Manufacturing Production Systems Research Articles

Simulation Models of Production Plants as a Tool for Implementation of the Digital Twin Concept into Production

The aim of the paper is to introduce the digital twin concept as part of the Industry 4.0 strategy. In the form of a case study, the procedure and outputs of the simulation of a specific production plant to-gether with its intermediate storage and output for the next plant are presented. In the research part is presented a simulation model of production lines and intermediate stock with material flow represen-tation. At the beginning of the research the analysis of production and logistics processes was carried out. The next part describes the programming methods used to record and redirect material flows between individual lines and stock. The simulation method using simulated production line models enables the digitization of dynamic production processes in enterprises. We expect that in the coming years there will be an increase in demand for the creation of simulation models of production systems in modern manufacturing companies that will try to implement the Industry 4.0 strategy and thus in-crease their competitiveness.

Control of key performance indicators of manufacturing production systems through pair-copula modeling and stochastic optimization

Key performance indicators (KPIs) modeling and control is important for efficient design and operation of complex manufacturing production systems. This paper proposes to implement the KPI control based on KPI modeling and stochastic optimization. The KPI relationship is first approximated using ordered block model and pair-copula construction (OBM-PCC) model, which is a non-parametric model that facilitates a flexible surrogate of the KPI relationship. Then, the KPI control is framed into a stochastic optimization problem, where the randomness in the cost function depends on the decision variables. To solve this stochastic optimization problem, the standard uniform distribution is employed to link the OBM-PCC model and the cost function to transform the problem into an ordinary stochastic optimization problem. The proposed method is efficient in KPI control and the performance is robust to the cost function. Extensive numerical studies and comparisons, together with a case study, are presented to demonstrate the effectiveness of the proposed KPI control framework.

A joint classification-regression method for multi-stage remaining useful life prediction

Remaining useful life (RUL) prediction plays an important role in increasing the availability and productivity of industrial manufacturing systems. This paper proposes a joint classification-regression scheme for multi-stage RUL prediction. First, the time domain and frequency domain features are extracted from various types of raw sensory data (e.g., acoustic, current, vibration and temperature) to constitute the training data set. Second, the system health stage is classified based on the trained model and real-time sensory data. Third, we perform stage-level RUL prediction with regression algorithm to estimate overall useful life. Distinct from the existing RUL estimation algorithms, the proposed multi-stage remaining useful life (MS-RUL) prediction effectively integrates the machine/deep learning based classification and regression to improve overall estimation accuracy. We conduct the performance evaluation with sensory data from real manufacturing systems. Experimental results demonstrate that the proposed MS-RUL achieves approximately 6.5% accuracy improvements over the state-of-the-art algorithms in the RUL prediction.

CHALLENGES AND CONCEPTUAL FRAMEWORK TO DEVELOP HEAVY-LOAD MANIPULATORS FOR SMART FACTORIES

Industry 4.0 has been one of the emerging topics in recent years, covering a wide range of concepts and applications as well as political, economic and technological views. Manufacturing is becoming smarter and smarter at all levels, moving toward the concept of Smart Factory (SF), based on the advancements of digital transformation technologies, including Artificial Intelligence (AI) and bigdata analytics, and abilities to learn, configure and execute with cognitive intelligence of smart machines and automation systems. However, the SF adoption in practice, especially in Small and Medium-sized Enterprises (SMEs), is still in the early stage. In addition, there are growing demands of product personalisation, mass-customisation and diversification. Therefore, the involvement of humans is still importantly required in many production processes in SF models, where smart machines, smart manipulators, collaborative robots and Automated guided vehicles (AGVs) are required to co-work with humans, leading to an important concern of safety, reliability, productivity and quality of smart manufacturing systems. In this paper, challenges and a proposed conceptual framework to develop smart heavy-load manipulators are presented, with the focus on the cost-effectiveness and applicability in industrial practices of SF for SMEs.

A Computing Model: The Closed-Loop Optimal Control for Large-Scale One-of-a-Kind Production Based on Multilevel Hierarchical PERT-Petri Net

For the design and operation of interim product manufacturing systems, large-scale one-of-a-kind production (OKP) features disruptions and lack of synchronization, which challenges real-time production process systematic control. The difficulty lies in two aspects. First, large-scale OKP is generally characterized by product design uncertainty and changes in resource availability, which often causes varied working time with workforce allocation variations. Second, specific types of large-scale OKP provides discrete production with complicated structures. There is the complicated work breakdown structure (WBS) of top-down and simple-to-complex refinement. Moreover, the production task relation structure in WBS, which consists of tandem structure, parallel structure and double-level-nested parallel structure, is complex. Indeed, many large-scale OKP enterprises do not have an effective method to both control and plan adjustment in a systematic way for dealing with the progress of OKP operations under workforce allocation disruptions and different working time scenarios. The traditional production project management and control system, theory, and methods do not handle this situation well since these technologies are developed with a view to managing production periods instead of controlling time-related cost systematically during production. Regarding the interim product production process in large-scale OKP as a manufacturing system, in this article we propose a cost dynamic control and optimization method based on the multilevel hierarchical PERT-Petri net (MLHPP) to achieve a closed-loop production dynamic control structure in large-scale OKP. The proposed modeling method and algorithms, through an industrial implementation in shipbuilding interim production (e.g., a ship block building), demonstrate a computing model for structuring and controlling large-scale OKP systems.

A new graph-based semi-supervised method for surface defect classification

Abstract Vision-based defect classification is an important technology to control the quality of product in manufacturing system. As it is very hard to obtain enough labeled samples for model training in the real-world production, the semi-supervised learning which learns from both labeled and unlabeled samples is more suitable for this task. However, the intra-class variations and the inter-class similarities of surface defect, named as the poor class separation, may cause the semi-supervised methods to perform poorly with small labeled samples. While graph-based methods, such as graph convolution network (GCN), can solve the problem well. Therefore, this paper proposes a new graph-based semi-supervised method, named as multiple micrographs graph convolutional network (MMGCN), for surface defect classification. Firstly, MMGCN performs graph convolution by constructing multiple micrographs instead of a large graph, and labels unlabeled samples by propagating label information from labeled samples to unlabeled samples in the micrographs to obtain multiple labels. Weighting the labels can obtain the final label, which can solve the limitations of computation complexity and practicality of original GCN. Secondly, MMGCN divides unlabeled dataset into multiple batches and sets an accuracy threshold. When the model accuracy reaches the threshold, the unlabeled datasets are labeled in batches. A famous case has been used to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed MMGCN can achieve better computation complexity and practicality than GCN. And for accuracy, MMGCN can also obtain the best performance and the best class separation in the comparison with other semi-supervised surface defect classification methods.

Expanding Industrial Architecture Through Creativity in Manufacturing

Throughout the decades, industrial firms have sought clearly in calling on industry production experts to production more producible prototypes for manufacturing purposes. This can typically be accomplished by evaluating the current product production manufacturing system capabilities, and difficulties often arise due to challenges to combining product description and manufacturing operations. This paper will discuss how advances in manufacturing (as new fabrication capabilities) will add to the industrial design space for product design engineers, enabling more design flexibility and increasing efficiency by eliminating and/or loosening constraints on product designs. The findings from a series of industrial case studies provide encouraging effects on how technological technologies will positively impact the growth of industrial design engineers' solution space. The findings from a series of industrial case studies provide encouraging effects on how technological technologies will positively impact the growth of industrial design engineers' solution space. To order to foster product creativity, design managers are expected to test new technologies, new design techniques and new manufacturing (working) practices. Keywords: Industrial Design; Design for X; Design to X, Product Innovation, Manufacturing Systems, Production Innovation. DOI: 10.7176/JRDM/71-05 Publication date: November 30 th 2020

Optimizing a cyanobacterial biofertilizer manufacturing system for village-level production in Ethiopia

A series of experiments in laboratory and hoop house environments was conducted to develop a cyanobacteria-based biological N fertilizer source that can be produced in Ethiopian villages, thus providing an alternative to expensive, imported urea. In the laboratory, factorial combinations of four Anabaena species strains (E-2, E-3, E-5, and E-9) isolated from Ethiopian soils and three water sources (river, lake, and ground water) were laid out in a complete randomized design in a light box constructed for this purpose with three replications. Factorial combinations of two pond-lining materials (transparent and black plastic sheets) and two aeration intervals (1-hour and 30-min intervals) were laid out in a complete randomized design with three replications in a hoop house using small ponds (1 m × 2 m × 0.25 m). Cyanobacteria strain E-3 performed best in the river water and was selected based on significantly higher values of optical density, growth rate, heterocyst frequency, and total Kjeldahl nitrogen than the other strains. DNA sequencing showed that clones of E-3 shared 99% similarity with Anabaena oscillariodes. Moreover, transparent plastic–lined ponds with a 1-hour aeration interval provided the best growing conditions for the E-3 strain based on its significantly higher dry biomass, optical density, and growth rate under these conditions as compared with ponds lined with black plastic or aerated in 30-min intervals. Therefore, transparent plastic–lined ponds with 1-hour aeration intervals are recommended for the mass production of A. osciolloides strain E-3 for use as a biofertilizer.

YAPI ÜRETİMİNDE EKLEMELİ İMALAT TEKNOLOJİLERİNİN KARŞILAŞTIRMALI DEĞERLENDİRMESİ

Bu çalışma, mimarlık alanında kullanılan eklemeli imalat teknolojileri konusunda gerçekleştirilen bir derleme çalışmasıdır. Temel olarak, eklemeli imalatın mimarlık alanındaki uygulama alanlarını, belirli sistematik içerisinde ele alan araştırma, gerek literatürde yer alan örnekler, gerekse uygulamaları bulunan yeni teknolojiler hakkında bilgi vermeyi amaçlamaktadır. Çalışmada, karşılaştırmalı olarak incelenen üretim teknolojilerinin güçlü ve zayıf yönleri, sistemlerin kullanım alanları, sistemlerde kullanılan malzemeler, mimari alan uygulamaları özelinde ele alınmıştır. Eklemeli imalat teknolojilerinin dezavantajlarından birisi olarak bilinen yüksek üretim ve malzeme maliyetinin, zaman içerisinde üretim yöntemlerinin yaygınlaşması ile çözümleneceği öngörülmektedir. Malzeme üretimi ve teknoloji konusunda halen gelişme sürecinde olan eklemeli imalat üretim sistemleri, gelecek endüstri uygulamalarında ve mimarisinde etkin olarak kullanılacağı düşünülmektedir. Yüksek maliyet sorununa çözüm olarak, eklemeli imalat yöntemi ile kalıp üretimlerinin ilerleyen yıllarda daha fazla gündemde olacağı öngörülmektedir.

Integrated Control Policy for a Multiple Machines and Multiple Product Types Manufacturing System Production Process with Uncertain Fault

The optimization of production cost has always been a key issue in manufacturing systems; for the single product type manufacturing systems, lots of research studies have proved the validity of the hedging point control policy in production cost control. However, due to the complexity of the multiple machines and multiple product types manufacturing systems with uncertain fault, it is difficult to achieve a good control effect only by using the hedging point control policy. To optimize the total production cost under constantly changing demands, an integrated control policy that combines the prioritized hedging point (PHP) control policy with the production capacity planning during production is proposed, and the decision variables are obtained by a particle swarm optimization (PSO) algorithm. The simulation experiments show the effectiveness of the proposed integrated control policy in production cost control for the multiple machines and multiple product types manufacturing system.

A review of the application of component-based software development in open CNC systems

The development of open CNC systems is a popular topic in the past three decades. From the perspective of software engineering, most open CNC prototypes are developed based on the component-based software development (CBSD) approach. That is, they are constructed by composing functional or logical components. This paper retrospects the development of CBSD-based open CNC systems. These systems are analyzed from three aspects. For the component granularity aspect, CNC domain analysis and component reusability are discussed. For the component model aspect, component models used in open CNC systems are summarized, illustrated, and discussed. Likewise, for the software architecture aspect, architecture styles are summarized, described, and analyzed. But due to the diversity and platform dependency of component models, there is no widely accepted platform that can collect and execute heterogeneous CNC components. Service is an evolution of components. It makes a traditional component to be platform-independent and supports communication via message. Thus, a service-oriented architecture (SOA)-based universal open CNC platform is an attractive development trend of diverse CBSD-based open CNC systems. And its development is similar to the development of the CBSD-based system, but more complicated because it contains more domain logic. Therefore, this paper provides a technical foundation for developing SOA-based open CNC platform which is a part of novel automation patterns, such as cloud manufacturing and cyber-physical production system.

Modularity measures: Concepts, computation, and applications to manufacturing systems

AbstractWe propose a measure to quantify the modularity of industrial production (manufacturing) systems and optimization formulations to compute it. From a manufacturing perspective, we argue that a system is deemed modular if: (a) the equipment units that comprise it form clusters (modules) of dense connectivity (i.e., difficult module assembly tasks are performed off‐site), (b) connectivity between modules is sparse (i.e., easy assembly tasks are performed on‐site), (c) the number of modules is small, and (d) the module dimensions facilitate transportation. We show that the measure proposed satisfies these requirements and that it can be computed by solving a convex mixed‐integer quadratic program. We provide a discussion on advantages and disadvantages of alternative modularity measures used in different scientific and engineering communities. Our results seek to highlight conceptual and computational challenges that arise from the need to define and quantify modularity in a manufacturing context.

Integrated maintenance policies for performance improvement of a multi-unit repairable, one product manufacturing system

Planned and unplanned downtime emanating from maintenance, production, and operational function adversely affects the performance of manufacturing plants. This paper develops joint maintenance, production and process/operations control policies integrating Opportunistic Maintenance (OM), which takes advantage of external (operational-related downtimes) and internal maintenance opportunities. The study quantifies the effects of maintenance strategies on the plant’s performance, here, the overall equipment effectiveness (OEE), which importantly presents insights on maintenance decision support for production facilities facing significant operational-related stoppages. The study integrates both economic and structural dependence and models the influence of alternative maintenance actions of different maintenance policies, on product quality. The developed model is validated by applying to a multi-unit repairable, imperfectly maintained raw meal grinding system of a cement plant. From the simulation modelling results, integration of the opportunistic maintenance approach, complementary to corrective, preventive and condition-based maintenance strategies, show more enhanced equipment performance, as measured through the OEE.

A systematic approach for responsiveness assessment for product and material flow in reconfigurable manufacturing system (RMS)

A reconfigurable manufacturing system (RMS) quickly changes machine configurations to adapt to variations in demand related to volume of product and design. A decision of machine configuration change is taken after considering many factors, such as time, effort and cost. This research proposes a systematic approach for responsiveness assessment for product and material flow in reconfigurable manufacturing system (RMS) by considering product and process characteristics. The proposed system uses three important metrics, namely reconfiguration effort, reconfiguration time and non-productive reconfiguration cost. The usefulness of the developed system is demonstrated with the help of an industrial case study of three hypereutectic aluminium automotive pistons from the same part family. These pistons are made of FM-425 aluminium alloy that contains 16.5 percent of silicon. The system is suitable for comparing and selecting efficient machine configurations in a reconfigurable manufacturing system.

Product design and manufacturing system operations: An integrated approach for product customization

To stay competitive in the marketplace, manufacturing firms that rely on high product variety should involve the customer in the product design process. However, customer interaction with the product at the design level could compromise the product functionality and the operational arrangement of the manufacturing system. To address these challenges, we aim to align the preferences of the design and manufacturing teams within a manufacturing firm in the context of mass customization. The preference of the design team is in terms of product modularity, associated with product upgradeability and flexibility. In the manufacturing team, complexity is a preference associated with cost and quality. In our research, the motivation for selecting these attributes is that product modularity is used to deal with product variety, and complexity is related to the uncertainty of product manufacturing. These two perspectives could support the manufacturing firm decision of selecting which product architecture is more convenient for a given operational arrangement and vice versa. We use a Multi-Attribute Utility function to assess the preferences of the design and manufacturing teams in terms of modularity and manufacturing complexity. We analyze the functional requirements of the product and then their mapping into the product architecture. Finally, we study the relationship of the product components to the operational arrangement of the manufacturing system. In addition, we include a case study in which we analyze two products and their relationship to a single manufacturing system, where the highest utility produces the best relationship between product design and manufacturing configuration complexity. By using this integrated approach, a manufacturer can decide to update the product design, the related operational configuration, or both.

CNC router selection under dynamic fuzzy environment

To cut several types of hard materials in manufacturing, Computer Numeric Control (CNC) router machines are commonly used. The tasks to be done by different machines can be performed by a single CNC router machine. Production of parts with better quality is possible at lower costs by production with CNC router machines, and these machines improve the productivity of manufacturing system. For these reasons, determination of the appropriate CNC router machine for manufacturing systems is a crucial decision. Different factors related to properties of machines are effective on the decision. Therefore, decision makers must include different effective aspects into decision process. Under this consideration, an analytic selection procedure for CNC router machines by taking uncertain expressions of experts on the selection criteria and variable values occur over time is proposed in this study. In order to handle the modelling difficulty of uncertainty of the statements and the value changes by time, dynamic intuitionistic multi attribute decision making is used to select the best CNC router. Applicability of the proposed selection procedure is demonstrated on an application, and a comparative analysis with dynamic neutrosophic multi attribute decision making is presented.

Design of a methodology to incorporate Lean Manufacturing tools in risk management, to reduce work accidents at service companies

Lean Manufacturing tools were originally developed to improve manufacturing production systems and enhance productivity and quality in organizations. For this reason, they are used more frequently in manufacturing than in service companies. The Ergo-Lean approach applies Lean Manufacturing to the field of Occupational Safety and Health, focusing on reducing activities that do not add value and on reducing the associated muscular-skeletal disorders. The purpose of this study is to integrate Lean Manufacturing tools in risk management to help control work-related accidents. Based on work accident data at a service company, the causes of the accidents were analyzed and the main Lean Manufacturing tools were reviewed and integrated, in order to develop a new methodology for the implementation of these tools to help prevent work-related accidents and diseases and to develop a culture of self-care.

Effect of 3D Printing Technology on 3C Product Manufacturing

3D printing technology, as one of the most subversive technologies in the 21st century, processes the entity through layer by layer superposition. It breaks through the design constraints and adapts to customized production with high flexibility, which is an important direction of industrial development in the future. 3C product has a very board application market and groups, so it has been widely valued over the world. The advantages of 3D printing technology are very suitable for the characteristics of 3C product, such as diversification, small-volume, personalized. Here, the impact of 3D printing technology on manufacturing industry is firstly discussed; 3C product manufacturing system integrated with 3D printing is finally put proposed. The advantages of the proposed system possess shorter manufacturing cycle, supply chain and logistics. It makes easier to realize personalized 3C product, localized manufacturing and reduce cost. The development and wide application of 3D printing technology in 3C product industry will make the manufacture of low-cost small batch or even individual customized products into a general mode towards the trend of “light luxury + light customization”.

Performance evaluation of the supply chain system of a food product manufacturing system using a questionnaire-based approach

Effective management of the end-to-end process of a food industry is vital for the achievement of the key strategic objectives of this organisation. However, various factors have limited the performance and productivity of the supply chain system of food industries; thus hindering an organisation from meeting its customers demand target. Hence; there is a need to appraise the performance of the supply chain system of a food industry with a view to identify factors limiting its performance and ensure continuous end-to-end process improvement. In light of this, this paper assesses the performance of the supply chain system of a food industry using a questionnaire-based approach. The structure of the questionnaire consists of myriad of questions that appraises the performance of various factors responsible for effective functioning and optimal flow of the supplier and production sections of the supply chain of a food industry based on the industry benchmark for these factors. The production operators responded to the appropriate sections of the questionnaire. The questionnaire result revealed that the overall performance of work stations 1, 3, 4 and 5 are below the organisation’s target. Hence, the resolutions of various factors lowering the performance of these work stations were recommended as future studies. The questionnaire developed in this study serve as a template that could be used by supply chain managers to measure the performance of their supply chain systems, with a view to ensure continuous and sustainable end-to-end process improvement.

Using Graphs to Link Data Across the Product Lifecycle for Enabling Smart Manufacturing Digital Threads

Smart manufacturing promises to provide significant increases in productivity and effectiveness of manufacturing systems by better connecting the data from people, processes, and things. However, there is no uniform, generalized method for deploying linked-data concepts to the manufacturing domain. The literature describes and commercial vendors offer centralized data repository solutions, but these types of approaches quickly breakdown under the intense burden of managing and reconciling all the data flowing in and out of the various repositories across the product lifecycle. In this paper, we introduce a method for linking and tracing data throughout the product lifecycle using graphs to form digital threads. We describe a prototype implementation of the method and a case study to demonstrate an information round-trip for a product assembly between the design, manufacturing, and quality domains of the product lifecycle. The expected impact from this novel, standards-based, linked-data method is the ability to use digital threads to provide data, system, and viewpoint interoperability in the deployment of smart manufacturing to realize industry's $30 Billion annual opportunity.