Manufacturing Shop Floor Research Articles

Integrated optimization of multiple warehouse replenishment for semiconductor manufacturing

With high variability of customer ordering in current marketing environment, it brings big challenges for manufacturers to adapt this kind variation with dynamic operation mechanism. From suppliers to manufactures and manufactures to customers, inventory management plays a critical role in the whole supply chain. In recent years, inventory problems are not limited to be solved with traditional economic mathematical model and lots of researchers focus their works on inventory optimization from supply chain perspectives. However, most of these models of inventory management are limited to be applied into external perspective of shop floor manufacturing process. Towards this end, it is necessary to develop a model which can be applied into internal perspective of factory and guide real on-line inventory of shop floor. This paper presents a dynamic inventory management method from multiple warehouse replenishment perspective. Within the shop floor environment of semiconductor manufacturing, the presented model is employed to optimize the inventory problem from internal and external perspectives of factory simultaneously. A computational simulation with the flexsim7.0 software is conducted to testify the proposed model and mechanism. The simulation results show that the improved solutions are achieved with the indicators of inventory quantity control, customer order fulfillment, and cycle time reduction.

Internet of Things for Real-time Waste Monitoring and Benchmarking: Waste Reduction in Manufacturing Shop Floor

In today's manufacturing IT system (e.g. ERP), waste streams in operations are rarely tracked in real-time. Therefore, operational decision that is based on late and incomplete information becomes untimely and not optimal. This paper presents an approach for real-time waste monitoring, analysis and discovers waste reduction potential in manufacturing shop floor. The objective of this study is to learn the waste performance in manufacturing shop floor. Data Envelope Analysis (DEA) is conducted to perform waste benchmarking analysis and the outcome is presented as relative efficiency. The efficiency values are then classified using quartile into 3 performance classes: normal, warning and abnormal. With the performance status, it leads manager to discover achievable waste reduction targets and hence empowers optimization of operational efficiency. A case study using microfluidic device manufacturing line is studied to demonstrate real-time waste monitoring and benchmarking.

Internet-of-Things Enabled Real-time Monitoring of Energy Efficiency on Manufacturing Shop Floors

Energy efficiency (EE) has become an important indicator in manufacturing industry due the rising concerns about climate change and tightening of environmental regulations. However, most manufacturing companies today are only able to monitor aggregated energy consumption and lack the real-time visibility of EE on the shop floors. The ability to access energy information and effectively analyze such real-time data to extract key indicators is a crucial factor for successful energy management. Therefore, in this paper, we introduce an internet-of-things (IoT) enabled software application for real-time monitoring of EE on manufacturing shop floors. While enabling real-time monitoring of EE, it also applies data envelopment analysis (DEA) technique to detect abnormal energy consumption patterns and quantify energy efficiency gaps. Through a case study of a microfluidic device manufacturing line, we demonstrate how the application can assist energy managers in embedding best energy management practices in their day-to-day operations and improve EE by eliminating possible energy wastages on manufacturing shop floors.

Game Theory Based Real-Time Shop Floor Scheduling Strategy and Method for Cloud Manufacturing

With the rapid advancement and widespread application of information and sensor technologies in manufacturing shop floor, the typical challenges that cloud manufacturing is facing are the lack of real-time, accurate, and value-added manufacturing information, the efficient shop floor scheduling strategy, and the method based on the real-time data. To achieve the real-time data-driven optimization decision, a dynamic optimization model for flexible job shop scheduling based on game theory is put forward to provide a new real-time scheduling strategy and method. Contrast to the traditional scheduling strategy, each machine is an active entity that will request the processing tasks. Then, the processing tasks will be assigned to the optimal machines according to their real-time status by using game theory. The key technologies such as game theory mathematical model construction, Nash equilibrium solution, and optimization strategy for process tasks are designed and developed to implement the dynamic optimization model. A case study is presented to demonstrate the efficiency of the proposed strategy and method, and real-time scheduling for four kinds of exceptions is also discussed.

Formulating and solving sustainable stochastic dynamic facility layout problem: a key to sustainable operations

Facility layout design, a NP hard problem, is associated with the arrangement of facilities in a manufacturing shop floor, which impacts the performance, and cost of system. Efficient design of facility layout is a key to the sustainable operations in a manufacturing shop floor. An efficient layout design not only optimizes the cost and energy due to proficient handling but also increase flexibility and easy accessibility. Traditionally, it is solved using meta-heuristic techniques. But these algorithmic or procedural methodologies do not generate effective and efficient layout design from sustainable point of view, where design should consider multiple criteria such as demand fluctuations, material handling cost, accessibility, maintenance, waste and more. In this paper, to capture the sustainability in the layout design these parameters are considered, and a new sustainable stochastic dynamic facility layout problem (SDFLP) is formulated and solved. SDFLP is optimized for material handling cost and rearrangement cost using various meta-heuristic techniques. The pool of layouts thus generated are then analyzed by data envelopment analysis to identify efficient layouts. A novel hierarchical methodology of consensus ranking of layouts is proposed which combines the multiple attributes/criteria. Multi attribute decision-making techniques such as technique for order preference by similarity to ideal solution, interpretive ranking process and analytic hierarchy process, Borda–Kendall and integer linear programming based rank aggregation techniques are applied. To validate the proposed methodology data sets for facility size $$N=12$$ for time period $$T=5$$ having Gaussian demand are considered.

A hybrid-data-on-tag–enabled decentralized control system for flexible smart workpiece manufacturing shop floors

Industry 4.0 describes future workpieces would be smart. They can control the individual stages of their production semiautonomously through negotiation with production resources. To this end, RFID is introduced into machining processes. The current applications of radio frequency identification mainly include two approaches: one is data-on-network approach which centrally stores workpiece-related data on network; another is data-on-tag approach, which stores the data on RFID tag. Data-on-network approach cannot adapt to flexible automated production lines, which require decentralized control system for flexible process and rapid response, and data-on-tag approach makes RFID tag store large amounts of data, which would result in slow data reading and writing problems. For solving this problem, a hybrid-data-on-tag approach is proposed. The on-tag data contains the basic machining information and the index of further machining information which is stored in the backend database. Based on the hybrid-data-on-tag approach, the control node model of decentralized control system and the corresponding cyber-physical systems architecture of flexible manufacturing shop floors are presented. Furthermore, a multi-agent-based decentralized control system is used to describe the communication of smart workpieces with production resources during their production procedures. Finally, a small-scale flexible automated production line is taken as an example to illustrate the utility of hybrid-data-on-tag approach.

Investigation into current industrial practices relating to product lifecycle management in a multi-national manufacturing company

ABSTRACTProduct Lifecycle Management (PLM) systems have gained growing acceptance for managing all information relating to products throughout their full lifecycle, from idea conceptualisation through operations to servicing and disposal. This paper, through an in-depth exploratory study into a leading power generation manufacturing organization, presents current PLM issues experienced by manufacturing companies, exploring three separate topics: 1) PLM, 2) Knowledge Management and Lessons Learnt and 3) Product Servicing and Maintenance. Following a review of published literature, results of the investigation are presented, analysing the responses of 17 employees interviewed. With respect to Product Development, it was found that information traceability is time consuming and change management requests take too long to complete. Results relating to knowledge management indicate that the Company operates a ‘who you know’ culture, but do aim to capture lessons learned on the manufacturing shop floor and asse...

Radio frequency identification–enabled monitoring and evaluating in the discrete manufacturing process

Process monitoring is a major focus on improving productivity of discrete manufacturing. With the recent application of new technologies such as radio frequency identification to manufacturing process, real-time production information has become available for decision support in manufacturing system. This study presents a radio frequency identification–based visibility monitoring rule for tracking work-in-process. Specific to the aviation manufacturing shop floor, a case study for demonstration is designed. The value of radio frequency identification–enabled real-time information is investigated within the discrete manufacturing operation. For this purpose, a simplified simulation model is developed to test the benefit of radio frequency identification–based rule compared to the classical scheduling rule. The results indicate that the adoption of radio frequency identification brings better production performance.

Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors

Physical Internet (PI, π) has been widely used for transforming and upgrading the logistics and supply chain management worldwide. This study extends the PI concept into manufacturing shop floors where typical logistics resources are converted into smart manufacturing objects (SMOs) using Internet of Things (IoT) and wireless technologies to create a RFID-enabled intelligent shop floor environment. In such PI-based environment, enormous RFID data could be captured and collected. This study introduces a Big Data Analytics for RFID logistics data by defining different behaviours of SMOs. Several findings are significant. It is observed that task weight is primarily considered in the logistics decision-making in this case. Additionally, the highest residence time occurs in a buffer with the value of 12.17 (unit of time) which is 40.57% of the total delivery time. That implies the high work-in-progress inventory level in this buffer. Key findings and observations are generated into managerial implications, which are useful for various users to make logistics decisions under PI-enabled intelligent shop floors.

An image-based multivariate generalized likelihood ratio control chart for detecting and diagnosing multiple faults in manufactured products

Image-capturing systems are increasingly being used in manufacturing shop floors since they can reliably capture important aesthetic information pertaining to the quality of manufactured parts in real time. State-of-the-art image-monitoring applications have focused on the detection of a single fault; however, the number of fault clusters per image in industrial applications can be numerous. To address this issue, we propose the use of a multivariate generalized likelihood ratio (MGLR) control chart for monitoring industrial products whose quality is described by a specific pattern (e.g. uniform patterns in LED screens or decorative patterns in textile products). Our method is specifically designed for greyscale images that are typical outputs of real-time industrial image-capturing systems. Extensive computer simulations show that the proposed method can detect the occurrence of single and multiple faults. We also present an experimental study to highlight how practitioners can implement and make use of the MGLR control chart in image-monitoring applications.

An optimization method for shopfloor material handling based on real-time and multi-source manufacturing data

With the wide use of auto-ID devices in manufacturing shop floors, it creates a huge number of real-time and multi-source manufacturing data. To make a better decision based on the real-time and multi-source manufacturing data, in this paper, a dynamical optimization model for shopfloor material handling (DOM-SMH) is designed. Contrast to a traditional material handling method, each trolley is an active entity which will request the transport tasks. Then, the optimal transport tasks will be assigned to the optimal trolleys according to their real-time status. The key technologies such as intelligent trolley, real-time information exchange and optimization for material handling tasks are designed and developed to implement the dynamical optimization model. The presented method is demonstrated by a case study, and its effectiveness is also analyzed and discussed through the empty-loading ratio and total distance.

A systematic approach of process planning and scheduling optimization for sustainable machining

The implementation of sustainability in manufacturing companies, whose activities are usually characterized by high variety and low volume, has been crippled by the lack of effective process planning and scheduling solutions for sustainable management of manufacturing shop floors. To address the challenge, an innovative and systematic approach for machining process planning and scheduling optimization has been developed. This approach consists of a process stage and a system stage, augmented with intelligent mechanisms for enhancing the adaptability and responsiveness to job dynamics in machining shop floors. In the process stage, key operational parameters for machining a part are optimized adaptively to meet multiple objectives and constraints, i.e., energy efficiency of the machining process and productivity as objectives and surface quality as a constraint. In the consecutive system stage, to achieve higher energy efficiency and shorter makespan in the entire shop floor, sequencing/set-up planning of machining features, operations and scheduling for producing multiple parts on different machines are optimized. Artificial neural networks are used for establishing the complex nonlinear relationships between the key process parameters and measured data sets of energy consumption and surface quality. Intelligent algorithms, including pattern search, genetic algorithm, and simulated annealing, are applied and benchmarked to identify optimal solutions. Experimental tests indicate that the approach is effective and configurable to meet multiple objectives and technical constraints for sustainable process planning and scheduling. The approach, validated through industrial case studies provided by a European machining company, demonstrates significant potentials of research applicability in practice.

A Self-Aware Machine Platform in Manufacturing Shop Floor Utilizing MTConnect Data


 
 
 We propose a framework of self-aware machines based on data collected using the MTConnect protocol. Beyond exist- ing applications of OEE (Overall Equipment Effectiveness) reporting, the proposed framework integrates multiple sources of information for work-piece and machine condition monitoring, and equipment time to failure prediction in manufacturing processes, and provides feedback to shop supervisor. Firstly, we propose a method to predict component wear and failure based on operational data. ICP (Interactive Closest Point) algorithm is used to find the best matching tool path given a certain tool number to identify similar machining pro- cesses. The result of ICP tool path matching, together with other parameters such as spindle speed, feed rate and tool number, are used to adaptively cluster the machining pro- cesses. For each process cluster, a particle filter based prognostic algorithm is used to predict tool wear and/or spindle bearing failure. Secondly, we propose to use anomaly detection methods to detect changes in normal behavior of the machines. Various machine learning algorithms are utilized to detect anomalies based on real-time data, and a voting mechanism is used to decide when to trigger an alarm. Thirdly, the axes traverse is aggregated to provide a measure of the wear on various axes in the machine, which is correlated to errors in position comparing to the commanded positions and nominal tool paths. Spindle load verse rotating speed is also examined to facilitate shop floor scheduling to avoid damage caused by unintentionally excessive machine usage. The proposed framework has been demonstrated using published data from two Mazak machine tools.
 
 

스마트디바이스를 이용한 HSE 적용에 관한 연구

Received 10 January 2014; received in revised form 10 February 2014; accepted 11 February 2014ABSTRACTIn this paper, HSE (Health and Safety Environments) research with smart device and biomet-rics is conducted for the worker’s HSE enhancement in the manufacturing shop floor. Today,various kinds of smart devices are popularized extensively. In addition, the wearable typedevices are being introduced such as google glass very recently. Smart device of werable type isexptected to bring great opportunity out in terms of HSE functionality. Smart devices of phoneor tablet type are being used for on-line work between control center and manufacturing shopfloor by virtue of wireless communication. However, those devices are not appropriate fordetecting of worker’s physical senses such as temperature and pulse. In this paper, we devel-oped a glass type smart device and required funictions for HSE enhancement with the investiga-tion of biometrics technology. Also, required sensors are investigated for the detecting oftemperature, pulse and noxious gasses. Smart device of glass type has advantages in that mostsenses of personnel are gathered around faces, and also it’s possible to detect neutral axis ofbody because the wearing location is fixed. We expect that the proposing HSE product of glasstype smart device could contribute the enhancement of the HSE of manufacturing shop floor.Key Words: Biometrics, Health and Safety Environments, Smart Device

Depth camera based collision avoidance via active robot control

A new type of depth cameras can improve the effectiveness of safety monitoring in human–robot collaborative environment. Especially on today's manufacturing shop floors, safe human–robot collaboration is of paramount importance for enhanced work efficiency, flexibility, and overall productivity. Within this context, this paper presents a depth camera based approach for cost-effective real-time safety monitoring of a human–robot collaborative assembly cell. The approach is further demonstrated in adaptive robot control. Stationary and known objects are first removed from the scene for efficient detection of obstacles in a monitored area. The collision detection is processed between a virtual model driven by real sensors, and 3D point cloud data of obstacles to allow different safety scenarios. The results show that this approach can be applied to real-time work cell monitoring.

Machine Monitoring System Based on MTConnect Technology

The modern manufacturing shop floor contains a variety of different types of machining equipment, each supporting different proprietary interfaces and communication protocols. This diversity has made it extremely challenging for shop managers to monitor and maintain their machines. Fortunately, the emerging MTConnect standard makes it possible for a monitoring system to accurately and consistently collect data from any MTConnect compatible machine, regardless of its brand or origin. This paper introduces a web-based machine monitoring system that provides data collection, analysis, and machine event notification for MTConnect compatible machines. This solution provides shop managers with the information needed to improve process efficiency on the shop floor, and increase overall equipment effectiveness (OEE).

On the Use of a Low-Cost Thermal Sensor to Improve Kinect People Detection in a Mobile Robot

Detecting people is a key capability for robots that operate in populated environments. In this paper, we have adopted a hierarchical approach that combines classifiers created using supervised learning in order to identify whether a person is in the view-scope of the robot or not. Our approach makes use of vision, depth and thermal sensors mounted on top of a mobile platform. The set of sensors is set up combining the rich data source offered by a Kinect sensor, which provides vision and depth at low cost, and a thermopile array sensor. Experimental results carried out with a mobile platform in a manufacturing shop floor and in a science museum have shown that the false positive rate achieved using any single cue is drastically reduced. The performance of our algorithm improves other well-known approaches, such as C4 and histogram of oriented gradients (HOG).

Digital Human and New Type of Man-Machine Coordinated Digital Shop Manufacturing System

On the basis of modern technology of wireless communication and wireless local area network, a concept of Digital Human (DM) is put forward. DM change ordinary man-machine coordinated mode as DM-machine coordinated mode. Base on the discussion of the realization of DM, an innovated mode of digital shop floor manufacturing system is put forward also by introducing the mobile computing technology. Finally, the innovated mode of Man-Machine Coordinated Digital Shop Manufacturing system (MMCDSM) is presented. This paper also analyzes the network architecture and function module of this type of shop floor manufacturing system. Finally, integrating with multi-agent technology and workflow technology, a new methodology of role-based workflow modeling by utilizing multi-agent & DM is also put forward which can be applied in the MMCDSM.

Simulation guided value stream mapping and lean improvement: A case study of a tubular machining facility

Purpose: This paper describes a typical Value stream mapping (VSM) application enhanced by the discrete event simulation (DES) to a dedicated tubular manufacturing process. Design/Methodology/Approach: VSM is prescribed as part of lean production portfolio of tools, not only highlights process inefficiencies, transactional and communication mismatches, but also guides improvement areas. Meanwhile, DES is used to reduce uncertainty and create consensus by visualizing dynamic process views. It is served as a complementary tool for the traditional VSM to provide sufficient justification and quantifiable evidence needed to convince the lean approaches. A simulation model is developed to replicate the operation of an existing system, and that of a proposed system that modifies the existing design to incorporate lean manufacturing shop floor principles. Findings: A comprehensive model for the tubular manufacturing process is constructed, and distinctive scenarios are derived to uncover an optimal future state of the process. Various simulation scenarios are developed. The simulated results are acquired and investigated, and they are well matched with the real production data. Originality/Value: DES is demonstrated as a guided tool to assist organizations with the decision to implement lean approaches by quantifying benefits from applying the VSM. A roadmap is provided to illustrate how the VSM is used to design a desired future state. The developed simulation scenarios mimic the behavior of the actual manufacturing process in an intuitive manner.

Fusing multiple image transformations and a thermal sensor with kinect to improve person detection ability

This paper proposes a novel approach to combine data from multiple low-cost sensors to detect people in a mobile robot. Robust detection of people is a key capability required for robots working in environments with people. Several works have shown the benefits of fusing data from complementary sensors. The Kinect sensor provides a rich data set at a significantly low cost, however, it has some limitations for its use on a mobile platform, mainly that people detection algorithms rely on images captured by a static camera. To cope with these limitations, this work is based on the fusion of Kinect and a thermical sensor (thermopile) mounted on top of a mobile platform. We propose the implementation of an evolutionary selection of sequences of image transformation to detect people through supervised classifiers. Experimental results carried out with a mobile platform in a manufacturing shop floor show that the percentage of wrong classified using only Kinect is drastically reduced with the classification algorithms and with the combination of the three information sources. Extra experiments are presented as well to show the benefits of the image transformation sequence idea here presented.