Wood is used as a raw material in various industries, including the production of furniture, which puts pressure on the exploitation of the forests and the continuous reduction of their surfaces, with undesirable effects on the environment. The paper provides a way of sustainably manufacturing furniture by replacing wood with composite materials based on natural fibers obtained from fast-growing renewable crops (hemp, willow, flax, etc.) and at the same time a method of assessing the forest areas which can be saved from cutting. The method’s algorithm is based on the estimation of forest area that ensures the annual consumption of wood for the production of furniture, both in the conventional production of furniture and in the unconventional one, where part of the products is made of composites. The agricultural areas required to be cultivated with technical plants to provide the natural fibers necessary for the wood replacement composite were also determined. The case study, based on the data of an upholstered furniture company, shows that replacing only part of the wood for the production of furniture can save about 3000 hectares of beech forests per year and the necessary plant fibers can be obtained from a surface area about 10 to 100 times smaller.

With the advent of manufacturing in Industry 4.0 and consumer demand, there has been a trend of mass customization of products. This customization requirement can only be achieved through the flexibility of manufacturing processes that are tailored to meet the quality standards of customers and the large volume of production in a short time. The increase of the production capacity is achieved through the processes of industrial automation of the manufacture which maintains the increased efficiency for the series production. This study was based on the Design for Six Sigma methodology (DMADV—Define, Measure, Analyze, Design and Verify) in order to determine the soldering process characteristics and how the soldering process can be automatized. When planning the implementation of a collaborative robot in a workstation in the production plant, the following must be taken into account: steps in operations that require the most time for the worker and/or that represent factors of physical and moral overload for him; the use of adequate precision fixing devices, delimitation of work areas, sensors as well as spaces for connecting the workstation to the electrical, hydraulic/pneumatic network and constant cycle time. The proposed solution can improve the productivity of the process by integrating advanced robotics and smart devices into the soldering line.

Artificial intelligence (AI) is becoming more and more integrated into manufacturing processes, revolutionizing conventional production, like CNC (Computer Numerical Control) machining. This study analyzes how large language models (LLMs), exemplified by ChatGPT, behave when tasked with G-code optimization for improving surface quality and productivity of automotive metal parts, with emphasis on systematically documenting failure modes and limitations that emerge when general-purpose AI encounters specialized manufacturing domains. Even if software programming remains essential for highly regulated sectors, free AI tools will be increasingly used due to advantages like cost-effectiveness, adaptability, and continuous innovation. The condition is that there is sufficient technical expertise available in-house. The experiment carried out involved milling three identical parts using a Haas VF-3 SS CNC machine. The G-code was generated by SolidCam and was optimized using ChatGPT considering user-specified criteria. The aim was to improve the quality of the part’s surface, as well as increase productivity. The measurements were performed using an ISR C-300 Portable Surface Roughness Tester and Axiom Too 3D measuring equipment. The experiment revealed that while AI-generated code achieved a 37% reduction in cycle time (from 2.39 to 1.45 min) and significantly improved surface roughness (Ra—arithmetic mean deviation of the evaluated profile—decreased from 0.68 µm to 0.11 µm—an 84% improvement), it critically eliminated the pocket-milling operation, resulting in a non-conforming part. The AI optimization also removed essential safety features including tool length compensation (G43/H codes) and return-to-safe-position commands (G28), which required manual intervention to prevent tool breakage and part damage. Critical analysis revealed that ChatGPT failures stemmed from three factors: (1) token-minimization bias in LLM training leading to removal of the longest code block (31% of total code), (2) lack of semantic understanding of machining geometry, and (3) absence of manufacturing safety constraints in the AI model. This study demonstrates that current free AI tools like ChatGPT can identify optimization opportunities but lack the contextual understanding and manufacturing safety protocols necessary for autonomous CNC programming in production environments, highlighting both the potential, but also the limitation, of free AI software for CNC programming.