Biocompatible titanium alloys are increasingly being used to make custom medical implants using additive manufacturing processes. This paper considered the welding reconditioning of a titanium-alloy customized additive manufactured hip implant with several manufacturing defects. The personalized implants are made starting from a Computer-Aided Design (CAD) model as a direct result from the medical imaging investigations of the areas of interest. Then the customized implant is fabricated using an additive manufacturing process (in this case Powder Bed Fusion—Direct Metal Laser Sintering—DMLS). The analysis of the chemical composition values as well as the values of the mechanical properties of the samples obtained via DMLS additive manufacturing process, revealed that such a manufacturing process can be successfully used to make customized surgical implants. The mechanical properties values of the DMLS samples are approximately equal to those specified by the manufacturer of the titanium powder used for sintering. On average, the tensile strength was found to be 24.75% higher, while yield strength 22.7% higher than the values provided in the standard for surgical implants applications. In case the additive manufacturing process produces products with defects one might want to try and recover the implant due to costs and time constraints. The Tungsten Inert Gas (TIG) welding reconditioning process with ERTi-5 Ti64 rod for welding titanium alloys with a content of 6% aluminum and 4% vanadium filler material was used to restore the geometric characteristics as well as the functional properties of a custom hip medical prosthesis. After welding depositing successive layers of materials, the surfaces of the prosthesis were machined to restore the functional properties according to the characteristics of the original 3D model. A 3D scan was used to compare the geometrical characteristics between the original part and reconditioned one. Deviations were less than 1 mm and were acceptable from the medical point of view.

The ever-increasing needs of the working population have led to the development of various branches of industry, an increase in the number of employees, and a rise in the number of work-related accidents. The welder occupation is one of the most sought after occupations in Europe, according to the EURopean Employment Services (EURES) statistics. Taking into account the work system in which welders conduct their activity (uncomfortable working positions, splashes, high temperatures, mechanical factors, gases and fumes, magnetic fields due to electric current), the paper presents the risk factors identified for the welder occupation, based on the occupational injury and illness risk assessments. Following the analysis of 25 risk assessments, carried out by the assessment team that must include qualified evaluators, process specialists, the workers’ representative, occupational health and safety responsible at various industrial economic agents, a total of 70 main risk factors of occupational accidents and diseases were identified for the welder occupation. Risk factors were classified according to four main work components: worker, workload, work environment, and means of production. To reflect the importance of the identified risk factor, the number of organizations that considered that the risk was likely to occur but also the possibility that the risk was above the acceptable limit, calculated using the National Institute for Research and Development for Labor Protection “Alexandru Darabont” (INCDPM) method, a method often used in Romania, was identified from the analyzed assessments. Finally, a prevention and protection plan was drawn up with regard to the risks identified for the welder occupation, the final aim of which was to respectively reduce the probability of occurrence with the severity of the risks identified.

This paper presents the application of the 8D method to a case study in the automotive industry, combined with the development of a pilot framework that integrates Industry 4.0 technologies—Artificial Intelligence (AI), Internet of Things (IoT), and advanced IT tools. Unlike traditional 8D implementations, the proposed solution introduces several innovations: (i) AI-based complaint analysis with automated keyword detection and classification and localization of batches in the warehouses achieving a 100% accuracy rate in categorizing root causes; (ii) IoT-enabled non-invasive monitoring of production parameters for tests and logs, reducing fault rate to 0 for current case study; (iii) digital cataloging and storage of corrective and preventive actions, improving retrieval speed of past solutions by 300 times (from 300 min to less than 1 min); and (iv) automated reporting tools, cutting the report preparation time from hours to minutes. Through these contributions, the system enhances both responsiveness and prevention, leading to an observed 100% reduction in fault recurrence associated with current case study and an estimated 75% decrease in customer complaints regarding defects occurring throughout the production sector where the pilot was implemented. In addition to the practical improvements, this work extends the literature on digitalized quality management by demonstrating how the 8D methodology can be effectively re-engineered with Industry 4.0 components. Comparative analysis against the enterprise’s conventional 8D system and against recent alternative approaches confirms that our framework provides measurable benefits in the speed, accuracy, and sustainability of problem-solving processes.

The implementation of a management system has increasingly become a necessity for organizations, one of the main purposes being to establish actions to achieve quality objectives. Currently there are many management systems but for a manufacturer of welded structures, in addition to the quality management system according to EN ISO 9001:2015 it is recommended to implement EN ISO 3834-2:2021 “Quality requirements for fusion welding of metallic materials. Part 2: Comprehensive quality requirements” The paper presents applied research on the assessment of the management system of a welded structure manufacturing organisation by conducting two internal audits at 7-month intervals as well as the analysis of the evolution of the management system after the implementation of corrective measures established after the first audit performed.

The paper presents the Failure Mode and Effects Analysis (FMEA) method applied to a process-based case study, together with an approach for implementing the AIAG & VDA harmonized FMEA standard by using modern digital tools. While classical FMEA is widely used in the industry, risk assessment based on the Risk Priority Number (RPN) often leads to the inconsistent ranking of failures and unclear prioritization of corrective actions. This paper explores the shift from the traditional Risk Priority Number (RPN) approach to the Action Priority (AP) concept introduced in the AIAG & VDA FMEA Handbook and explains why this change leads to clearer, more consistent risk-based decisions. Rather than focusing only on the methodological differences, the paper also outlines a practical framework for full implementation, showing how Industry 4.0 technologies can strengthen traceability, improve response time, and ensure greater consistency in PFMEA development. It also examines how Artificial Intelligence (AI) and Large Language Models (LLMs) can support engineers in everyday practice—for example, by helping identify potential failure modes, standardizing documentation, and guiding the definition of prevention and detection controls. In parallel, IoT-based monitoring and real-time data collection can provide valuable feedback to validate occurrence and detection ratings. Over time, this data-driven feedback loop can improve the accuracy and reliability of risk assessments. The proposed framework contributes to improved responsiveness in process optimization activities, reduces the probability of recurring failures, and supports continuous quality improvement in manufacturing organizations. The solution is discussed in relation to classical FMEA practices and recent trends in the digital transformation of quality management systems.

Failure Mode and Effects Analysis (FMEA) is a method commonly used in activities with the main objective of finding measures to improve manufacturing processes or products, by identifying actual and potential ways in which components fail or processes malfunction. The paper presents the practical application of the FMEA method for the process of obtaining an important component of the resistance system of a motor vehicle, namely a chassis. Some general information is presented on the importance of the FMEA method, the product analyzed, followed by the practical application of the method, starting from the determination of the probable causes of failure and their effects on: the organization, the customer and the end user. The processes carried out to obtain the chassis are identified and for a category of processes the Severity (S), Occurrence (O), Detection (D) and Risk Priority Number (RPN) indicators are calculated. Also, the preventive control methods and the control methods to be applied to reduce the occurrence of the defect are identified and indicated.