Custom Manufacturing Research Articles

Efficiency of optimization algorithms on the adjustment of process parameters for geometric accuracy enhancement of denture plate in single point incremental sheet forming

Single point incremental forming is a sheet metal forming technique with great potential for use in prototyping and custom manufacture. Although this technology has undergone considerable development in recent years, it still suffers from low geometrical accuracy in terms of its application in the industry. Therefore, solutions for errors reduction or compensation are required to improve the process. In this paper, an optimization procedure of the geometric precision, based on genetic algorithm, global optimum determination by linking and interchanging kindred evaluators solver and newly developed algorithm called grasshopper optimization algorithm, is tested and doubly validated numerically and experimentally. The denture plate part simultaneously simulated and manufactured shows how it is possible to obtain sound component with reduced geometric errors such as springback, bending and pillow effect errors by properly chosen optimal process parameters. The results indicated that the reduction in the shape defects between the obtained geometry and the target model generated by computer-aided design can be achieved through coupling of numerical simulations and optimization techniques.

New firm to restart functional-dye plant

American Chemical Solutions (ACS) has been formed to acquire and restart an Esco chemical plant in Muskegon, Michigan. The new company was launched by Todd Zahn, a chemist who is also CEO of the boron intermediates maker BoroPharm. ACS says it will invest $15 million to restart the plant’s production of functional dyes for thermal and carbonless printing and to provide custom manufacturing services.

Laser-Based Hybrid Manufacturing of Endosseous Implants: Optimized Titanium Surfaces for Enhancing Osteogenic Differentiation of Human Mesenchymal Stem Cells

Additive manufacturing (AM) is becoming increasingly important in the orthopedic and dental sectors thanks to two major advantages: the possibility of custom manufacturing and the integration of complex structures. However, at smaller scales, surface conditions of AM products are not mastered. Numerous non-fused powder particles give rise to roughness values (Sa) greater than 10 μm, thus limiting biomedical applications since the surface roughness of, e.g., metal implants plays a major role in the quality and rate of osseointegration. In this study, an innovative hybrid machine combining AM and a femtosecond laser (FS) was used to obtain Ti6Al4V parts with biofunctional surfaces. During the manufacturing process, the FS laser beam "neatly" ablates the surface, leaving in its path nanostructures created by the laser/matter interaction. This step decreases the Sa from 11 to 4 μm and increases the surface wettability. The behavior of human mesenchymal stem cells was evaluated on these new AM+FS surfaces and compared with that on AM surfaces and also on polished surfaces. The number of cells attached 24 h after plating is equivalent on all surfaces, but cell spreading is higher on AM+FS surfaces compared with their AM counterparts. In the longer term (days 7 and 14), fibronectin and collagen synthesis increase on AM+FS surfaces as opposed to AM alone. Alkaline phosphatase activity, osteocalcin production, and mineralization, markers of osteogenic differentiation, are significantly lower on raw AM surfaces, whereas on the AM+FS specimens they display a level equivalent to that on the polished surface. Overall, these results indicate that using an FS laser beam during the fabrication of AM parts optimizes surface morphology to favor osteoblastic differentiation. This new hybrid machine could make it possible to produce AM implants with functional surfaces directly at the end of AM, thereby limiting their post-treatments.

Company Profile: Aldevron Expands to Facilitate Emerging Therapies.

Company Profile: Aldevron Expands to Facilitate Emerging Therapies.

A framework and implementation of Customer Platform-connection manufactory to service (CPMS) model in product service system

With the continuous improvement of living standards, customers are gradually pursuing products which are individualization and entertainment. The traditional large-scale manufacturing mode cannot meet the individual needs of customers. The production mode of the enterprises has been changed from the traditional inventory mode to the customization manufacturing mode driven by the customer orders. Furthermore, the simple customization model cannot meet the industrial needs in the open and networked era. Therefore, there is a lack of an innovative manufacturing mode that not only meets the individual needs of customers, but also adapts to today’s open and networked environment. In this paper, a new customization model was proposed, which is a continuously improved customization model called CPMS (Customer Platform-connection Manufactory to Service). By means of research methodology of literature review and analysis, the birth, concept, and evolution of CPMS in PSS are discussed. Through analysing the development of industry, combining with the existing analysis and projects we involved in, and the method of system engineering analysis, an overall framework of CPMS model is derived. At last, a case study for implementation of CPMS in the car industry has been given. Under this model, enterprises can improve the flexible production capacity of production line, reduce the production cost, reduce inventory, improve the modular configuration of products, and etc. Moreover, it can provide some reference value for enterprises to plan, design, implement and apply a new customization production model that conform to the requirements of the new era of open and networked.

Blockchain-based reputation management for custom manufacturing service in the peer-to-peer networking environment

Blockchain enables users to be autonomous without the need of trust by using a digital ledger of decentralized consensus. Blockchain-based IoT allows service providers to transfer the security and maintenance responsibility to self-maintaining customers. Adopting peer-to-peer (P2P) networking and computing for more than billions of transactions can reduce the costs arising from the installation and maintenance of centralized systems. Small manufacturers providing industrial IoT (IIoT) services can participate more actively in blockchain-based IIoT applications with three-dimensional printing and digital manufacturing technologies, but the measure to maintain privacy on a blockchain is not robust. Here, we propose a P2P networking-based custom manufacturing service, which is an order-driven trading service between a manufacturer and a customer in the blockchain-based IIoT architecture. The proposed system consists of reputation management and service architecture. We propose a new reputation assessment method customized to increase the reliability and accuracy of industrial manufacturing systems. We also propose a manufacturer rating classification to guide the customers’ decision making in a reliable manner and a malicious evaluator identification to exclude feedbacks from malicious evaluators. The proposed service architecture is composed of trustless P2P protocols designed for preserving privacy and providing non-repudiation. We used a cryptographic algorithm for ensuring transaction privacy and the digital signing of blockchain for non-repudiation. We also analyzed the proposed service architecture and the possible attack scenarios to verify the security requirements. We verified that the reputation management system was influenced by each feedback dynamically and guided the customer’s present decision making with reliable and classified manners, by simulating reputation classification and malicious evaluator identification. Further, we have summarized the originality and the characteristics of the proposed approach by comparing closely related studies and concluded with a future research guide.

Catalent will enter gene-therapy service market

Seeking to enter the business of gene-therapy manufacturing, the drug service firm Catalent Pharma Solutions has agreed to pay $1.2 billion for the custom manufacturer Paragon Bioservices. Paragon will put Catalent in the business of making adeno-associated virus vectors, plasmids, and lentivirus vectors used to deliver gene and cell therapies. Earlier this month, Paragon opened a gene-therapy manufacturing facility in Maryland’s Anne Arundel County, complementing the production capacity at its headquarters at University of Maryland’s BioPark. The new capacity will support customers’ clinical- and commercial-stage programs. After the Catalent deal was unveiled, Paragon announced it will expand its relationship with a key customer, Sarepta Therapeutics. In October, Sarepta enlisted Paragon to help make its experimental Duchenne muscular dystrophy gene therapy; now, Paragon and Sarepta are considering a joint venture to manufacture gene therapies at a new site. The deal for Paragon, which expects to have sa...

Dyadic alignment in capital goods companies

PurposeThe purpose of this paper is to identify the main factors of the dyadic alignment in the supply chain of capital goods companies.Design/methodology/approachA survey was conducted among 159 respondents (53 supplier companies, 53 manufacturers and 53 clients).FindingsUsing structural equation modelling, no evidence of alignment between suppliers and manufacturers was identified. However, for the manufacturers, there is a partial mediation effect of the operational capabilities in the relationship between supply chain management and business performance.Originality/valueThis research investigates whether there is a dyadic alignment among supplier–manufacturer, manufacturer–customer and supplier–customer from the capital goods manufacturers’ perspective.

Top Management, Green Innovations, and the Mediating Effect of Customer Cooperation in Green Supply Chains

This study examines the mediating role of customer cooperation in green supply chain relationships. Employing 181 manufacturer–customer relationships, we test the mediating effect of customer cooperation on the process and managerial innovations. As proposed, customer cooperation positively mediates the relationship between top management commitment and process innovation. However, customer cooperation fails to mediate the relationship between top management commitment and managerial innovation. The findings suggest that the magnitude of customer cooperation increases the adoption of green innovations between supply chain partners to mitigate the negative impact on the environment. Furthermore, customer cooperation enforces a sense of mutuality between supply chain partners to diminish the impact of carbon footprint.

Joint production and maintenance operations in smart custom-manufacturing systems

Machines in custom manufacturing environments with IoT (Internet-of-Things) capability are predicted to be pervading enterprises. However, there is a need to develop new algorithms that reap the benefits of such technologies. We consider a system where jobs with stochastic workloads arrive to a machine in an arbitrary fashion and upon arrival, their workload is revealed (enabled by IoT). The tool on the machine gets used up based on the speed at which the jobs are processed. Knowing that tool-replacement consumes a significant amount of time, we want to develop online algorithms that maximize the capacity of the machine by determining: (i) the speed at which each job is processed; and (ii) the epoch when the tool is replaced. We provide online approaches that leverage the ability to reveal workload in real-time and effectively balance future uncertainties. We derive asymptotic bounds for the online algorithm performance and show using numerical experimentation that a little revealed information could result in a tremendous improvement in performance. Our online algorithms also work under realistic conditions of non-stationary batch arrivals and correlated workloads. Our work opens up research directions for a variety of operational settings that may benefit from revealing stochastic quantities by mining information.

A novel approach for feature fatigue analysis using HMM stemming and adaptive invasive weed optimisation with hybrid firework optimisation method

Due to the rapid growth of customer product reviews in e-commerce website makes the new online customer to analyse reviews to know about the features of the product that they want to buy. Integrating many features into a single product provides more attractive which makes the customer to buy that product, after worked with the high-feature product; the customer may get dissatisfied which eventually reduces the manufacturer's Customer Equity (CE). Thus, it is necessary to analyse the usability of the product. In this paper, k-optimal rule discovery technique with adaptive invasive weed optimisation is proposed to help designers to find an optimal feature that provides the decision supports for product designers to enhance the product usability using Hybrid firework optimisation. Then, the Feature Fatigue (FF) is alleviated efficiently. The proposed approaches are experimented and result shows that proposed work achieves 97% accuracy which is higher than existing work.

Manufacture of an aerospace component by single point incremental forming

Abstract Single point incremental forming has shown promising application in the field of rapid prototyping and custom manufacture. De- pending on the required geometry of the component, one or multi-stage forming might be required for the successful manufacture. In this paper, a C-channel designed for the vibrational testing of airplane fuselage system was developed. The final component was developed through trial and error using a combination of strategies. Conventional downward strategy was found to be successful in the manufacture of the component. The intermediate geometries employed for the successful manufacture are presented. Geomet- rical errors in the resulting component are also reported. This work will be of interest to anyone seeking to employ conventional downward strategy to manufacture a complex part. The results show that forming complicated shapes successfully requires trial and error experimental work, and knowledge of the process. The developed component still may have unacceptable geometrical errors and thickness variation.

Multiobjective Optimization Model of Production Planning in Cloud Manufacturing Based on TOPSIS Method with Combined Weights

Combined with the research of mass customization and cloud manufacturing mode, this paper discussed the production planning of mass customization enterprises in the context of cloud manufacturing in detail, analyzed the attribute index of manufacturing resource combination, and given a system considering the characteristics of batch production in mass customization and the decentralization of manufacturing resources in cloud manufacturing environment. Then, a multiobjective optimization model has been constructed according to the product delivery date, product cost, and product quality that customers care most about. The Pareto solution set of production plan has been obtained by using NSGA‐II algorithm. This paper established a six‐tier attribute index system evaluation model for the optimization of production planning scheme set of mass customization enterprises in cloud manufacturing environment. The weight coefficients of attribute indexes were calculated by combining subjective and objective weights with analytic hierarchy process (AHP) and entropy weight method. Finally, the combined weights calculated were applied to the improved TOPSIS method, and the optimal production planning scheme has been obtained by ranking. This paper validated the effectiveness and feasibility of the multiobjective model and NSGA‐II algorithm by the example of company A. The Pareto effective solution has been obtained by solving the model. Then the production plan set has been sorted synthetically according to the comprehensive evaluation model, and the optimal production plan has been obtained.

Heuristic dynamic approach to perishable products in presence of deterioration effect

Joining warehouses and suppliers facilities to deliver the finished product to the end customer is a complex process that requires extensive consideration. The resulting chain is an integration of such entities as the supplier, manufacturer, distributor, warehouse, retailer, and end customer. A perishable product is any product that can rot, spoil, or deteriorate rapidly and, soon after manufacture, may become unusable or obsolete. Perishable products thus have special nutritional characteristics that necessitate care and unique treatment for them. Such products can be anything that becomes outdated a short time after production or harvest, such as fruits, vegetables, meat, certain drinks, blood, and pharmaceuticals. The objective of this study is to find the best heuristics for distributing multiple perishable products as early as possible to maximize profit. Case studies involving featuring perishable products at different rates of degradation with multiple retailers and limited transportation capacity were carried out to demonstrate the effectiveness of the proposed method.

Implementation of a Message-driven Manufacturing Execution Workflow System

Manufacturing Service is an important part of PSS. However, production process in manufacturing service changes frequently, and this has greatly reduced the productivity and agility. Therefore, this paper proposes a message-driven workflow to fit the production process. The framework of message-driven manufacturing execution workflow includes: Manufacturing Execution Workflow module(MEW), Instant Messaging Server (IMS) and Instant Messaging Client (IMC). The function of MEW is mainly used to generate a request message for an operation event, which is generated in executing workflow transaction operations by a related role. The main function of IMS is to complete the message push mechanism. The IMC is used to receive the message transmitted by the instant message server, and users corresponding to the relevant role can process the message in workgroups. This paper proposes a mechanism for message-driven manufacturing execution workflow. This mechanism packages the tasks generated in the MEW through the custom Manufacturing Transaction Protocol (MTP) and Instant Messaging Protocol (IMP), and sends them to the process participants in the IMC to drive the execution of the entire process. On the one hand, the mechanism can integrate with ERP to improve the efficiency of business processing. On the other hand, as a workflow product, it can solve the business diversity or reconstruction problems that occur in enterprises.

Real-Time manufacturing optimization with a simulation model and virtual reality

This paper presents the main steps towards Real-Time (RT) manufacturing optimization with a simulation model and Virtual-Reality (VR) methods and equipment. Real-world production system characteristics are applied to a simulation model in the simulation software Simio, using different simulation scenarios. We want to determine the possible bottlenecks, the desired cycle time, the consequences of the system failures and maximum processing time at critical points in the production system. Using VR Oculus Rift equipment, we propose a Real-Time optimization method for the purpose of production optimization. Practical implementation of VR methods allows RT production system optimization and high-resolution graphical presentation of the simulation model. The simulation results obtained are presented numerically and graphically, the findings and suggestions are given for the optimization of the production system. The basic contribution of the article is the implementation of discrete system simulations and VR methods for the purpose of RT decision-making. Due to the complexity of high customization manufacturing systems, RT optimization is vital for obtaining a cost efficient production system, especially when we talk about a high-mix low-volume production system.

Optimal Control of Production and Maintenance Operations in Smart Custom Manufacturing Systems with Multiple Machines

Enterprises equipped with IoT (Internet of Things) are the new generation of manufacturing industry. There is a need for new optimization models which incorporate the advantages of IoT. In this paper, a new mathematical model and heuristic algorithm are developed to minimize the total cost in a multiple machine environment which enables the industries to take economically better decisions and effectively use their resources. A heuristic algorithm is developed for identical machines which process with the same tool. A system in which jobs with stochastic workloads arrive randomly and upon arrival, their workload is facilitated by IoT. The proposed algorithm determines the assignment of workload to the machine and processing speed. The algorithm works for both online and offline frameworks.

Production as a Service: A Digital Manufacturing Framework for Optimizing Utilization

In current practice, product developers with customized small batch production needs come across the problem of finding capable and flexible manufacturers, whereas manufacturers face underutilization due to inconsistent demand. This paper presents a Production as a Service (PaaS) framework to connect users (consumers or product developers) who have customized small batch manufacturing needs with manufacturers who have existing underutilized resources. PaaS is a cloud-based, centralized framework based on a service-oriented architecture that abstracts the manufacturing steps of a product as individual (production) service requests. Using PaaS, the user is able to reach many capable manufacturers at once and receive quotations for the production request. On the other end, PaaS reduces the effort required to find new customers and enables the manufacturers to easily submit quotations to increase the utilization of their resources. The functionalities and concepts defined in this paper are illustrated through case studies. Note to Practitioners-In order to transition product fabrication from the design phase to manufacturing, there is a need for identifying capable manufacturers with available resources that could be paired with user requirements. We propose Production as a Service (PaaS) in this paper and present initial implementations of the front-end and back-end components with a customizable optimization algorithm. Proposed abstractions and data structures make PaaS a unique, efficient, and intellectual property preserving framework. The current implementation of the framework has been tested with new designs. The PaaS framework has the potential to scale over a large network of manufacturers to effectively coordinate geo-distributed manufacturers for custom manufacturing needs.

Service composition and optimal selection in cloud manufacturing: landscape analysis and optimization by a hybrid imperialist competitive and local search algorithm

Cloud manufacturing as an emerging service-oriented manufacturing paradigm integrates and manages geographically distributed manufacturing resources such that complex and highly customized manufacturing tasks can be performed cooperatively. The service composition and optimal selection (SCOS) problem, in which manufacturing cloud services are optimally selected for performing subtasks, is one of the key issues for implementing a cloud manufacturing system. In this paper, we propose a new mixed-integer programming model for solving the SCOS problem with sequential composition structure. Unlike the majority of previous research on the problem, in the proposed model, the transportation between distributed resources and its effects on quality of services are considered. Although a wide variety of metaheuristics have been tailored for solving the SCOS problem, no consistent and comprehensive conclusion has been reached so far on the superiority of a specific algorithm. Therefore, for the first time, solution space landscape of the problem was analyzed through several statistical criteria which demonstrated that the landscape is rugged and local optima are clustered in a small region of the search space. Therefore, to find good solutions, a metaheuristic algorithm needs to perform both proper exploitation and exploration of the search space. According to the landscape analysis, the basic imperialist competitive algorithm (ICA) was hybridized with a local search (LS) algorithm resulting in the hybrid ICA (HICA). To examine the performance of the proposed HICA, an example of online motorcycle production in the USA as well as four randomly generated large-scale instances, were solved through the LS, ICA, and HICA. Computational results showed that transportation consideration is indispensable for obtaining more realistic solutions in cloud manufacturing. The results also revealed that the HICA outperformed the LS and basic ICA in terms of the value of cost objective function, the stability of solutions and convergence speed. Hence, not only statistically but also analytically, it was proved that algorithms incorporating both exploitation and exploration are able to solve the SCOS problem more efficiently.

Three-dimensional Printing Technology and Role for Custom Implants in Orthopedic Oncology

Three-dimensional (3D) printing is an evolving technology that involves the sequential application of material to a flat surface to gradually build structures from the base up. This technique of “additive manufacturing” has a broad range of applications in the medical field and has significant potential to improve current clinical practice. Orthopedic surgery is at the forefront of these advancements and has incorporated the use of this technology in various ways. 3D-printed surgical applications include surgical planning, custom manufacturing of cutting guides and jigs, patient-matched implants, and 3D-printed biotechnology. This article introduces the current uses of 3D printing technology in orthopedic oncology, as well as the future potential for biological applications.