Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Bugra Alkan, Robert Harrison Highly diverse factors including technological advancements, uncertain global market and mass personalisation are believed to be main causes of ever-growing complexity of manufacturing systems. Although complex systems may be needed to achieve global manufacturing requirements, complexity affects on various factors, such as: system development effort and cost, ease of re-configuration, level of skill required across the system life-cycle (e.g. design, operate and maintain). This article aims to develop a scientifically valid and industrially applicable complexity assessment approach to support early life-cycle phases of component-based automation systems against unwanted implications of structural system complexity. The presented approach defines component-based automation system as a constellation of basic components which can be represented in various design domains, such as: mechanical, electrical, pneumatic, control, etc. Accordingly, structural complexity is expressed as the combination of both inherent complexity of system entities and topological complexity resulting from the integration of elements of such constellations in a multi-layered network. The proposed approach is used to specify and implement a complexity assessment module which can be integrated into a series of virtual system design software solutions, in order to add complexity assessment as part of the design support and validation tools used by manufacturing engineers. Consequently, the proposed approach is integrated into the vueOne virtual engineering tool, wherein virtual automation system design data can be streamlined and used as input to the theoretical complexity model. In the developed tool, only mechanical and logical design domains are considered due to the limited data availability in early design phase. Inherent complexity of both mechanical and logical system entities and their interactions are expressed as a function of domain-specific design elements, and topological complexity is defined as the graph energy of the corresponding design connectivity matrix. Furthermore, the values of mathematical model parameters are determined based on an optimisation study, where subjective opinions of system/control engineers regarding the effort/difficulty associated with the development of thirty different component-based automation system designs are correlated with the corresponding complexity model outputs to minimise the prediction errors. The proposed approach is also demonstrated on a modular production system consisting of four sub-modules. The study shows that the approach can help designers/managers to better identify root causes of structural system complexity, and provides a systemic approach to compare alternate system designs during early system planning phase.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Lukas Baier, Julian Frommherz, Elmar Nöth, Toni Donhauser, Peter Schuderer, Jörg Franke Fast identification of failure root causes is a major task in minimizing rejects in manufacturing. Due to increasing complexity of products and supply chains many interdependencies affect the final product quality. As knowledge about every possible interdependence is hardly held by individuals, data analysis strategies are required to evaluate captured information. Hence, we propose a root cause identification method for determining the main influencing factors on end products failing end of line tests. Additionally, graphical representation of calculation results in spectrograms similar to audio signals facilitates human interpretation. The evaluation of the proposed method on the basis of a use case proves the applicability in a real scenario. The method is able to identify the root cause for rejects within short periods of time. In this specific case it shortened the analysis time by a factor of about 50. In the future, it empowers smart production systems to automatically identify failure root causes and to take countermeasures like adjusting process parameters.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Xiao Zhang, Benjamin Lies, Hao Lyu, Hantang Qin Electrohydrodynamic inkjet (e-jet) printing is a technique which utilizes electrical forces to generate droplets in micro/nano scale using conductive inks. Currently, there is no procedure in place to measure the printed patterns without taking the sample away from the printer setup. Removal of the substrate from the printing stage during the e-jet printing process prevents any additional work from being performed on the sample. We investigated the application of scalar diffraction for the in-situ measurement and digital reconstruction of opaque material printed on transparent substrates. Measurement and characterization of the printed material can be achieved in-situ to alter printing condition in process for quality assurance. In order to accomplish the sample reconstruction, a digital recording of a scalar diffraction pattern in the image plane was employed in this paper with a magnification of 5× with the help of a combination of lenses. The reconstructed images were then compared to images captured by an offline high-resolution microscope. The results indicated a submicron accuracy of the feature radii and the locations of feature centers. In addition to the quantitative measurements, this method also allows for the operator to view the overall form of the printed patterns. Our findings demonstrate an effective approach for in-situ monitoring of e-jet printing and printed patterns, which could pave the way for the industrial application in printing testing field.
Abstract: Publication date: Available online 26 August 2019Source: Journal of Manufacturing SystemsAuthor(s): Qianqian Dong, Raed Kontar, Min Li, Gang Xu, Jinwu Xu Statistical monitoring of advanced production processes is becoming increasingly challenging due to the large number of key performance variables that characterize a process. These variables often are non-Gaussian, highly correlated and exhibit non-linear dependencies. Traditional multivariate monitoring methods handle non-Gaussian data through combining both independent component analysis (ICA) and support vector data description (SVDD). However, redundant independent components not only increase the modeling complexity of SVDD but also reduce the accuracy of the monitoring. In this article, we solve the above problems by introducing a Laplacian based weighting score to adjust the ICA-SVDD procedure. The key aspect of our model is that independent components are automictically selected using a Laplacian algorithm, which are inputted to a SVDD model to determine the control limits. The advantageous features of the proposed method are demonstrated through a numerical study as well as a case study which concerns an application to a hot rolling process for monitoring steel production processes.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Pallab Biswas, Sameer Kumar, Vipul Jain, Charu Chandra The purpose of this paper is to assess supply chain reconfigurability in a global scenario. The paper attempts to measure comprehensive relative reconfigurability index (CRRI) in an automobile case supply chain by holistically considering fifteen enablers of reconfigurability. The present research evaluates CRRI by utilizing two distinct assessment models that calculate relative reconfigurability index (RRI). Firstly, a deterministic assessment model is developed from the total interpretive structural modeling (TISM) digraph, and secondly, an integrated assessment methodology that utilizes both the Delphi technique and the additive weighting method to calculate RRI. The uniqueness of the present research is to quantify reconfigurability in the case supply chain by a numerical index termed as CRRI. The paper analyzes the level of reconfigurability competence of the case supply chain in comparison with an ideal supply chain and thus facilitates structural flexibility. The proposed model for calculating CRRI, using two methodologies and comparing the results, is an innovative way of measuring supply chain reconfigurability. Supply chain’s preparedness to structurally change in the present business environment of uncertainty and turbulence can be evaluated and it would act as an aide in decision making pertaining to reconfigurability in supply chains.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Boonserm (Serm) Kulvatunyou, Hakju Oh, Nenad Ivezic, Scott T. Nieman 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.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Andrew Kusiak Manufacturing is undergoing transformation driven the developments in process technology, information technology, and data science. The incoming changes are disruptive and will likely result in manufacturing solutions unimaginable in the recent past. A future manufacturing corporation will be highly digital, and it will function in new modes discussed in this paper. After decades of integration of engineering design and manufacturing, the design-for-dedicated manufacturing will gradually transform in the design-for-open manufacturing. In many instances, manufacturing processes will become manufacturing-as-service (service manufacturing) systems. An enterprise will be gradually dominated by formation of services in a cloud. The emerging service manufacturing will be open, shared, easy to configurable, efficient, and democratic. Designing a manufacturing system of the past will reduce to formulating and solving an enterprise configuration problem. The presence of services in the cloud will be facilitated by the autonomously generated models. A formal modeling approach to configuration of manufacturing enterprises is discussed. The computational complexity of the configuration problem calls for different modeling and solution approaches ranging from mathematical programming and data science to quantum computing.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Cong He, Zailin Guan, Yeming Gong, Chuangjian Wang, Lei Yue A novel production line with high automation, flexibility, reliability and reconfigurability, which is designed for the smart factory and named as automated flexible transfer line (AFTL), is studied in this paper. Different from the other production lines, AFTL consists of sequential and reconfigurable stages which are grouped by multiple machining cells, and each machining cell is composed of a single robot and several machines. This special line structure contributes to the complexity of the non-linear line cycle time relations, and the cost of the line contains the cost of machines, robots and stages. The aim is to balance and configure the AFTL in minimal cost with a given line cycle time, which is equivalent to find the appropriate subsets of operations and assign each of them to the stage with an optimal configuration. Three novel and efficient lower bounds in different levels based on solving the set partitioning problem in AFTL design problem are presented and an effective algorithm is developed. The experimental results and case problem results prove that the proposed algorithm together with the lower bounds are effective and applicable for the industrial cases.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Dong-Hee Lee, Jin-Kyung Yang, Cho-Heui Lee, Kwang-Jae Kim Semiconductor wafers are fabricated through sequential process steps. Some process steps have a strong relationship with wafer yield, and these are called critical process steps. Because wafer yield is a key performance index in wafer fabrication, the critical process steps should be carefully selected and managed. This paper proposes a systematic and data-driven approach for identifying the critical process steps. The proposed method considers troublesome properties of the data from the process steps such as imbalanced data, missing values, and random sampling. As a case study, we analyzed hypothetical operational data and confirmed that the proposed method works well.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Jenny L. Diaz C., Carlos Ocampo-Martinez Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.
Abstract: Publication date: Available online 3 July 2019Source: Journal of Manufacturing SystemsAuthor(s): Qi Tian, Weihong Guo In order to stay responsive to evolving customer demands and to meet the need for greater product customizations, many manufacturing enterprises are recognizing the need to quickly reconfigure their manufacturing systems and supply chains. Making reconfiguration decisions requires a system-level optimization that involves many factors such as manufacturing tasks, outsourcing decisions, supply chain configurations, as well as risks. This paper proposes a graph-based cost model to optimize the configuration of manufacturing supply chain networks to support reconfiguration decision-making. An optimization model is formulated to minimize the total cost of the manufacturing enterprise with the consideration of operating cost and reconfiguration cost. To effectively quantify the reconfiguration cost, a graph-based cost model is developed to characterize the relationship between the graphical similarity of two supply chain networks and the reconfiguration cost. Outsourcing decisions and supply chain risks are also considered in the proposed model. A case study on a supply chain for laptop computer assembly is presented to demonstrate the effectiveness of the proposed method.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Daniel Opritescu, Christoph Hartmann, Wolfgang Riedl, Michael Ritter, Wolfram Volk Driven by lean manufacturing and technological progresses, the heterarchic and highly interdependent press-shop production networks in the automotive industry hold heavily-reduced redundancies at machine level. In these environments, even short-term machine or tool failure carry risks that can hardly be predicted, since single sites cannot usually continue to compensate incidents autonomously.This paper introduces a new optimization model for integrated machine scheduling with logistics planning at press-shop network level. The approach enables optimal capacity utilization for overcoming machine unavailability, and supports decision management for reconfiguring short-term production plans. Furthermore, the comprehensive integration of suppliers and subcontractors within the holistic concept is illustrated.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): İhsan Erozan Management of critical components in manufacturing systems aims at managing components with very low reliability or the highest risk which can cause disruptions in manufacturing. This study presents a method for identifying critical components and a decision support tool for managing maintenance activities of critical components in manufacturing systems. Unlike the traditional reliability function, the proposed method uses the duty cycle, utilization rate of capacity, safety stock effect, and redundancy effect. It also has the ability to calculate some of the costs associated with the reliability and maintenance. In addition to the proposed method, an expert system as a decision support tool has also been proposed to assist in managing maintenance activities of critical components. The proposed method and the developed decision support system have been tested with a real data set taken from an industrial company and a randomly generated data set. The results have shown that the proposed method has a more realistic impact on component reliability compared to the traditional reliability function. The experimental results have validated the credibility of the proposed decision support system to manage maintenance activities of critical components. Besides, two comparison tables have shown that the proposed decision support system outperforms some approaches such as ANN, FMEA, FMECA, and AHP.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Derek Machalek, Kody Powell Demand side management of energy is a vital function in the smart grid that allows for greater integration of renewable energy resources, and is facilitated with economic incentives in energy and demand pricing schedules. Manufacturing systems can take advantage of these incentives to reduce their energy costs through active energy management. This paper is a simulation analysis on the implementation of a novel algorithm that levelizes the maximum power load of an industrial bakery that operates under an on-peak demand price structure. The algorithm takes advantage of an untapped thermal energy storage resource in the facility, a chilled glycol buffer tank, to level the short intra-day power oscillations that the facility experiences. One of the key advantages of the algorithm is that it does not require precise demand forecasting or complex control algorithms, such as model predictive control. Even with substantial error in peak power estimates (5%), the algorithm is expected to result in at least a 2% peak reduction. Under ideal prediction, the algorithm reduces the on-peak facility power maximum by 7.3% of the possible 9.0% reduction the storage can provide. Further, the algorithm uses very few facility specific inputs, and can readily be adapted for any facility with short intra-day oscillatory power profiles and untapped storage capacity. The key insight of the paper is the employment of a novel algorithm to leverage untapped energy storage in manufacturing facilities to transform them into smart grid participants with no major capital investment.
Abstract: Publication date: Available online 11 June 2019Source: Journal of Manufacturing SystemsAuthor(s): Botao Zhang, Archak Goel, Omkar Ghalsasi, Sam Anand This paper discusses a set of geometry based computational pre-processing algorithms developed for Powder Bed Fusion Additive Manufacturing (PBFAM) processes. To start with, based on an initial part design, an automatic support structure generation module generates customized CAD-based support structures for a given part build orientation. Various additive manufacturing (AM) parameters and Design for Additive Manufacturing (DFAM) metrics are calculated on the fly for assigning producibility scores at different part build orientations. A set of stand-alone computational geometry-based algorithms with associated graphical user interfaces (GUI) are developed for calculating support structure parameters, as well as for detecting and highlighting DFAM features that are difficult to manufacture. These stand-alone tools provide a quantified output for each of the parameters or features, which are then used downstream during the producibility index (PI) calculation. An algorithm that evaluates ease of removing supports during the post-processing phase, and suggests the optimum number of setups needed to remove support structures is developed. Finally, Producibility Index, which is a weighted optimization metric, brings together the quantified outputs of the DFAM analysis, support structure parameters, accessibility analysis and suggests the best build orientations for the given part geometry. All the algorithms are implemented within the Siemens NX modelling environment utilizing C++ and NX API functions. The developed algorithm and tools have been succesfully demonstrated on two sample parts.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): James C. Chen, Yin-Yann Chen, Tzu-Li Chen, Yi-Hsin Kuo The module process is labor-intensive in the thin film transistor–liquid crystal display (TFT–LCD) industry because of the difficulty in applying automation to this process as compared with array, color filter, and cell processes. The module process is also considered a multi-model assembly line, which means several models from a basic product family are manufactured simultaneously. Therefore, a module process with integrated arrangement and line-balancing can reduce labor requirements and increase production efficiency. This research considered several practical characteristics of the TFT–LCD module process, including multi-skilled workers and operator efficiency, to address the resource-constrained multi-model assembly line balancing problem. A novel mathematical programming model is proposed to obtain the optimal allocation of tasks, workers, machines, and workstations. A heuristic two-phase approach based on the adaptive genetic algorithm is developed to address this NP-hard problem. Data from the TFT–LCD module factories in Taiwan are applied to evaluate the performance of the proposed approach based on the design of experiments and response surface method. This study integrated theoretical research and practical applications for the assembly line balancing problem in the TFT–LCD module process. Hence, from the results, the production efficiency can be improved and the cost can be reduced, which can enhance the global competitiveness of TFT–LCD manufacturers.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Biao Lu, Xiaojun Zhou For manufacturing systems, product quality and machine reliability are two key health indicators, which usually deteriorate as a result of machine deterioration. Maintenance can mitigate the machine deterioration and consequently improve product quality and machine reliability. In this context, we propose a quality and reliability oriented condition-based maintenance (CBM) policy for the serial multistage manufacturing systems. In the policy, the conditions of quality-related components are monitored to evaluate the quality loss of final products and the system failure rate, which are further compared with the corresponding thresholds to decide whether to trigger preventive maintenance (PM). When PM is triggered, a cost-based improvement factor is introduced to identify the importance ranking of PM groups and then determine a group of machines for PM. This factor is developed based on importance measure and jointly considers the improvement of quality and reliability and the reduction of maintenance costs. A multi-level CBM decision-making process is developed to evaluate the total cost over the planning horizon for decision optimization. The effectiveness and superior performance of the proposed CBM policy is demonstrated through a case study of a serial four-stage machining system producing shaft sleeves.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Aaron D. Neal, Richard G. Sharpe, Paul P. Conway, Andrew A. West Cyber-Physical Systems (CPSs) are becoming a significant research focus resulting from advancements in technologies such as the Internet of Things (IoT), Cloud Manufacturing and Intelligent Products. Successful deployment of CPSs has the potential to provide a step change in manufacturing efficiency, flexibility and production yield as envisaged by the fourth industrial revolution or Industry 4.0 paradigm. The realisation of intelligent products and services is in the provisioning of predictive, risk preventative and high-performance manufacturing systems. As part of these manufacturing systems, Returnable Transit Items (RTIs) play a critical role in provisioning robust and efficient means of component (e.g. Work in Progress and finished items) protection and logistics. The research outlined in this paper details how a Returnable Transit Item (RTI) can become an integral part of the Industrie 4.0 vision as an intelligent container that can interact with components, machines and other cyber-physical manufacturing services. This paper discusses a CPS reference architecture for the integration of intelligent containers and presents a hardware and software proof of concept solution suitable for industrial deployments. The paper concludes with feasibility studies utilising the intelligent container for context determination services including the identification of intelligent components and monitoring of logistical handling process (i.e. the detection of collisions, lifting and turns).
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Yuchen Wen, Xiaowei Yue, Jeffrey H. Hunt, Jianjun Shi A new shape control system has been developed to reduce the dimensional deviations between two composite fuselages. To evaluate the system, the virtual assembly and residual stress analysis are needed. Since actuators’ forces are applied to each fuselage during the assembly, residual stresses may remain after the release of actuators. The residual stresses could lead to severe mechanical problems for the fuselage. Therefore, we propose a new finite element simulation and virtual assembly analysis method for evaluating the assembly process of two composite fuselages. Our method simulates the release of actuators directly instead of applying reverse forces, which mimics the assembly process and increases the simulation accuracy. The dimensional change and residual stresses during and after the assembly process are evaluated. The results show that the assembly process with new shape control system is feasible since the residual stresses resulting from the control system are much smaller than the failure threshold.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Y.C. Liang, W.D. Li, X. Lu, S. Wang Cloud enabled prognosis systems have been increasingly adopted by manufacturing industries. The effectiveness of the cloud systems is, however, crippled by the high latency of data transfer between shop floors and the cloud. To overcome the limitation, this paper presents an innovative fog enabled prognosis system for machining process optimization. The system functions include: (1) dynamic prognosis - Convolutional Neural Network (CNN) based prognosis is implemented to detect potential faults from customized machining processes. Pre-processing mechanisms of the CNN are designed for partitioning and de-noising monitored signals to strengthen the performance of the system in practical manufacturing situations; (2) an innovative fog enabled prognosis architecture for machining process optimization – it consists of a terminal layer, a fog layer and a cloud layer to minimize data traffic and improve system efficiency. Under the architecture, monitored signals during machining collected on the terminal layer are processed using the trained CNN deployed on the fog layer to efficiently detect abnormal situations. Intensive computing activities like training of the CNN and system re-optimization responding to detected faults are carried out dynamically on the cloud layer to leverage its computation powers. The system was validated in a UK machining company. With the system deployment, the efficiency of energy and production was improved for 29.25% and 16.50% on average. In comparison with a cloud system, this fog system achieved 70.26% reduction in the bandwidth requirement between shop floors and cloud, and 47.02% reduction in data transfer time. This research, sponsored by EU projects, demonstrates that industrial artificial intelligence can facilitate smart manufacturing practices effectively.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Yi-Kuei Lin, Ping-Chen Chang, Louis Cheng-Lu Yeng, Shang-Fu Huang A job-shop production system (JPS) is a manufacturing system wherein each workstation configures multiple types of machines to produce small batches of a variety of products. In each workstation of a JPS, the number of machines that operate normally exhibits multiple levels of capacity owing to failures, partial failures, and maintenance. That is, the number of normal machines in each workstation is stochastic (i.e., multistate). To analyze such a JPS, the JPS is transformed into a multistate job-shop production network (MJPN) using a network topology. For the MJPN, a critical issue is to maximize the network reliability and to minimize the purchase cost when setting up the JPS. To achieve such bi-objective optimization, a machine vector (MV) representing the current number of normal machines in each workstation is introduced to evaluate network reliability. An algorithm based on a depth-first search (DFS) with an expanding technique is proposed to search all MVs for satisfying demand. Subsequently, to obtain a machine configuration (MF) simultaneously having the maximal network reliability and the minimal purchase cost, a two-stage approach is developed based on the non-dominated sorting genetic algorithm II (NSGA-II) and the technique for order of preference by similarity to ideal solution (TOPSIS). A real case of t-shirt production is utilized to illustrate the proposed method.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Dong-Hee Lee, Chang-Ho Lee, Seung-Hyun Choi, Kwang-Jae Kim In the semiconductor wafer fabrication process, wafers go through a series of sequential process steps. Typically, each process step has several machines, and the wafers are assigned to one whenever they enter the process step. When assigning wafers to machines, it is important to consider both the quality and productivity perspectives. Major semiconductor companies in Korea have recently implemented a wafer assignment system to improve wafer yield, a critical measure for semiconductor quality. This system, however, does not consider the productivity perspective. This paper presents a systematic method for assigning wafers to maximize the wafer yield while satisfying a predetermined target level of productivity. A simple hypothetical example is presented to illustrate the method.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Narges Asadi, Mats Jackson, Anders Fundin To enable the production of high product variety, mix flexibility in assembly systems is of paramount importance for manufacturing companies. Mixed-product assembly lines (MPALs) are growing as the key means of realizing mix flexibility in many manufacturing sectors, as they absorb volume fluctuations and offer high product variety. With the increasing product variety in MPALs, these assembly systems are becoming more complex. However, the practical challenges of these assembly systems, in particular those concerning product design, have not been adequately addressed. By performing a case study of a heavy machinery manufacturing company, this paper investigates the implications of realizing mix flexibility in an assembly system for product modularity. The findings pinpoint the low level of product modularity in assembly as the most important challenge in MPALs. Accordingly, realizing mix flexibility in an MPAL impacts product modularity through establishing a common assembly sequence and defining similar module contents across distinct product families.
Abstract: Publication date: July 2019Source: Journal of Manufacturing Systems, Volume 52, Part AAuthor(s): Zhuoyu Huang, Jiwon Kim, Alireza Sadri, Steve Dowey, Matthew S. Dargusch As the next wave of productivity, Industry 4.0 aims to enhance the competitiveness and efficiency of manufacturers by bridging the gap between industrial manufacturing and information technology. Through digitalisation, it provides the advantage of enabling the real-time/near-real-time monitoring of manufacturing. This digital information allows monitoring tools such as Value Stream Mapping (VSM) to help the decision makers efficiently capture the non-value-adding processes on the factory floor. However, the application of VSM into small and medium sized enterprises (SMEs), including diverse manufacturing environments, is not an easy task. It is even more challenging especially when the product processing is more complicated and requires improvements to labour management and facility utilization. Conventional VSM is not competent to handle the contemporary rapid dynamic manufacturing environment, complex material flow or efficiency of machine and labour performance. These three are the most important resources on the shop floor to bring transparency to the decision maker. We present a multi-agent system composed of several cost effective embedded Arduino systems as agents and a Raspberry-Pi® as a core agent. Equipped with Cyber-Physical System (CPS) technology, these agents, placed on or near the station, can reflect the non-linear material value flow without modelling the process or using RFID tags. Moreover, through the sensor node installed in each machine and by knowing the staff ID, the agents could send the relevant information in the form of dynamic value stream mapping (DVSM) in near-real-time for storage, analysis and visualization. We present a suitable visualization tool based in Node-RED® to carry out DVSM.
Abstract: Publication date: Available online 22 April 2019Source: Journal of Manufacturing SystemsAuthor(s): Maxwell Praniewicz, Thomas R. Kurfess, Christopher Saldana Multi-axis machining processes are often used to fabricate complex components with tight geometric tolerances. Thus, the need for highly accurate 5-axis machine tools is imperative in high precision industries such as aerospace and mold and die. Often, construction errors result in geometric errors within the machine tool. These errors must be identified and compensated in order to guarantee accuracy of the machine. In this work, kinematic equations of motion for a BC-style machine tool were derived while incorporating the 8 distinct kinematic error constants associated with a 5-axis machine tool. A method is presented to derive these kinematic error constants from eccentricity values obtained using 3-axis simultaneous tests for table-table style 5-axis machine tools. To validate this method, error constants were input into the kinematic simulation. Eccentricity values were then output from the simulation and error constants were derived and compared to the input values. It was shown that if the procedure is followed, the error constants can be correctly derived and compensated. This method was then implemented on a BC-style machine tool and error constants were derived.
Abstract: Publication date: Available online 22 April 2019Source: Journal of Manufacturing SystemsAuthor(s): Brian K. Paul, Steven Kawula, Chuankai Song Heating, ventilation, and air conditioning systems represent nearly half of building energy usage in the United States. Recent building regulations requiring increased air turnover within buildings will result in even greater air conditioning usage. To alleviate these concerns, membrane-based heat-and-moisture exchangers known as energy recovery ventilators have been developed to reduce the energy expenditure of air conditioning systems by pre-conditioning incoming air through sensible and latent heat transfer with building air exhaust. In this work, we propose and validate a manufacturing process design for channel lamination based on surface mount adhesives capable of meeting the process requirements of a membrane-based ventilator. In particular, the device requires rib supports with height-to-width aspect ratios greater than one. The proposed manufacturing process design is capable of meeting this process requirement by stretching the adhesive after initial adhesive tacking. A manufacturing process flow diagram, a machine tool specification and a cost model are proposed for meeting process requirements. A set of design constraints are developed detailing the adhesive requirements necessary for meeting requirements. The cost model is validated by building a sub-scale mesochannel array demonstrating the ability to meet process requirements. Results show that additional work is needed to validate the curing step but that the process is capable of producing a mesochannel array with an acceptable level of variation.
Abstract: Publication date: Available online 22 April 2019Source: Journal of Manufacturing SystemsAuthor(s): Ji Hoon Kim, Lyang Suan Wang, Kaushalya Putta, Payam Haghighi, Jami J. Shah, Pete Edwards Multi-Material Joining Design Explorer is discussed in this paper, which is a knowledge-based advisory system to help structural designers at the early design phase to select the potential joining methods. Data mining on various joining methods was conducted from any available sources, such as experts from academia and industry, handbooks, and vendors. Collected data was organized in a concept map which is an informal way of representing the data structure. The data were arranged into several categories according to their characteristics which include joinable materials, mechanical and design requirements, geometry, and so on. Common parameters and unique parameters were extracted from deep investigation of the gathered data to create a formalized data structure. A database using a general tree structure was then created to be fed into the advisory system. Searching algorithm using SQL query was implemented to navigate through the database to find the joining methods that match the requirements defined by the user. Two test cases were generated to validate the function of the knowledge-based system.
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