Smart Shop Floor Research Articles

A new data-driven production scheduling method based on digital twin for smart shop floors

As a mainstream means for solving smart shop floor production scheduling problems, the data-driven scheduling method has gained considerable attention in recent years. However, extant studies have primarily utilized physical shop floor data with limited quantity and quality to train scheduling models, which suffer from the drawbacks of long training time and poor scheduling performance. Therefore, this study proposes a new data-driven scheduling method based on digital twin for smart shop floors, which utilizes the data from physical shop floor and digital shop floor constructed by digital twin to train scheduling models. Specifically, in this method, a model-level data fusion mechanism is designed to achieve the fusion and complementary advantages of these two types of data, thus providing sufficient and high-quality data support for high-precision model training. Additionally, a multi-layer feedforward neural network with a generative adversarial network-based sample expansion mechanism is further integrated to efficiently generate scheduling decisions. Experiments in a semiconductor production shop floor are conducted to confirm the effectiveness of the proposed method.

Double deep Q-network-based self-adaptive scheduling approach for smart shop floor

Double deep Q-network-based self-adaptive scheduling approach for smart shop floor

Reduction of variability in a smart shop floor using discrete event simulation

Reduction of variability in a smart shop floor using discrete event simulation

Hierarchical Reinforcement Learning for Multi-Objective Real-Time Flexible Scheduling in a Smart Shop Floor

With the development of intelligent manufacturing, machine tools are considered the “mothership” of the equipment manufacturing industry, and the associated processing workshops are becoming more high-end, flexible, intelligent, and green. As the core of manufacturing management in a smart shop floor, research into the multi-objective dynamic flexible job shop scheduling problem (MODFJSP) focuses on optimizing scheduling decisions in real time according to changes in the production environment. In this paper, hierarchical reinforcement learning (HRL) is proposed to solve the MODFJSP considering random job arrival, with a focus on achieving the two practical goals of minimizing penalties for earliness and tardiness and reducing total machine load. A two-layer hierarchical architecture is proposed, namely the combination of a double deep Q-network (DDQN) and a dueling DDQN (DDDQN), and state features, actions, and external and internal rewards are designed. Meanwhile, a personal computer-based interaction feature is designed to integrate subjective decision information into the real-time optimization of HRL to obtain a satisfactory compromise. In addition, the proposed HRL framework is applied to multi-objective real-time flexible scheduling in a smart gear production workshop, and the experimental results show that the proposed HRL algorithm outperforms other reinforcement learning (RL) algorithms, metaheuristics, and heuristics in terms of solution quality and generalization and has the added benefit of real-time characteristics.

Design of intelligent manufacturing system based on digital twin for smart shop floors

ABSTRACT The operation and management of smart shop floors depends on the integration and interaction of various information systems. In this paper, a framework of intelligent manufacturing system based on digital twin (DT-IMS) is designed for smart shop floors. Intelligent manufacturing system is composed of DT layer, process layer (Computer Aided Design and Computer Aided Process Planning), management layer, (Product Lifecycle Management), planning and scheduling layer (Enterprise Resource Planning and Manufacturing Execution System), perception layer (Supervisory Control And Data Acquisition), control layer (Programmable Logic Controller and Distributed Control System) and equipment layer. A digital twin model with high fidelity is established from the multidimensional dimension of geometry, physics, movement, behavior, rule, constraint and communication. Then, utilizing the historical data of each layer of information systems and the real-time data collected by industrial Internet of Things technology drive the digital twin model for simulation to realize the real-time function of process, production planning and scheduling and monitoring of the smart shop floor. The results show that the implementation of DT-IMS in an industrial company illustrates the potential advantages for process design, production planning and scheduling, monitoring and control with reduced complexity and uncertainty of the shop floor.

Development of a Data-Driven Decision-Making System Using Lean and Smart Manufacturing Concept in Industry 4.0: A Case Study

Nowadays, industries are emphasizing the implementation of a smart shop floor management method because of different types of problems faced in controlling the production activities in Industry 4.0. Several shop floor management methods are currently implemented in the present Industry 4.0 scenario, including lean manufacturing, logistics, Internet of things, smart manufacturing, cyber-physical system, and artificial intelligence. The present research work is focused on the development and Taguchi validation methodology of a data-driven decision-making system using L9 orthogonal array for smart shop floor management based on the relationship between production sustainability and constraints. The proposed system has been validated by a comprehensive investigation of a case of mining machinery manufacturing unit. The result of the investigation revealed that productivity has been enhanced by effective controlling of production activities on the shop floor. Taguchi L9 orthogonal array method of design of experiments is implemented to enhance flexibility for shop floor control and meanwhile minimize the production time due to inefficient operating conditions on the shop floor. Taguchi method was implemented for critical conditions affecting production lead time and resource utilization. The authors have detailed discussion on developing present novel hybrid integration of lean and smart manufacturing approaches to enhance operational excellence in production activities and other complicated manufacturing environment on the shop floor within available resources. The present finding demonstrates that the adopted digital technologies under smart manufacturing with lean manufacturing are found to be cost-effective approach under different environmental conditions. The proposed system has significantly improved the efficiency of production management and operational performance by using smart systems and has proved effective in improving the financial position by making a safer shop floor management approach. In this article, a robust problem-solving system is provided. The present work aims to introduce revolutionary methods for Industry 4.0 that would result in productivity enhancement and beneficial impact on industry persons by improving the smart shop floor management. The study also provides valuable perspective and sustainable guidelines to facilitate industry individuals to implement lean and smart manufacturing for productivity enhancement in the production environment of Industry 4.0.

A data-driven scheduling knowledge management method for smart shop floor

ABSTRACT The data-driven method has been widely used to mine knowledge from the smart shop floors’ production data to guide dynamic scheduling. However, the mined knowledge may be invalid when the production scene changes. In order to address this problem and ensure the validity of the knowledge, this paper studies a data-driven scheduling knowledge life-cycle management (SKLM) method for the smart shop floor. The proposed method includes four phases: knowledge generation, knowledge application, online knowledge evaluation, and knowledge update. Specifically, the extreme learning machine (ELM) is applied to learn knowledge based on the composite scheduling rules. The quality control theory is used to evaluate the quality of scheduling knowledge. And the online sequential ELM (OS-ELM) is adopted to update the knowledge. Knowledge life-cycle management is implemented through the iterative knowledge update. The proposed method is validated on the MIMAC6, which is a simulation model of the semiconductor production line. Experimental results show that the proposed method could improve the effectiveness of scheduling knowledge and further optimize the performance of the smart shop floor.

A case study of intelligent manufacturing for key components of vehicles

Currently, most research on intelligent manufacturing stays at the theoretical stage, which can enhance the understanding of smart factory concepts, but it is still difficult to establish a clear strategy for the industry. In this research, cyber-physical system (CPS) is applied to connect the manufacturing equipment of the production system, so as to develop the internet of things (IoT) object layer. Furthermore, a smart factory framework consisting of IoT object layer, CPS network layer, and CPS service layer is developed, connecting the smart shop-floor and the cloud service platform through the industrial network so as to implement the integration of physical manufacturing and virtual manufacturing. This framework is applied in the production of vehicle components, and the results show that the application of this framework in an actual automobile chassis smart factory can greatly improve production efficiency and energy efficiency, reduce enterprise costs, and shorten product development cycles, accelerating the process of enterprise transformation to informatization, digitalization, and intelligence. Since this framework for intelligent manufacturing has been verified under actual conditions, it can be taken as a reference for further development of the framework for intelligent manufacturing.

A framework for planning and scheduling shop floor logistics via cloud-edge collaboration

Under the autonomous production mode, the production and operation process of smart shop-floor is operated by manufacturing units themselves. This paper describes a new framework for the order-driven products processing based on cloud-edge collaboration, in which the production-logistics tasks are planned and scheduled by the manufacturing resources at the bottom of smart shop-floor. The key enabled technologies and roadmap are further pointed out. The core idea of autonomous production mode is the autonomous communication and collaboration through an industrial network between all physical resources during production in a shop-floor. It is intended to serve as a reference for the managers while handling the flexible production organization and management based on the dynamic customer requirements.

A cyber-physical production monitoring service system for energy-aware collaborative production monitoring in a smart shop floor

To satisfy the varied requirements of customers and improve their sense of participation, manufacturers collaborate with equipment providers and customers to produce customer-centered individualized products. This can be achieved with the aid of new information technologies such as Cyber-Physical System (CPS) and advanced business models such as Product-Service System (PSS). However, how to incorporate CPS and PSS into the shop floor-level production system to support service-oriented collaborative manufacturing operations such as production monitoring is a vital issue. This work proposes a Cyber-Physical Production Monitoring Service System (CPPMSS) for the collaborative production monitoring of individualized product orders. First, the business model, framework, and operating logic of CPPMSS are researched. Second, the energy-aware production monitoring service for customers, manufacturers, and equipment providers in the CPPMSS is clarified. Third, a demonstrative case is studied to verify the feasibility of CPPMSS. The results show that CPPMSS can satisfy the collaborative manufacturing operations among different stakeholders in the mass individualization environment.

Using cloud computing technology to design and implementation of smart shop floor control system

Smart manufacturing has evolved and become more automated, computerized and cloud computing. The current shop floor control system (SFCS) has been implemented for many years, and has gradually encountered many problems. It moves towards the flexible smart manufacturing owing to the multiplex customer demand. Therefore, the current SFCS has been customized in many versions, which makes the low maintenance efficiency and the version control difficult. This paper is using cloud computing as a platform of developing and integrating multiplex customer demand to establish the integrity smart system function, and using Team Server to centralize the program versions to solve version management. Besides, the maintenance efficiency is raised by using production parameter configuration of the service-oriented design. The cloud based method enables deploying to be performed in any place other than the production line, which also facilitates the promotion of maintenance efficiency. The subscription function offered by the reporting service provides reports at specific time. The result of this research is to develop the cloud-based smart SFCS, which can achieve the goal of integrating production process and the promotion of production efficiency. With the system report, cloud-based smart SFCS can support managers to make right decision to promote the product quality.

Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems

The digitalization processes in manufacturing enterprises and the integration of increasingly smart shop floor devices and software control systems caused an explosion in the data points available in Manufacturing Execution Systems. The degree in which enterprises can capture value from big data processing and extract useful insights represents a differentiating factor in developing controls that optimize production and protect resources. Machine learning and Big Data technologies have gained increased traction being adopted in some critical areas of planning and control. Cloud manufacturing allows using these technologies in real time, lowering the cost of implementing and deployment. In this context, the paper offers a machine learning approach for reality awareness and optimization in cloud.Specifically, the paper focuses on predictive production planning (operation scheduling, resource allocation) and predictive maintenance. The main contribution of this research consists in developing a hybrid control solution that uses Big Data techniques and machine learning algorithms to process in real time information streams in large scale manufacturing systems, focusing on energy consumptions that are aggregated at various layers. The control architecture is distributed at the edge of the shop floor for data collecting and format transformation, and then centralized at the cloud computing platform for data aggregation, machine learning and intelligent decisions. The information is aggregated in logical streams and consolidated based on relevant metadata; a neural network is trained and used to determine possible anomalies or variations relative to the normal patterns of energy consumption at each layer. This novel approach allows for accurate forecasting of energy consumption patterns during production by using Long Short-term Memory neural networks and deep learning in real time to re-assign resources (for batch cost optimization) and detect anomalies (for robustness) based on predicted energy data.

3D bare-hand interactions enabling ubiquitous interactions with smart objects

Ubiquitous augmented reality (UAR) implementation can benefit smart shop floor operations significantly. UAR from a user’s first-person view can support and provide the user with suitable and comprehensive information without him/her being distracted from ongoing tasks. A natural hand-based interaction interface, namely, a mobile bare-hand interface (MBHI), is proposed to assist a user in exploring and navigating a large amount of information for a task in the user’s first-person view. The integration of a smart shop floor and UAR-based MBHI is particularly challenging. A real shop floor environment is composed of challenging conditions for the implementation of UAR, e.g., messy backgrounds and significant changes in illumination conditions. Meanwhile, the MBHI is required to provide precise and quick responses to minimize the difficulty of a user’s task. In this study, a wearable UAR system integrated with an MBHI is proposed to augment the shop floor environment with smart information. A case study is implemented to demonstrate the practicality and effectiveness of the proposed UAR and MBHI system.

A Simple Smart Shop Floor Paradigm for Industry 4.0

This paper illustrates the needs and challenges of the smart shop floor in intelligent manufacturing and combines with insights from the field of current MES system needs, advantages and drawbacks, and describes the cloud platform enrich production service system. In a word, recent advanced technologies has paved way for a complete and systematic implement of it. the smart shop floor is in the initial stage of development in factory. At present, there is an urgent need for a viable paradigm of smart shop floor as a guideline. By integrating all relative applications and solutions, Chongqing Guangshu Robot Co., Ltd has designed a simple enough paradigm to adopt the Industry 4.0, within which all related information is closely monitored and synchronized between the physical shop floor and the network computational space.

Analyzing the cyber-physical system–based autonomous collaborations among smart manufacturing resources in a smart shop floor

Industry 4.0 focuses on the realization of smart manufacturing from shop floors to factories and to the whole supply chain. As a key technology of smart manufacturing, cyber-physical system has been widely discussed in the aspects of system design, data collection and processing, and cyber-physical synchronization. In a smart shop floor, manufacturing resources with intelligence and autonomy are abstracted as cyber-physical system units. They can communicate with each other autonomously to make optimal production decisions according to the real-time status of the shop floor. In this article, an autonomous collaboration network comprised of cyber-physical system–based smart manufacturing resources is modeled by using complex network theory. The collaboration activities among them are further analyzed, from which the information of key cyber-physical system units and key collaboration relationships are excavated. A demonstrative case is studied to verify the feasibility of the proposed model. From the case, it can be seen that (1) autonomous collaboration network has a small-world feature; (2) cyber-physical system units with bigger degrees and the collaborative relationships with bigger tightness are more important; (3) the workload of cyber-physical system units needs to be balanced because some cyber-physical system units have exceeded their capacities; and (4) cyber-physical system units with larger collaboration clustering coefficients will attract other nodes to form communities centered by them. Based on these results, the autonomous production control and management of smart shop floor will become more accurate, efficient, and balanced.

Enhancing smart shop floor management with ubiquitous augmented reality

This paper describes a framework for implementing Smart manufacturing Shop floor systems based on the Ubiquitous Augmented Reality technology (SSUAR). The proposed system makes use of data sharing between shop floor resources and a sensor network in order to optimise the production schedules for carrying out projects. The optimisation is performed in real-time and the production scheduling responds to new projects as well as the changing status of resources, such as machines and workers. Ubiquitous augmented reality interface has been developed and utilised as a user interface for the shop floor workers to receive information, instructions and guidance from the experts and manufacturing systems, and to update the systems on task parameters, such as estimated completion times, progress and machine status. A review of related work, methodology and implementation of the proposed system, and a case study are presented in this paper. Using this architecture, real-time scheduling of tasks in the smart shop floor can be achieved. The case study demonstrated the ability of SSUAR to integrate task scheduling with two-way communication between the system and the users.

Digital twin-driven cyber-physical production system towards smart shop-floor

Smart manufacturing is the core in the 4th industrial revolution. Smart shop-floor is one of the basic units of smart manufacturing. With the development of the advanced technologies (e.g. cloud computing, internet of things, model-based definition, advanced simulation, artificial intelligence), a larger number of virtual shop-floors are being built. However, it is very important that how to realize the intelligent interconnection and interaction between physical shop-floors and virtual ones. Digital twin (DT) is one of the key technologies associated to the cyber-physical system. In this paper, we present our vision on the cyber-physical production system (CPPS) towards smart shop-floor at scale via DT. This paper firstly explores a product manufacturing digital twin (PMDT), which focuses on the production phase in smart shop-floor. The proposed PMDT consists of five models: Product Definition Model (PDM), Geometric and Shape Model (GSM), Manufacturing Attribute Model (MAM), Behavior and Rule Model (BRM) and Data Fusion Model (DFM). And then based on PMDT, this paper proposes a new architecture of CPPS, which is composed of five layers (physical layer, network layer, database layer, model layer, application layer). Finally, this paper addresses the opportunities to use DT for the CPPS to support job scheduling during normal operation. Furthermore, the related further work and suggestions are also discussed.

Axiomatic Design based Design of a Software Prototype for Smart Shopfloor Management

Shopfloor management systems are currently undergoing a change. With the era of Industry 4.0, traditional shop floor management concepts are changing to new and digitally supported approaches for the coordination and management of production at the shop floor level. In order to investigate this change, the research project “Smart Shopfloor” is aiming to develop a software prototype for production management in the era of Industry 4.0. Requirements of industry are collected through literature review and workshops with industry and are translated into software functionalities. These functionalities lead, via an Axiomatic Design (AD) decomposition to design solutions for Smart Shop Floor Management systems. The results of the AD study form the basis for the software architecture and the definition of core and add-on functionalities of the software prototype. The focus of this paper lies on the AD decomposition of the design concept and further gives an overview of potential functionalities. In future, the developed concept will then be implemented in a lab environment before implementing and testing it in industrial case studies.

Using autonomous intelligence to build a smart shop floor

The vision of smart shop floor is based on the notion of Industry 4.0 that denotes technologies and concepts related to Cyber-Physical Production Systems (CPPS). In smart shop floors, CPPS monitors physical processes, creates a virtual copy of the physical world, and makes decentralized decisions. CPPS allows virtual world to store data, process data, communicate, and cooperate with each other in real time. This paper presents architecture of smart shop floor based on physical, logical, and communication layers that embed intelligent approaches within manufacturing processes. Every physical entity in the smart shop floor is regarded as an autonomous intelligent logical unit that performs operations guided by distributed control functions. Moreover, computing power and optimization approaches are embedded into each logical unit to make decisions to agilely respond to frequent occurrence of unexpected disturbances at the shop floor. A test platform has been set up to demonstrate how physical entities can be cooperative and autonomous logical units that can automatize the shop floor operations. The results verify the feasibility and efficiency of the proposed method.

Using Autonomous Intelligence to Build a Smart Shop Floor

The vision of smart factory is based on the notion of Industry 4.0 that denotes technologies and concepts related to cyber-physical systems and the Internet of Things (IoT). In smart factories cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the IoT, cyber-physical systems communicate and cooperate with each other in real time. This paper presents a smart factory architecture based on communication and computing layers that embed scheduling mechanisms within a mechanical shop floor. Every physical entity in the shop floor is seen as an autonomous intelligent agent that performs tasks guided by dynamic scheduling functions. A test bed has been set up to show how physical entities can be cooperative and autonomous units that can automatize the shop floor operation processes. The results verify the feasibility and efficiency of proposed method.