Efficiency of complex production in changing environment

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Development of methods for computer-aided manufacturing of abrasive grains and a device for its implementation

У статті проаналізовано методи і пристрої для автоматизованого виробництва абразивних гранул певної геометричної форми та запропоновано шляхи удосконалення конструкції пристроїв для виготовлення гранул в формі пірамід, що мають в основі не опуклий шестикутник.В статье проанализированы методы и устройства для автоматизированного производства абразивных гранул определенной геометрической формы и предложены пути усовершенствования конструкции устройств для изготовления гранул в форме пирамид, в основании которых лежит невыпуклый шестиугольникThe existing methods of manufacturing of abrasive granules with various geometric shapes and the new method of computer-aided manufacturing of abrasive granules in rubber and ceramic bonding and device for its implementation are examined in the article. A device for manufacturing of abrasive granules with desired geometric shape is presented. This device allows solving the problem of serial automated tools production for treatment of parts with complex geometric shapes in an environment of free abrasives. Using all stages of CAD-system KOMPAS-3D enabled to create associative drawings quickly, which significantly reduced design time and provide maximum visualization of the designed device. A parametric model of the device for manufacturing of abrasive granules with desired geometric shape was initially designed. This model allows automatically change the design of the device at changing of abrasive granules parameters (changing the type of binder, forms of beads and so on.), or their number. An economic efficiency of the designed device in real production conditions was confirmed

A Peaking and Tailing Approach to Education and Curriculum Renewal for Sustainable Development

Contextual factors for sustainable development such as population growth, energy, and resource availability and consumption levels, food production yield, and growth in pollution, provide numerous complex and rapidly changing education and training requirements for a variety of professions including engineering. Furthermore, these requirements may not be clearly understood or expressed by designers, governments, professional bodies or the industry. Within this context, this paper focuses on one priority area for greening the economy through sustainable development—improving energy efficiency—and discusses the complexity of capacity building needs for professionals. The paper begins by acknowledging the historical evolution of sustainability considerations, and the complexity embedded in built environment solutions. The authors propose a dual-track approach to building capacity building, with a short-term focus on improvement (i.e., making peaking challenges a priority for postgraduate education), and a long-term focus on transformational innovation (i.e., making tailing challenges a priority for undergraduate education). A case study is provided, of Australian experiences over the last decade with regard to the topic area of energy efficiency. The authors conclude with reflections on implications for the approach

Robustness- and complexity-oriented characterization of supply networks’ structures

In the past period the efficiency aspects of production were emphasized, sometimes even overemphasized. As a result, the vulnerability of production structures was put in the background, and consequently, by now, it is usually beyond its acceptable degree. The frequently changing and uncertain environment which manufacturing companies are facing in our days requires robustness on every level of the production hierarchy from the process / machine level, through the system and enterprise levels, up to the level of supply chains and networks. As to the supply networks, the question may arise, what level of complexity is required for achieving a certain degree of robustness while, naturally, keeping the efficiency aspects in mind as well. In order to be able to give appropriate answers to this question, it is indispensable to quantify the robustness and complexity of supply chains and networks. Structural (static) and operational (dynamic) robustness and complexity are distinguished in the paper, which focuses on the structural aspects. A complex network approach is used for this purpose, namely the structural - both robustness and complexity - nature of the networks is described by applying graph theoretical concepts. Appropriate, quantitative graph measures are introduced and their applicability for characterizing the robustness and complexity of supply chains and networks is investigated by using structures of three types, namely real and artificially generated ones, and structures taken from the literature. Finally, it is illustrated how a decision support system based on the approach described in the paper can contribute to the design and redesign of supply chains and networks striving for an appropriate balance between the robustness, complexity and efficiency aspects of the problem

Dynamic Scheduling Method for Job-Shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization

With the rapid development of Industrial 4.0, the modern manufacturing system has been experiencing profoundly digital transformation. The development of new technologies helps to improve the efficiency of production and the quality of products. However, for the increasingly complex production systems, operational decision making encounters more challenges in terms of having sustainable manufacturing to satisfy customers and markets’ rapidly changing demands. Nowadays, rule-based heuristic approaches are widely used for scheduling management in production systems, which, however, significantly depends on the expert domain knowledge. In this way, the efficiency of decision making could not be guaranteed nor meet the dynamic scheduling requirement in the job-shop manufacturing environment. In this study, we propose using deep reinforcement learning (DRL) methods to tackle the dynamic scheduling problem in the job-shop manufacturing system with unexpected machine failure. The proximal policy optimization (PPO) algorithm was used in the DRL framework to accelerate the learning process and improve performance. The proposed method was testified within a real-world dynamic production environment, and it performs better compared with the state-of-the-art methods

Dynamic Scheduling Method for Job-shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization

With the rapid development of Industry 4.0, modern manufacturing systems have been experiencing profound digital transformation. Development of new technologies can help to improve the efficiency of production and the quality of products. With the increasingly complex production systems, operational decision-making has encountered challenges in the sustainable manufacturing process to satisfy customers and markets' ever-changing demands. Nowadays, the rule-based heuristics approaches are widely used for scheduling management in production systems, which however significantly depends on the expert domain knowledge. In this way, the efficiency of decision-making cannot be guaranteed nor meet the dynamic scheduling requirements in the job-shop manufacturing environment. In this study, we propose using deep reinforcement learning (DRL) methods to tackle the dynamic scheduling problem in the job-shop manufacturing system. The proximal policy optimization (PPO) algorithm has been used in the DRL framework to accelerate the learning process and improve performance. The proposed method has been testified within a real-world dynamic production environment, and it performs better compared with the state-of-the-art method

Adaptive development of competitive advantages of an industrial enterprise on the basis of analysis and ensuring the competitiveness of its products

Insufficient theoretical study of the issues of adapting the resource potential of an enterprise in the context of imperatives of innovative development reinforces the theoretical and practical significance of conducting a study aimed at studying the processes of the essence of adapting the resource potential of an industrial enterprise within a cluster, developing tools for evaluating and managing this process, allowing to model alternative uses of key components their potential within the cluster. Successful implementation of this task implies the development of an organizational and managerial mechanism for managing the potential of industrial enterprises - potential cluster members, including the formation of a capitalization strategy for their resource potential, an important unit that is information-analytical tools integrated into the cluster management system as a whole. This determined the relevance of the allocation of this spectrum of problems in an independent direction of scientific research, had a direct impact on the choice of topics, setting goals and objectives

Adaptive development of competitive advantages of an industrial enterprise on the basis of analysis and ensuring the competitiveness of its products

Insufficient theoretical study of the issues of adapting the resource potential of an enterprise in the context of imperatives of innovative development reinforces the theoretical and practical significance of conducting a study aimed at studying the processes of the essence of adapting the resource potential of an industrial enterprise within a cluster, developing tools for evaluating and managing this process, allowing to model alternative uses of key components their potential within the cluster. Successful implementation of this task implies the development of an organizational and managerial mechanism for managing the potential of industrial enterprises - potential cluster members, including the formation of a capitalization strategy for their resource potential, an important unit that is information-analytical tools integrated into the cluster management system as a whole. This determined the relevance of the allocation of this spectrum of problems in an independent direction of scientific research, had a direct impact on the choice of topics, setting goals and objectives

Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future