Additive manufacturing is a technology of making a three-dimesional solid object of any shape from a digital model. Today on the global market exist various additive manufacturing processes. All of these processes build parts by applying...

Last year’s developments are characterized by a dramatic drop in customer demand leading to stiff competition and more challenges that each enterprise needs to cope with in a globalized market. Production in low-mix/high-volume batches is replaced with low-volume/high-variety production, which demands excessive information flow throughout production facilities. To cope with the excessive information flow, this production paradigm requires the integration of new advanced technology within production that enables the transformation of production towards smart production, i.e., towards Industry 4.0. The procedure that helps the decision-makers to select the most appropriate I4.0 technology to integrate within the current assembly line considering the expected outcomes of KPIs are not significantly been the subject of the research in the literature. Therefore, this research proposes a conceptual procedure that focus on the current state of the individual assembly line and proposes the technology to implement. The proposed solution is aligned with the expected strategic goals of the company since procedure takes into consideration value from the end-user perspective, current production plans, scheduling, throughput, and other relevant manufacturing metrics. The validation of the method was conducted on a real assembly line. The results of the validation study emphasize the importance of the individual approach for each assembly line since the preferences of the user as well as his diversified needs and possibilities affect the optimal technology selection.

New trends in industry emphasize green and sustainable production on the one hand and personalized or individualized production on the other hand. Introducing new manufacturing technologies and materials to integrate the customer’s specific requirements into the product, while keeping the focus on environmental footprint, becomes a serious challenge. As a result, new production paradigms are developed to keep up with new trends. The most known Industry 4.0 paradigm is oriented towards new technologies and digitalization. Recently, Industry 5.0 appeared as a supplement to the existing Industry 4.0 paradigm, oriented to sustainability and the worker. A multidisciplinary approach is necessary to address these challenges. The Industry 5.0 paradigm’s main pillars—human centricity, resilience, and sustainability—are also pillars of the multidisciplinary approach used in this research. A human-centric approach includes workforce reskilling and acquiring new technologies to ensure that technology serves to enhance human work, while creating a supportive and inclusive work environment and prioritizing employee engagement and wellbeing. Resilience as a second pillar is related to the ability of manufacturing systems and processes to adapt to changing conditions to remain robust and flexible, and sustainability is an important and long-term requirement of this multidisciplinary approach. Based on the research part of the Erasmus+ ExCurS project, particularly research focused on application and training related to digital twins, an advanced concept of organizational sustainability is presented in this paper. The concept of organizational sustainability is realized through the usage of key digital twin technologies aligned with human-centric approaches. A new prototype of a digital twin that optimizes an assembly system based on a developed algorithm and humanoid decision-making is provided as a proof of concept. The human-centric digital twin for industrial application is presented through a case study of personalized products.

This paper examines the influence of three cutting parameters (cutting speed, cutting depth and feed rate) on surface roughness and power in the longitudinal turning process of aluminium alloy. For the analysis of data gathered by...

Due to the increasing heterogeneity of consumer needs and preferences, manufacturing companies are forced to expand their product range to maintain market share while avoiding cost increases. However, increasing product variety increases the complexity of assembly systems and complicates planning, design, and production management. The quantification of manufacturing complexity and its impact on key performance indicators remains a subject of debate. To examine the relationship between assembly complexity, assembly line balance, and productivity from an operator-oriented perspective, an empirical complexity indicator for mixed-model assembly workstations is proposed. This indicator is based on experimentally collected data and analysis of working time variability. The proposed indicator is evaluated through controlled experimental case studies conducted in a learning factory environment. The results indicate that the relationship between complexity and productivity is not linear. Instead, within the investigated experimental boundaries, the observed trend suggests a turning point beyond which further increases in complexity are associated with decreased productivity, while line balance continues to improve. This finding suggests that integrating the proposed complexity indicator into production planning and management may support decision-making related to assembly line balancing and complexity management in manual assembly systems.