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Energy consumption is different for various technological processes used in manufacturing (cutting, plastic deformation, sintering, welding, etc.) and nowadays is becoming a more and more important issue to be considered when planning certain manufacturing processes. Related to this, when releasing a quotation for a given product request coming for the market, it is important to have a tool enabling to evaluate the estimated energy consumption that will be required. This paper proposes a method to predict the consumption of energy in the welding process depending on the same main parameters of the process. The method is based on causal identification and on the NN modelling technique. Method application is sampled in the case of a real database including information concerning pipes welding. The method proves to be fast and delivers results of reasonable precision.

In the actual economic environment, it is more and more important for people to be aware of their impact on environmental aspects, consumption of natural resources and usage of resources several times by recycling and reuse at the initial intended usage or changing the place of usage in opposition to the principle of owning, using and then throwing away. The increasing of waste quantities are the direct consequences of consumption that exceeds real needs and, at the same time, alters the state of the environment as the main provider of resources. In the coming years, approaches of the products’ design and built must be focused to allow for the continued reuse of resources. The introduction of the circular economy is a challenge to the linear economy approach to resource production and consumption. The circular economy is an economy that produces zero waste. The circular economy concept is not so new. European and world decision-making communities have been circulating for years the concept of circular economy, but only now the concept is beginning to gain visibility and applicability. In this paper, a study on the transition of companies from the linear economy to the circular economy is developed. Starting from the fact that current global sustainable development is given special attention, based on the transition from linear to a circular economy, the study provides an image of the degree of knowledge on the concept of circular economy of private companies, highlighting the major discrepancies between the linear and the circular economy, as well as the multidimensional impact of the implementation of the circular economy. It is necessary for more and more companies to experience how the circular economy can help them in the extraction and transport of raw materials, materials and product design, production, distribution, and consumption of goods, repairs, remanufacturing schemes and reuse, waste management and recycling.

In manufacturing system management, the decisions are currently made on the base of ‘what if’ analysis. Here, the suitability of the model structure based on which a model of the activity will be built is crucial and it refers to multiple conditionality imposed in practice. Starting from this, finding the most suitable model structure is critical and represents a notable challenge. The paper deals with the building of suitable structures for a manufacturing system model by data-driven causal modelling. For this purpose, the manufacturing system is described by nominal jobs that it could involve and is identified by an original algorithm for processing the dataset of previous instances. The proposed causal modelling is applied in two case studies, whereby the first case study uses a dataset of artificial instances and the second case study uses a dataset of industrial instances. The causal modelling results prove its good potential for implementation in the industrial environment, with a very wide range of possible applications, while the obtained performance has been found to be good.

In recent years, productivity and competitiveness have played a major role in manufacturing. To improve these aspects, many companies use computer-aided inspection (CAI) technologies. Inspection of parts, using 3D scanning technologies, is one the most modern and competitive ways to ensure the quality control of products. This paper presents a study on the assessment of the machined parts’ dimensional accuracy. An Atos Core device for 3D scanning of the part was used. The Atos device allows a non-contact inspection of the part’s entire surface, generating a very dense point cloud, which stores, with great accuracy, all the details of the inspected object. The machined part is compared with its numerical model, obtained by computer-aided design (CAD). The accuracy of the machining was proven to be very good, and the inspection’s result proves that the dimensions of the piece fall within the tolerance limits. A complete inspection of the piece, using 3D scanning, guarantees a fast and accurate quality control, thus reducing the production times.

The manufacturing system is a widely addressed subject. Many researchers have worked since a significant number of years in this field. Still, manufacturing system performance is capped by the insufficient level of accordance between manufacturing system and product. At the same time, global energy and global information networks have been created during recent years. Besides other applications, these networks can be turned in hard and soft infrastructure supporting the manufacturing system. For this reason, many specialists consider that the conceptual rebuilding of the manufacturing system is a notable challenge. This paper answers to this challenge by suggesting a holonic technique for manufacturing system management at basic, operational level. The proposed technique lays on the following hypothesis: i) next generation manufacturing system will be a modular cyber-physical system, ii) the typical management task is decision making, and iii) the procedure for decision making consists in options identification, followed by their comparative assessment. The approach is to manage the manufacturing system activity by holonic modelling of the system functioning, followed by the identification of a dedicated holarchy. The proposed technique aims to mitigate system performance in what concerns responsiveness capacity, investment efficiency and production costs. The technique is presented in architectural conceptual manner.

The optimal management of the manufacturing processes is achieved through a set of optimal decisions, which must be made for choosing the best way to follow, every time we find ourselves in a point from which several potential manufacturing paths start. A dedicated method, namely the Holistic Optimization Method has been already developed in this purpose, and validated in a number of studies based on artificial and real instances databases. In the current papers that approach the optimal management of the manufacturing processes, in order to estimate the consequences of a decision, are used known methods, such as: NN modeling, big data analysis, statistics, etc. In all these cases, the database size plays an essential role in terms of estimation quality. The present study aims to prove the feasibility of applying the Holistic Optimization Method when the decision-maker does not dispose of a consistent database. This can be a significant advantage relative to the other methods. The study is performed using an artificially generated instances database in the case of a turning process, and the results obtained are promising.

Advanced manufacturing technologies are based on-time-delivery principles. The customer requirements regarding quality, cost and delivery terms become more and more important , both for supplier and sub-supplier. Taking into account these considerations, all companies started the implementation of some new strategies and technologies based on continuous improvement principle. One of these is hard turning, as a cost efficient alternative to grinding. The technology is analyzed in the case of a bearing component, as a new process step versus the existing grinding process. Experimental tests were performed in order to describe the advantages of this technology. Cubic Boron Nitride grades (CBN) are used for cutting. The main influence of this technology in process flow is analyzed from different approaches. Experimental results were analyzed in terms of the ring surface quality, roughness and profile, and also on process time and production cost.

In industry, process planning has a major impact on manufacturing performance. This paper proposes a method aiming to increase the efficiency in process planning, more specific in selecting the set of plans with high potential of performance. The method works on the base of processes classification after their energy efficiency. It is applied for processes grouped in the same utility area, and involves the definition of energy classes. A case study, having as purpose to prove the feasibility and effectiveness of the proposed method is also presented. Energy labelling may further become an efficient tool enabling the manufacturing process optimization from energetic point of view.