A method of NC machine tools intelligent monitoring system in smart factories

The construction of effectual connection to bridge the gap between physical machine tools and upper software applications is one of the inherent requirements for smart factories. The difficulties in this issue lies in the lack of effective and appropriate means for real-time data acquisition, storage and processing in monitoring and the post workflows. The rapid advancements in Internet of things (IoT) and information technology have made it possible for the realization of this scheme, which have become an important module of the concepts such as “Industry 4.0”, etc. In this paper, a framework of bi-directional data and control flows between various machine tools and upper-level software system is proposed, within which several key stumbling blocks are presented, and corresponding solutions are subsequently deeply investigated and analyzed. Through monitoring manufacturing big data, potential essential information are extracted, providing useful guides for practical production and enterprise decision-making. Based on the integrated model, an NC machine tool intelligent monitoring and data processing system in smart factories is developed. Typical machine tools, such as Siemens series, are the main objects for investigation. The system validates the concept and performs well in the complex manufacturing environment, which will be a beneficial attempt and gain its value in smart factories.     

Hidden Markov model-based autonomous manufacturing task orchestration in smart shop floors

Smart manufacturing requires flexible production organization and management to handle the dynamic customer requirements rapidly and efficiently. In the context of smart manufacturing, work-in-progress (WIP), machines, and other physical resources in smart shop floors are endowed with intelligence, such as self-perception and self-decision-making. In this situation, the manufacturing task orchestration in such smart shop floors becomes autonomous, which is different from the traditional one that is centrally set and managed. The manufacturing tasks are accomplished with the help of autonomous communication between the WIP and the machines. This paper firstly clarifies the logic of autonomous manufacturing, in which the core idea is the autonomous communication and collaboration between the WIP and the machines during production. Furthermore, the autonomous manufacturing task orchestration (AMTO) problem is described. An improved hidden Markov model (HMM) is proposed to formulate the problem and generate an optimal AMTO solution for a certain process flow. A demonstrative case is implemented to verify the feasibility of the proposed model and method. The results show that HMM can give suggestions on AMTO and dynamically adjust the situation based on the real-time manufacturing data.     

Edge-cloud cooperation driven self-adaptive exception control method for the smart factory

Production exceptions often occur due to the uncertainties of resources such as random machine broken, urgent production tasks. The timely identification and optimal control of the production exceptions are the core issues to ensure the reliable operation of smart manufacturing system. However, due to the evolution mechanism of uncertain factor is hard to describe, and the current cloud-based production analysis and decision-making mode often requires a long processing time. As a result, the extraction of abnormal events often delays and the optimal control decisions are hard to made. To meet this issue, an edge-cloud cooperation driven self-adaptive exception control method is proposed for the smart factory. Firstly, the advanced industrial Internet of things and embedded edge-computing technologies are applied to create an intelligent environment where the resources are made smart with the ability of self-sensing, self-analytics and self-optimization. Then, the fuzzy Bayesian network is used to extract the production exceptions and diagnose their causes, where fuzzy sets are used to describe the production status. After that, a self-adaptive production exception handling method is involved through an edge-cloud cooperation mechanism. The machines will adjust their production parameters at first, and then the horizontal collaboration among machines at same production level and vertical cooperation between different production levels will be used to deal with exceptions self-adaptively. Lastly, a case from casting post-processing system is used to demonstrate the proposed method, and results show that the proposed method can largely improve the production efficiency.     

An IIoT-driven and AI-enabled framework for smart manufacturing system based on three-terminal collaborative platform

Smart manufacturing has great potential in the development of network collaboration, mass personalised customisation, sustainability and flexibility. Customised production can better meet the dynamic user needs, and network collaboration can significantly improve production efficiency. Industrial internet of things (IIoT) and artificial intelligence (AI) have penetrated the manufacturing environment, improving production efficiency and facilitating customised and collaborative production. However, these technologies are isolated and dispersed in the applications of machine design and manufacturing processes. It is a challenge to integrate AI and IIoT technologies based on the platform, to develop autonomous connect manufacturing machines (ACMMs), matching with smart manufacturing and to facilitate the smart manufacturing services (SMSs) from the overall product life cycle. This paper firstly proposes a three-terminal collaborative platform (TTCP) consisting of cloud servers, embedded controllers and mobile terminals to integrate AI and IIoT technologies for the ACMM design. Then, based on the ACMMs, a framework for SMS to generate more IIoT-driven and AI-enabled services is presented. Finally, as an illustrative case, a more autonomous engraving machine and a smart manufacturing scenario are designed through the above-mentioned method. This case implements basic engraving functions along with AI-enabled automatic detection of broken tool service for collaborative production, remote human-machine interface service for customised production and network collaboration, and energy consumption analysis service for production optimisation. The systematic method proposed can provide some inspirations for the manufacturing industry to generate SMSs and facilitate the optimisation production and customised and collaborative production.     

A novel methodology for cross-technology interoperability in CNC machining

In CNC part programmes, the lack of standardisation for representing part geometry and semantics of manufacturing operations leads to the necessity for existence of a unique part programme for each machine. Generating multiple programmes for producing the same part is not a value adding activity and is very time consuming. This wasteful activity can be eliminated if users are given the ability to write an NC program for a specific machine and robustly convert the program to syntax suitable for another CNC machine with a different structure. This, cross-technology interoperability, would enable for parts manufactured on old CNC machines using legacy code to be manufactured on new CNC machines by automatically converting the programmes. Every NC programme is written based on various categories of information such as: cutting tool specifications, process planning knowledge and machine tool information. This paper presents an approach for cross-technology interoperability by refining high-level process information (i.e., geometric features on the part and embedded manufacturing resource data) from NC programmes. These refined items of information stored in compliance with the ISO14649 (STEP-NC) standard may then be combined with new manufacturing resource information to generate NC code in a format that is compatible with machines based on different technologies. The authors provide a framework for this process of identification, semantic interpretation and re-integration of information. The focus of this paper is on asymmetric rotational components as the initial application area. To demonstrate the proposed cross-technology interoperability approach, a C-axis CNC turn–mill machine and a 4 axis CNC machining centre have been used with a simple test component.     

Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation

Digital twin (DT) and artificial intelligence (AI) technologies are powerful enablers for Industry 4.0 toward sustainable resilient manufacturing. Digital twins of machine tools and machining processes combine advanced digital techniques and production domain knowledge, facilitate the enhancement of agility, traceability, and resilience of production systems, and help machine tool builders achieve a paradigm shift from one-time products provision to on-going service delivery. However, the adaptability and accuracy of digital twins at the shopfloor level are restricted by heterogeneous data sources, modeling precision as well as uncertainties from dynamical industrial environments. This article proposes a novel modeling framework to address these inadequacies by in-depth integrating AI techniques and machine tool expertise using aggregated data along the product development process. A data processing procedure is constructed to contextualize metadata sources from the design, planning, manufacturing, and quality stages and link them into a digital thread. On this consistent data basis, a modeling pipeline is presented to incorporate production and machine tool prior knowledge into AI development pipeline, while considering the multi-fidelity nature of data sources in dynamic industrial circumstances. In terms of implementation, we first introduce our existing work for building digital twins of machine tool and manufacturing process. Within this infrastructure, we developed a hybrid learning-based digital twin for manufacturing process following proposed modeling framework and tested it in an external industrial project exemplarily for real-time workpiece quality monitoring. The result indicates that the proposed hybrid learning-based digital twin enables learning uncertainties of the interaction of machine tools and machining processes in real industrial environments, thus allows estimating and enhancing the modeling reliability, depending on the data quality and accessibility. Prospectively, it also contributes to the reparametrization of model parameters and to the adaptive process control.     

A human-in-the-loop manufacturing control architecture for the next generation of production systems

In recent years, the introduction of Industry 4.0 technologies in the manufacturing landscape promoted the development of smart factories characterised by relevant socio-technical interactions between humans and machines. In this context, understanding and modelling the role of humans turns out to be crucial to develop efficient manufacturing systems of the future. Grounding on previous researches in the field of Human-in-the-Loop and Human Cyber-Physical Systems, the paper aims at contributing to a deep reflection about human-machine interaction in the wider perspective of Social Human-in-the-Loop Cyber-Physical Production Systems, in which more agents collaborate and are socially connected. After presenting an evolution of manufacturing control organisations, an architecture to depict social interactions in smart factories is proposed. The proposed architecture contributes to the representation of different human roles in the smart factory and the exploration of both hierarchical and heterarchical data-driven decision-making processes in manufacturing.     

An autonomous manufacturing system based on swarm of cognitive agents

Disruptions of the manufacturing systems caused by the disturbances such as the tool wear, machine breakdown, malfunction of robot or transporter, and so on reduce the productivity as well as increase the cost of product. The conventional manufacturing systems are unable to face with the disturbances by their rigid structure. These systems should be stopped when the disturbances occur. The paper presents an Autonomous Manufacturing System based on Swarm of Cognitive Agents (AMS-SCA) in order to adapt to the disturbances. In the AMS-SCA, the manufacturing system is considered as a swarm of cognitive agents where work-pieces, machines, robots, and transporters are controlled by the corresponding cognitive agents. The system reacts to disturbances autonomously based on the reaction of each agent or the cooperation among them. To develop the AMS-SCA, the disturbances happened in the machining shop were analyzed to find out the corresponding management methods. A test-bed was implemented to prove the functionality of the proposed AMS-SCA.     

Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.     

Modelling adaptive multi-agent manufacturing control with discrete event system formalism

This paper introduces an approach for modelling and designing multi-agent control architectures for agile manufacturing using a generic formalism based on a system-theoretic discrete event approach. To describe the details of the modelling strategy, we apply the proposed approach to a multi-agent network for Job flow control in a manufacturing plant. Two interacting types of autonomous controllers, Part Agents and Machine Agents, are in charge of controlling the part flow and the machine processing sequences. Both type of agents are first modelled as atomic discrete event systems and subsequently integrated in the model of the entire network of autonomous controllers. To improve the performance of the network of agents, we introduce a mechanism based on evolutionary algorithms adapting the agents’ decision laws that are encapsulated in agents’ states. Through network simulation, the algorithm continuously searches for effective decision laws, consequently adapting agent's behaviour to the current operational conditions of the manufacturing floor. Simulation results show the potentialities of the approach.     

A machine tool matching method in cloud manufacturing using Markov Decision Process and cross-entropy

Cloud Manufacturing (CMfg) is a state-of-the-art manufacturing paradigm implementing the concept of service-oriented manufacturing. Machine tools are one kind of the critical manufacturing resources in Cloud Manufacturing, however machine tool matching is still immature owning to customization manufacturing service demands from users and various disturbing factors in production. This paper proposes a machine tool matching method for dealing with a single Cloud Manufacturing task with complex machine tool application demands. In this method, the demands of machine tools and themselves are described and evaluated based on a universal framework to obtain candidate resource groups satisfying local requirements of sub-demands. Then, a series of Markov Decision Processes (MDP) is established, which take the minimal service cost as optimal object to meet global requirements, and a cross-entropy based algorithm is used to solve the optimal object. Finally, simulation experiments are conducted to validate the usability and superiority in efficiency of the proposed method.     

Tool wear assessment based on type-2 fuzzy uncertainty estimation on acoustic emission

In modern manufacturing industry, developing automated tool condition monitoring system become more and more import in order to transform manufacturing systems from manually operated production machines to highly automated machining centres. This paper presents a nouvelle cutting tool wear assessment in high precision turning process using type-2 fuzzy uncertainty estimation on acoustic Emission. Without understanding the exact physics of the machining process, type-2 fuzzy logic system identifies acoustic emission signal during the process and its interval set of output assesses the uncertainty information in the signal. The experimental study shows that the development trend of uncertainty in acoustic emission signal corresponds to that of cutting tool wear. The estimation of uncertainties can be used for proving the conformance with specifications for products or auto-controlling of machine system, which has great meaning for continuously improvement in product quality, reliability and manufacturing efficiency in machining industry.     

Analytic network process decision-making to assess slicing machine in terms of precision and control wafer quality

We discuss and develop a manufacturing quality yield model to forecast the 12 in silicon wafer slicing based on an analytic network process (ANP) framework. The ANP is a general theory of relative measurement used to derive composite-priority-ratio scales from individual-ratio scales that represent the relative influence of factors that interact with respect to the control criteria. Through its supermatrix, which is composed of matrices of column priorities, the ANP framework captures the outcome of dependence and feedback within and between clusters of factors. Additionally, the proposed algorithm can select the evaluation outcomes to identify the optimal machine of precision. Finally, results of the EWMA control chart and Process Capability Indices demonstrate the feasibility of the proposed ANP-based algorithm in effectively selecting the evaluation outcomes and in evaluating the precision of the optimal performing machines. We illustrate how the ANP model implemented for helping the engineer can find out the manufacturing process yield quickly and effectively.     

An intelligent simulation environment for manufacturing systems

The manufacturing field is an area where the application of simulation is an essential tool for validating methods and architectures before applying them on the factory floor. Despite the fact that there are a great number of simulation tools, most of them do not take into account the specific requirements of the “new manufacturing era” such as distributed organization, interoperability, cooperation, scalability, fault tolerance and agility. On the other hand, Multiagent System technology has demonstrated its utility in manufacturing system modeling and implementation. Agenthood features such as proactivity, reactivity, and sociability may also be useful for associating them with the specific simulation needs of the new changing requirements for manufacturing systems. In this paper, an Agent-supported Simulation Environment for intelligent manufacturing systems is presented. The different roles that are played by the agents of the simulation environment are defined taking into account the specific dynamic features in manufacturing simulation and the requirements of the new manufacturing era. Moreover, the interaction and cooperation scenarios among these agents are specified to facilitate manufacturing simulation in an appropriate and flexible way. A detailed evaluation study, with regards to the new manufacturing era requirements, demonstrates the advantages of the proposed approach over current state-of-the-art proposals.     

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.     

Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model

In a high speed milling operation the cutting tool acts as a backbone of machining process, which requires timely replacement to avoid loss of costly workpiece or machine downtime. To this aim, prognostics is applied for predicting tool wear and estimating its life span to replace the cutting tool before failure. However, the life span of cutting tools varies between minutes or hours, therefore time is critical for tool condition monitoring. Moreover, complex nature of manufacturing process requires models that can accurately predict tool degradation and provide confidence for decisions. In this context, a data-driven connectionist approach is proposed for tool condition monitoring application. In brief, an ensemble of Summation Wavelet-Extreme Learning Machine models is proposed with incremental learning scheme. The proposed approach is validated on cutting force measurements data from Computer Numerical Control machine. Results clearly show the significance of our proposition.     

Digital twin-based smart assembly process design and application framework for complex products and its case study

With rapid advances in new generation information technologies, digital twin (DT), and cyber-physical system, smart assembly has become a core focus for intelligent manufacturing in the fourth industrial evolution. Deep integration between information and physical worlds is a key phase to develop smart assembly process design that bridge the gap between product assembly design and manufacturing. This paper presents a digital twin reference model for smart assembly process design, and proposes an application framework for DT-based smart assembly with three layers. Product assembly station components are detailed in the physical space layer; two main modules, communication connection and data processing, are introduced in the interaction layer; and we discuss working mechanisms of assembly process planning, simulation, predication, and control management in the virtual space layer in detail. A case study shows the proposed approach application for an experimental simplified satellite assembly case using the DT-based assembly application system (DT-AAS) to verify the proposed application framework and method effectiveness.     

Process control in CNC manufacturing for discrete components: A STEP-NC compliant framework

With today's highly competitive global manufacturing marketplace, the pressure for right-first-time manufacture has never been so high. New emerging data standards combined with machine data collection methods, such as in-process verification lead the way to a complete paradigm shift from the traditional manufacturing and inspection to intelligent networked process control. Low-level G and M codes offer very limited information on machine capabilities or work piece characteristics which consequently, results in no information being available on manufacturing processes, inspection plans and work piece attributes in terms of tolerances, etc. and design features to computer numerically controlled (CNC) machines. One solution to the aforementioned problems is using STEP-NC (ISO 14649) suite of standards, which aim to provide higher-level information for process control. In this paper, the authors provide a definition for process control in CNC manufacturing and identify the challenges in achieving process control in current CNC manufacturing scenario. The paper then introduces a STEP-compliant framework that makes use of self-learning algorithms that enable the manufacturing system to learn from previous data and results in eliminating the errors and consistently producing quality products. The framework relies on knowledge discovery methods such as data mining encapsulated in a process analyser to derive rules for corrective measures to control the manufacturing process. The design for the knowledge-based process analyser and the various process control mechanisms conclude the paper.     

Semantic communications between distributed cyber-physical systems towards collaborative automation for smart manufacturing

Machine-to-machine (M2M) communication is a crucial technology for collaborative manufacturing automation in the Industrial Internet of Things (IIoT)-empowered industrial networks. The new decentralized manufacturing automation paradigm features ubiquitous communication and interoperable interactions between machines. However, peer-to-peer (P2P) interoperable communications at the semantic level between industrial machines is a challenge. To address this challenge, we introduce a concept of Semantic-aware Cyber-Physical Systems (SCPSs) based on which manufacturing devices can establish semantic M2M communications. In this work, we propose a generic system architecture of SCPS and its enabling technologies. Our proposed system architecture adds a semantic layer and a communication layer to the conventional cyber-physical system (CPS) in order to maximize compatibility with the diverse CPS implementation architecture. With Semantic Web technologies as the backbone of the semantic layer, SCPSs can exchange semantic messages with maximum interoperability following the same understanding of the manufacturing context. A pilot implementation of the presented work is illustrated with a proof-of-concept case study between two semantic-aware cyber-physical machine tools. The semantic communication provided by the SCPS architecture makes ubiquitous M2M communication in a network of manufacturing devices environment possible, laying the foundation for collaborative manufacturing automation for achieving smart manufacturing. Another case study focusing on decentralized production control between machines in a workshop also proved the merits of semantic-aware M2M communication technologies.