Purpose – The purpose of this paper is to provide a personalised view by the Editors of the Rapid Prototyping Journal.Design/methodology/approach – It collects their years of experience in a series of observations and experiences that can be considered as a snapshot of where this technology is today.Findings – Development of these technologies has progressed according to application, materials and how the designers have applied their creativity to such a unique manufacturing tool.Originality/value – The paper predicts how the future of additive manufacturing will look from the perspective of three key elements: applications, materials and design.
Manufacturing processes for therapeutic monoclonal antibodies (mAbs) have evolved tremendously since the first licensed mAb product (OKT3) in 1986. The rapid growth in product demand for mAbs triggered parallel efforts to increase production capacity through construction of large bulk manufacturing plants as well as improvements in cell culture processes to raise product titers. This combination has led to an excess of manufacturing capacity, and together with improvements in conventional purification technologies, promises nearly unlimited production capacity in the foreseeable future. The increase in titers has also led to a marked reduction in production costs, which could then become a relatively small fraction of sales price for future products which are sold at prices at or near current levels. The reduction of capacity and cost pressures for current state-of-the-art bulk production processes may shift the focus of process development efforts and have important implications for both plant design and product development strategies for both biopharmaceutical and contract manufacturing companies.
Abstract The promotion of the green transformation of the manufacturing industry has become the main means to achieve the dual goals of environmental protection and economic growth. Environmental regulation, as an environment governance tool, exerts an important impact on the green transformation of the manufacturing industry. Therefore, this paper focused on the influencing factors of green transformation of the manufacturing industry under environmental regulation. Based on survey data of 500 Chinese manufacturing enterprises (2017), the influencing factors of green transformation were studied by developing a targeted structural equation model. The main conclusions showed that (1) human capital, financing ability, technology innovation, and government behavior all exerted significant positive impact on green transformation performance in the manufacturing industry. (2) Environmental regulation, as a moderating variable, positively affected the green transformation in the manufacturing industry by acting on technology innovation and governmental behavior; however, it decreased the positive impact of financing ability on green transformation. (3) Environmental regulation was a reversal mechanism, which affected green transformation by influencing financing capacity, technology innovation, and governmental behavior of manufacturing enterprises. The study suggested that the government should consider the positive impact of these influencing factors, and design appropriate environmental regulation policies to promote the green transformation in the manufacturing industry to achieve economic green growth.
Mohammadhossein Ghahramani, Yan Qiao, Mengchu Zhou
et al.
Smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things ( IIOT ) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing machine learning and artificial intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on evolutionary computing and neural network algorithms toward making semiconductor manufacturing smart. We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a genetic algorithm and neural network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.
The growing interest in soft robots comes from the new possibilities offered by these systems to cope with problems that cannot be addressed by robots built from rigid bodies. Many innovative solutions have been developed in recent years to design soft components and systems. They all demonstrate how soft robotics development is closely dependent on advanced manufacturing processes. This review aims at giving an insight on the current state of the art in soft robotics manufacturing. It first puts in light the elementary components that can be used to develop soft actuators, whether they use fluids, shape memory alloys, electro-active polymers or stimuli-responsive materials. Other types of elementary components, such as soft smart structures or soft-rigid hybrid systems, are then presented. The second part of this review deals with the manufacturing methods used to build complete soft structures. It includes molding, with possibly reinforcements and inclusions, additive manufacturing, thin-film manufacturing, shape deposition manufacturing, and bonding. The paper conclusions sums up the pros and cons of the presented techniques, and open to developing topics such as design methods for soft robotics and sensing technologies.
Abstract New emerging manufacturing paradigms such as cloud manufacturing, IoT enabled manufacturing and service-oriented manufacturing, have brought many advantages to the manufacturing industry and metamorphosis the industrial IT infrastructure. However, all existing paradigms still suffer from the main problem related to centralized industrial network and third part trust operation. In a nutshell, centralized networking has had issues with flexibility, efficiency, availability, and security. Therefore, the main aim of this paper is to present a distributed peer to peer network architecture that improves the security and scalability of the CMfg. The proposed architecture was developed based on blockchain technology, this facilitated the development of a distributed peer to peer network with high security, scalability and a well-structured cloud system. The proposed architecture which was named as the “BCmfg” is made up of five layers namely; resource layer, perception layer, manufacturing layer, infrastructure layer and application layer. In this paper, the concept of its architecture, secure data sharing, and typical characteristic are discussed and investigated as well as the key technologies required for the implementation of this proposed architecture is explained based on demonstrative case study. The proposed architecture is explained based on a case study which contains five service providers and 15 end users with considering 32 OnCloud services. For evaluation purpose, the qualitative and quantitative methods are utilized and the results show that the proposed methodology can bring more advantages to CMfg than the security and scalability.
Zeqing Jin, Zhizhou Zhang, Kahraman G. Demir
et al.
Summary Increasing demand for the fabrication of components with complex designs has spurred a revolution in manufacturing methods. Additive manufacturing stands out as a promising technology when it comes to prototyping multi-functional and multi-material designs. However, challenges still exist in the additive manufacturing process, such as mismatched material properties, lack of build consistency, and pervasive imperfections in the printed part. These inherent challenges can be avoided by implementing algorithms to detect imperfections and modulate printing parameters in real time. In this paper, several algorithms, with a focus on machine learning methods, are reviewed and explored to systematically tackle the three main stages of the additive manufacturing process: geometrical design, process parameter configuration, and in situ anomaly detection. Current challenges and future opportunities for algorithmically driven additive manufacturing processes, as well as potential applications to other manufacturing methods, are also discussed.
Manufacturing Engineering is concerned with designing, building, planning, operating, and managing economical production systems for discrete manufacturing. Manufacturing engineers need to have a thorough knowledge of materials and manufacturing processes. They should also be able to design, operate and manage integrated systems that include people, materials, machine tools, material handling equipment, robots, quality measuring equipment, controls and computers.
Abstract The goal of any manufacturer is customer’s satisfaction this can be achieved by delivering the quality product, right on time at reasonable cost. Any organization whether manufacturing or service will survive and sustain the competency if it is flexible enough to continuously and systematically respond to the customers need and accordingly adds value to the product. Equipment, Material, and labour cost will increase with the inflation rate which are the dominant parameters that affect the price of the product. A simple mathematics, underutilization of equipment, material and labour is direct loss incurred. So, without any doubt the first focus of attention must be towards maximum utilization of this dominant parameters followed by reducing wastages in manufacturing activities. Lean manufacturing are now vital tools in all manufacturing sectors like automotive, electronics, plastic, textile, food, dairy, foundry, stampings, maintenance. The benefit observed after implementation of individual or combined lean manufacturing technique was reduction in cycle time, elimination of non-valued activities, clean, tidy, and hygienic workplace. Besides this there will be a smooth production flow, increase in productivity, reduction in production cost, involvement of employees, documentation of orders, reduction of inventory, breakdown with better intra and inter connectivity to take decisions fast and quick response. The present review focuses on different manufacturing sectors to see the effect of lean manufacturing techniques implemented for improving the process and reducing the wastages.