacturing process can save manpower required for testing, improve equipment effectiveness, and increase both the production capacity and the yield rate of the production line. The proposed model improves the overall manufacturing process.

Time Latency-Centric Signal Processing: A Perspective of Smart Manufacturing

Smart manufacturing employs embedded systems such as CNC machine tools, programable logic controllers, automated guided vehicles, robots, digital measuring instruments, cyber-physical systems, and digital twins. These systems collectively perform high-level cognitive tasks (monitoring, understanding, deciding, and adapting) by making sense of sensor signals. When sensor signals are exchanged through the abovementioned embedded systems, a phenomenon called time latency or delay occurs. As a result, the signal at its origin (e.g., machine tools) and signal received at the receiver end (e.g., digital twin) differ. The time and frequency domain-based conventional signal processing cannot adequately address the delay-centric issues. Instead, these issues can be addressed by the delay domain, as suggested in the literature. Based on this consideration, this study first processes arbitrary signals in time, frequency, and delay domains and elucidates the significance of delay domain over time and frequency domains. Afterward, real-life signals collected while machining different materials are analyzed using frequency and delay domains to reconfirm its (the delay domain’s) significance in real-life settings. In both cases, it is found that the delay domain is more informative and reliable than the time and frequency domains when the delay is unavoidable. Moreover, the delay domain can act as a signature of a machining situation, distinguishing it (the situation) from others. Therefore, computational arrangements enabling delay domain-based signal processing must be enacted to effectively functionalize the smart manufacturing-centric embedded systems.

Knowledge-Driven Manufacturing Process Innovation: A Case Study on Problem Solving in Micro-Turbine Machining

Micromachining techniques have been applied widely to many industrial sectors, including aerospace, automotive, and precision instruments. However, due to their high-precision machining requirements, and the knowledge-intensive characteristics of miniaturized parts, complex manufacturing process problems often hinder production. To solve these problems, a systematic scheme for structured micromachining process problem solving and an innovation support system is required. This paper presents a knowledge-based holistic framework that enables process planners to achieve micromachining innovation design. By analyzing innovation design procedures and available knowledge sources, an open multi-source Machining Process Innovation Knowledge (MPIK) acquisition paradigm is presented, including knowledge units and a knowledge network. Further, a MPIK network-driven structured process problem-solving and heuristic innovation design method was explored. Subsequently, a knowledge-driven heuristic design system for machining process innovation was integrated in the Computer-Aided Process Innovation (CAPI) platform. Finally, a case study involving specific process problem-solving and innovation scheme design for micro-turbine machining was studied to validate the proposed approach.

Ergonomics 4.0: A bibliometric review of Human Factors research in Industrial Revolution 4.0 (IR 4.0)

BACKGROUND

The scientometric study is a visualization method used to collect big data from databases, to explore the relationships between citing and co-cited documents and then visualize the results. Unlike the new term Ergonomics 4.0, bibliometric analysis has been studied in various related fields of Ergonomics.

OBJECTIVE

The aim of this study was to create a bibliometric analysis in related field of Ergonomics and Fourth Industrial Revolution. This analysis can shed light on the new developed research field in both sides of the present study, occupational ergonomics and industry 4.0.

METHODS

After selecting related keywords, Advance search was done in Scopus and Web of Science. Bibliometric results were presented by these databases' analyzer and by exported data to VOS viewer software. No time or language restriction was applied.

RESULTS

Retrieved Articles were 104 and 285 for Web of Science and Scopus respectively. The frequent co-occurrences for keywords were seen between "industry 4.0" and "Human Factors". The USA and Germany were also the most productive countries in this field. Well-known Ergonomics journals had low participation in the Evolution of Ergonomics and Fourth Industrial Revolution topics.

CONCLUSION

Due to more participation of industry 4.0-related researchers in this topic, it is recommended that ergonomists from around the world, and especially Eastern countries, attempt to conduct research in this field. Furthermore, devoting some forth-coming special issues in this field is recommended to top ergonomics journals.

Product Inspection Methodology via Deep Learning: An Overview

In this study, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. In addition, we explain the steps for building a deep-learning-based inspection system in detail. Second, we address connection schemes that efficiently link deep learning models to product inspection systems. Finally, we propose an effective method that can maintain and enhance a product inspection system according to improvement goals of the existing product inspection systems. The proposed system is observed to possess good system maintenance and stability owing to the proposed methods. All the proposed methods are integrated into a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compare and analyze the performance of the methods in various test scenarios. We expect that our study will provide useful guidelines to readers who desire to implement deep-learning-based systems for product inspection.

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing

The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely accepted framework in production and manufacturing. This data-driven knowledge discovery framework provides an orderly partition of the often complex data mining processes to ensure a practical implementation of data analytics and machine learning models. However, the practical application of robust industry-specific data-driven knowledge discovery models faces multiple data- and model development-related issues. These issues need to be carefully addressed by allowing a flexible, customized and industry-specific knowledge discovery framework. For this reason, extensions of CRISP-DM are needed. In this paper, we provide a detailed review of CRISP-DM and summarize extensions of this model into a novel framework we call Generalized Cross-Industry Standard Process for Data Science (GCRISP-DS). This framework is designed to allow dynamic interactions between different phases to adequately address data- and model-related issues for achieving robustness. Furthermore, it emphasizes also the need for a detailed business understanding and the interdependencies with the developed models and data quality for fulfilling higher business objectives. Overall, such a customizable GCRISP-DS framework provides an enhancement for model improvements and reusability by minimizing robustness-issues.

Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

Traditional manufacturing businesses lack the standards, skills, processes, and technologies to meet today's challenges of Industry 4.0 driven by an interconnected world. Enterprise Integration and Interoperability can ensure efficient communication among various services driven by big data. However, the data management challenges affect not only the technical implementation of software solutions but the function of the whole organization. In this paper, we bring together Enterprise Integration and Interoperability, Big Data Processing, and Industry 4.0 in order to identify synergies that have the potential to enable the so-called “Fourth Industrial Revolution.” On this basis, we propose an architectural framework for designing and modeling Industry 4.0 solutions for big data-driven manufacturing operations. We demonstrate the applicability of the proposed framework through its instantiation to predictive maintenance, a manufacturing function that increasingly concerns manufacturers due to the high costs, safety issues, and complexity of its application.

Digital Twin for Automatic Transportation in Industry 4.0

Industry 4.0 is the fourth industrial revolution consisting of the digitalization of processes facilitating an incremental value chain. Smart Manufacturing (SM) is one of the branches of the Industry 4.0 regarding logistics, visual inspection of pieces, optimal organization of processes, machine sensorization, real-time data adquisition and treatment and virtualization of industrial activities. Among these tecniques, Digital Twin (DT) is attracting the research interest of the scientific community in the last few years due to the cost reduction through the simulation of the dynamic behaviour of the industrial plant predicting potential problems in the SM paradigm. In this paper, we propose a new DT design concept based on external service for the transportation of the Automatic Guided Vehicles (AGVs) which are being recently introduced for the Material Requirement Planning satisfaction in the collaborative industrial plant. We have performed real experimentation in two different scenarios through the definition of an Industrial Ethernet platform for the real validation of the DT results obtained. Results show the correlation between the virtual and real experiments carried out in the two scenarios defined in this paper with an accuracy of 97.95% and 98.82% in the total time of the missions analysed in the DT. Therefore, these results validate the model created for the AGV navigation, thus fulfilling the objectives of this paper.

Towards Efficient Milling of Multi-Cavity Aeronautical Structural Parts Considering ACO-Based Optimal Tool Feed Position and Path

Cavities are typical features in aeronautical structural parts and molds. For high-speed milling of multi-cavity parts, a reasonable processing sequence planning can significantly affect the machining accuracy and efficiency. This paper proposes an improved continuous peripheral milling method for multi-cavity based on ant colony optimization algorithm (ACO). Firstly, by analyzing the mathematical model of cavity corner milling process, the geometric center of the corner is selected as the initial tool feed position. Subsequently, the tool path is globally optimized through ant colony dissemination and pheromone perception for path solution of multi-cavity milling. With the advantages of ant colony parallel search and pheromone positive feedback, the searching efficiency of the global shortest processing path is effectively improved. Finally, the milling programming of an aeronautical structural part is taken as a sample to verify the effectiveness of the proposed methodology. Compared with zigzag milling and genetic algorithm (GA)-based peripheral milling modes in the computer aided manufacturing (CAM) software, the results show that the ACO-based methodology can shorten the milling time of a sample part by more than 13%.

MOSS-Multi-Modal Best Subset Modeling in Smart Manufacturing

Smart manufacturing, which integrates a multi-sensing system with physical manufacturing processes, has been widely adopted in the industry to support online and real-time decision making to improve manufacturing quality. A multi-sensing system for each specific manufacturing process can efficiently collect the in situ process variables from different sensor modalities to reflect the process variations in real-time. However, in practice, we usually do not have enough budget to equip too many sensors in each manufacturing process due to the cost consideration. Moreover, it is also important to better interpret the relationship between the sensing modalities and the quality variables based on the model. Therefore, it is necessary to model the quality-process relationship by selecting the most relevant sensor modalities with the specific quality measurement from the multi-modal sensing system in smart manufacturing. In this research, we adopted the concept of best subset variable selection and proposed a new model called Multi-mOdal beSt Subset modeling (MOSS). The proposed MOSS can effectively select the important sensor modalities and improve the modeling accuracy in quality-process modeling via functional norms that characterize the overall effects of individual modalities. The significance of sensor modalities can be used to determine the sensor placement strategy in smart manufacturing. Moreover, the selected modalities can better interpret the quality-process model by identifying the most correlated root cause of quality variations. The merits of the proposed model are illustrated by both simulations and a real case study in an additive manufacturing (i.e., fused deposition modeling) process.

Blockchain Reference System Architecture Description for the ISA95 Compliant Traditional and Smart Manufacturing Systems

The next-generation technologies enabled by the industry 4.0 revolution put immense pressure on traditional ISA95 compliant manufacturing systems to evolve into smart manufacturing systems. Unfortunately, the transformation of old to new manufacturing technologies is a slow process. Therefore, the manufacturing industry is currently in a situation that the legacy and modern manufacturing systems share the same factory environment. This heterogeneous ecosystem leads to challenges in systems scalability, interoperability, information security, and data quality domains. Our former research effort concluded that blockchain technology has promising features to address these challenges. Moreover, our systematic assessment revealed that most of the ISA95 enterprise functions are suitable for applying blockchain technology. However, no blockchain reference architecture explicitly focuses on the ISA95 compliant traditional and smart manufacturing systems available in the literature. This research aims to fill the gap by first methodically specifying the design requirements and then meticulously elaborating on how the reference architecture components fulfill the design requirements.

Switchable Glass Enabled Contextualization for a Cyber-Physical Safe and Interactive Spatial Augmented Reality PCBA Manufacturing Inspection System

Augmented reality (AR) has been demonstrated to improve efficiency by up to thrice the level of traditional methods. Specifically, the adoption of visual AR is performed widely using handheld and head-mount technologies. Despite spatial augmented reality (SAR) addressing several shortcomings of wearable AR, its potential is yet to be fully explored. To date, it enhances the cooperation of users with its wide field of view and supports hands-free mobile operation, yet it has remained a challenge to provide references without relying on restrictive static empty surfaces of the same object or nearby objects for projection. Towards this end, we propose a novel approach that contextualizes projected references in real-time and on demand, onto and through the surface across a wireless network. To demonstrate the effectiveness of the approach, we apply the method to the safe inspection of printed circuit board assembly (PCBA) wirelessly networked to a remote automatic optical inspection (AOI) system. A defect detected and localized by the AOI system is wirelessly remitted to the proposed remote inspection system for prompt guidance to the inspector by augmenting a rectangular bracket and a reference image. The rectangular bracket transmitted through the switchable glass aids defect localization over the PCBA, whereas the image is projected over the opaque cells of the switchable glass to provide reference to a user. The developed system is evaluated in a user study for its robustness, precision and performance. Results indicate that the resulting contextualization from variability in occlusion levels not only positively affect inspection performance but also supersedes the state of the art in user preference. Furthermore, it supports a variety of complex visualization needs including varied sizes, contrast, online or offline tracking, with a simple robust integration requiring no additional calibration for registration.

Process Control and Energy Efficiency

We review the impact of control systems and strategies on the energy efficiency of chemical processes. We show that, in many ways, good control performance is a necessary but not sufficient condition for energy efficiency. The direct effect of process control on energy efficiency is manyfold: Reducing output variability allows for operating chemical plants closer to their limits, where the energy/economic optima typically lie. Further, good control enables novel, transient operating strategies, such as conversion smoothing and demand response. Indirectly, control systems are key to the implementation and operation of more energy-efficient plant designs, as dictated by the process integration and intensification paradigms. These conclusions are supported with references to numerous examples from the literature.

Low-Cost Piezoelectric Sensors for Time Domain Load Monitoring of Metallic Structures During Operational and Maintenance Processes

The versatility of piezoelectric sensors in measurement techniques and their performance in applications has given rise to an increased interest in their use for structural and manufacturing component monitoring. They enable wireless and sensor network solutions to be developed in order to directly integrate the sensors into machines, fixtures and tools. Piezoelectric sensors increasingly compete with strain-gauges due to their wide operational temperature range, load and strain sensing accuracy, low power consumption and low cost. This research sets out the use of piezoelectric sensors for real-time monitoring of mechanical strength in metallic structures in the ongoing operational control of machinery components. The behaviour of aluminium and steel structures under flexural strength was studied using piezoelectric sensors. Variations in structural behaviour and geometry were measured, and the load and μstrains during operational conditions were quantified in the time domain at a specific frequency. The lead zirconium titanate (PZT) sensors were able to distinguish between material types and thicknesses. Moreover, this work covers frequency selection and optimisation from 20 Hz to 300 kHz. Significant differences in terms of optimal operating frequencies and sensitivity were found in both structures. The influence of the PZT voltage applied was assessed to reduce power consumption without signal loss, and calibration to μstrains and loads was performed.

Defining Near-Term to Long-Term Research Opportunities to Advance Metrics, Models, and Methods for Smart and Sustainable Manufacturing

Over the past century, research has focused on continuously improving the performance of manufacturing processes and systems-often measured in terms of cost, quality, productivity, and material and energy efficiency. With the advent of smart manufacturing technologies-better production equipment, sensing technologies, computational methods, and data analytics applied from the process to enterprise levels-the potential for sustainability performance improvement is tremendous. Sustainable manufacturing seeks the best balance of a variety of performance measures to satisfy and optimize the goals of all stakeholders. Accurate measures of performance are the foundation on which sustainability objectives can be pursued. Historically, operational and information technologies have undergone disparate development, with little convergence across the domains. To focus future research efforts in advanced manufacturing, the authors organized a one-day workshop, sponsored by the U.S. National Science Foundation, at the joint manufacturing research conferences of the American Society of Mechanical Engineers and Society of Manufacturing Engineers. Research needs were identified to help harmonize disparate manufacturing metrics, models, and methods from across conventional manufacturing, nanomanufacturing, and additive/hybrid manufacturing processes and systems. Experts from academia and government labs presented invited lightning talks to discuss their perspectives on current advanced manufacturing research challenges. Workshop participants also provided their perspectives in facilitated brainstorming breakouts and a reflection activity. The aim was to define advanced manufacturing research and educational needs for improving manufacturing process performance through improved sustainability metrics, modeling approaches, and decision support methods. In addition to these workshop outcomes, a review of the recent literature is presented, which identifies research opportunities across several advanced manufacturing domains. Recommendations for future research describe the short-, mid-, and long-term needs of the advanced manufacturing community for enabling smart and sustainable manufacturing.

A Case about the Upgrade of Manufacturing Equipment for Insertion into an Industry 4.0 Environment

Industry 4.0 is a synonym for the confluence of technologies that allows the integration of information technology, data science, and automated equipment, to produce smart industrial systems. The process of inserting new technologies into current conventional environments involves a wide range of disciplines and approaches. This article presents the process that was followed to identify and upgrade one station in an industrial workshop to make it compatible with the more extensive system as it evolves into the Industry 4.0 environment. An information processing kit was developed to upgrade the equipment from an automated machine to an Industry 4.0 station. The kit includes a structure to support the sensor and the data processing unit; this unit consisted of a minicomputer that records the data, graded the performance of the components, and sent the data to the cloud for storage, reporting, and further analysis. The information processing kit allowed the monitoring of the inspection system and improved the quality and speed of the inspection process.

Task Scheduling Based on a Hybrid Heuristic Algorithm for Smart Production Line with Fog Computing

Fog computing provides computation, storage and network services for smart manufacturing. However, in a smart factory, the task requests, terminal devices and fog nodes have very strong heterogeneity, such as the different task characteristics of terminal equipment: fault detection tasks have high real-time demands; production scheduling tasks require a large amount of calculation; inventory management tasks require a vast amount of storage space, and so on. In addition, the fog nodes have different processing abilities, such that strong fog nodes with considerable computing resources can help terminal equipment to complete the complex task processing, such as manufacturing inspection, fault detection, state analysis of devices, and so on. In this setting, a new problem has appeared, that is, determining how to perform task scheduling among the different fog nodes to minimize the delay and energy consumption as well as improve the smart manufacturing performance metrics, such as production efficiency, product quality and equipment utilization rate. Therefore, this paper studies the task scheduling strategy in the fog computing scenario. A task scheduling strategy based on a hybrid heuristic (HH) algorithm is proposed that mainly solves the problem of terminal devices with limited computing resources and high energy consumption and makes the scheme feasible for real-time and efficient processing tasks of terminal devices. Finally, the experimental results show that the proposed strategy achieves superior performance compared to other strategies.

Standards-based Semantic Integration of Manufacturing Information: Past, Present, and Future

Service-oriented architecture (SOA) has been identified as a key to enabling the emerging manufacturing paradigms such as smart manufacturing, Industrie 4.0, and cloud manufacturing where things (i.e., various kinds of devices and software systems) from heterogeneous sources have to be dynamically connected. Data exchange standards are playing an increasingly important role to reduce risks associated with investments in these Industrial Internet of Things (IIoT) and adoptions of those emerging manufacturing paradigms. This paper looks back into the history of the standards for carrying the semantics of data across systems (or things), how they are developed, maintained, and represented, and then presents an insight into the current trends. In particular, the paper discusses the emerging move in data exchange standards practices toward model-based development and usage. We present functional requirements for a system supporting the model-based approach and conclude with implications and future directions.

Vision-Based Apple Classification for Smart Manufacturing

Smart manufacturing enables an efficient manufacturing process by optimizing production and product transaction. The optimization is performed through data analytics that requires reliable and informative data as input. Therefore, in this paper, an accurate data capture approach based on a vision sensor is proposed. Three image recognition methods are studied to determine the best vision-based classification technique, namely Bag of Words (BOW), Spatial Pyramid Matching (SPM) and Convolutional Neural Network (CNN). The vision-based classifiers categorize the apple as defective and non-defective that can be used for automatic inspection, sorting and further analytics. A total of 550 apple images are collected to test the classifiers. The images consist of 275 non-defective and 275 defective apples. The defective category includes various types of defect and severity. The vision-based classifiers are trained and evaluated according to the K-fold cross-validation. The performances of the classifiers from 2-fold, 3-fold, 4-fold, 5-fold and 10-fold are compared. From the evaluation, SPM with SVM classifier attained 98.15% classification accuracy for 10-fold and outperformed the others. In terms of computational time, CNN with SVM classifier is the fastest. However, minimal time difference is observed between the computational time of CNN and SPM, which were separated by only 0.05 s.

Contextualising manufacturing data for lifecycle decision-making

Recent advances enable data from manufacturing systems to be captured and contextualised relative to other phases of the product lifecycle, a necessary step toward understanding system behaviour and satisfying traceability requirements. Significant challenges remain for integrating information across the lifecycle and enabling efficient decision-making. In this paper, we explore opportunities for mapping standard data representations, such as the Standard for the Exchange of Product Data (STEP), MTConnect, and the Quality Information Framework (QIF) to integrate information silos existing across the lifecycle. To demonstrate this vision, we describe a reference implementation with a contract manufacturer in the National Institute of Standards and Technology (NIST) Smart Manufacturing Systems Test Bed. Using this implementation, we explore how knowledge generated from manufacturing can support lifecycle decision-making. As a case study, we then present an interactive prototype correlating the test bed's data based on the context that must be provided for a specific decision-making viewpoint.

An Investigation to Manufacturing Analytical Services Composition using the Analytical Target Cascading Method

As cloud computing is increasingly adopted, the trend is to offer software functions as modular services and compose them into larger, more meaningful ones. The trend is attractive to analytical problems in the manufacturing system design and performance improvement domain because 1) finding a global optimization for the system is a complex problem; and 2) sub-problems are typically compartmentalized by the organizational structure. However, solving sub-problems by independent services can result in a sub-optimal solution at the system level. This paper investigates the technique called Analytical Target Cascading (ATC) to coordinate the optimization of loosely-coupled sub-problems, each may be modularly formulated by differing departments and be solved by modular analytical services. The result demonstrates that ATC is a promising method in that it offers system-level optimal solutions that can scale up by exploiting distributed and modular executions while allowing easier management of the problem formulation.

Smart Manufacturing

Historic manufacturing enterprises based on vertically optimized companies, practices, market share, and competitiveness are giving way to enterprises that are responsive across an entire value chain to demand dynamic markets and customized product value adds; increased expectations for environmental sustainability, reduced energy usage, and zero incidents; and faster technology and product adoption. Agile innovation and manufacturing combined with radically increased productivity become engines for competitiveness and reinvestment, not simply for decreased cost. A focus on agility, productivity, energy, and environmental sustainability produces opportunities that are far beyond reducing market volatility. Agility directly impacts innovation, time-to-market, and faster, broader exploration of the trade space. These changes, the forces driving them, and new network-based information technologies offering unprecedented insights and analysis are motivating the advent of smart manufacturing and new information technology infrastructure for manufacturing.