Additive manufacturing has transformed the production process by enabling the construction of components in a layer-by-layer approach. This study integrates Artificial Neural Networks to explore the nuanced relationship between process parameters and mechanical performance in Fused Filament Fabrication. Using a fractional Taguchi design, seven key process parameters are systematically varied to provide a robust dataset for model training. The resulting model confirms its accuracy in predicting tensile strength. In particular, the mean squared error is 0.002, and the mean absolute error is 0.024. These results significantly advance the understanding of 3D manufactured parts, shedding light on the intricate dynamics between process nuances and mechanical outcomes. Furthermore, they underscore the transformative role of machine learning in precision-driven quality prediction and optimization in additive manufacturing.

In times of digitalisation, visual assistance systems in assembly are increasingly important. The design of these assembly systems needs to be highly complex to meet the requirements. Due to the increasing number of variants in production processes, as well as shorter innovation and product life cycles, assistance systems should improve quality and reduce complexity of assembly processes. However, many large kitchen manufacturers still assemble kitchen cabinets manually, due to the high variety of components, such as rails and fittings. This paper focuses on the analysis and evaluation of virtual assistance systems to improve quality and usability in individualised kitchen cabinet assembly processes at a large German manufacturer. A solution is identified and detailed.

Epoxy resins are widely used polymers in the automotive and aerospace fields. Different blends of novel biodegradable resins have been studied in the last years in order to provide sustainability while maintaining the same properties of epoxy resins. Bio-based thermoset resins made with acrylated epoxidized soybean oil are well-studied in different vat polymerization techniques. The present work compares a bio-based resin and a petroleum-based resin. A benchmark with different features was designed and manufactured by a VAT photopolymerization process using both materials; measured with an optical scanning device; thus, the dimensional deviations were analyzed through inspection software. Tensile and flexural specimens were manufactured with the same procedure and tested with a dynamometer machine. Therefore, the comparison between a biodegradable resin and a petroleum-based resin is discussed in terms of the quality and mechanical performances of manufactured parts, considering the use of identical printing conditions. Some parts are required to satisfy both the requirements at the same time, such as the gears. Therefore, dimensional accuracy and mechanical strength need to be controlled and evaluated in a unique final quantification. This work proposes a novelty performance index to quantify dimensional accuracy and mechanical strength simultaneously. By combining the two aspects it is possible to define the overall performance obtained with the used material, optimizing the manufacturing process by choosing the proper material for each purpose.

This work presents a synthetic geometrical performance index that combines all dimensional and geometrical deviations of a printed part to
express its quality. Smaller is the value of this index, greater is the part quality. The paper demonstrates the effectiveness of this index by printing
a set of parts through Fused Filament Fabrication in a biological polymer. These parts, characterized by representive hollows, were measured by
optical scanning technology. The proposed methodology shows the advantages of this index, thus future work can focus on further printing
techniques and varying process parameter values.

Industrial requirements to design high quality products in shorter and shorter times impose the use of numerical models
to estimate the geometrical deviations of these products, which are assemblies, starting from the geometrical deviations
of their components. Numerical models may support this estimation activity, thus reducing the time to market and the
design costs.
The free-body model is an interesting numerical method to translate the tolerance chain into a static problem solved by
algebraic or graphical procedures by using the free-body diagrams of force analysis. This work presents a free-body
model that is able to deal with dimensional and geometrical tolerances that involve translations of the features to which
the tolerances are applied. Moreover, it validates the free-body model for tolerance analysis of rigid parts by defining and
solving a case study through numerical and experimental activities. The present work uses a case study to demonstrate
the effectiveness of the free-body model by underlining that the experimental results obtained are very close to those
from the numerical model.

Statistical tolerance analysis based on Monte Carlo simulation can be applied to obtain a cost-optimized tolerance specification that satisfies both the cost and quality requirements associated with manufacturing. However, this process requires time-consuming computations. We found that an implementation that uses the graphics processing unit (GPU) for vector-chain-based statistical tolerance analysis scales better with increasing sample size than a similar implementation on the central processing unit (CPU). Furthermore, we identified a significant potential for reducing runtime by using array vectorization with NumPy, the proper selection of row- and column- major order, and the use of single precision floating-point numbers for the GPU implementation. In conclusion, we present open source statistical tolerance analysis and statistical tolerance synthesis approaches with Python that can be used to improve existing workflows to real time on regular desktop computers.

The importance of geometric deviations of components for the aesthetic and functional quality of products has been undisputed for decades. So, it is not surprising that not only have numerous researchers devoted themselves to this field, but also commercial software tools for the analysis and optimization of tolerance specifications (currently already fully integrated in 3D-CAD systems) have been available for around 30 years. However, it is even more surprising that the well-founded specification of tolerances and their analysis using a so-called statistical tolerance analysis are only established in a few companies. There is thus a contradiction between the proclaimed relevance of tolerances and their actual consideration in everyday business life. Thus, the question of the significance of geometric deviations and tolerances as well as the use of statistical tolerance analysis arises. Therefore, a survey among 102 German companies was carried out. The results are presented and discussed in this paper.

The specification of cost-optimal tolerances is ever since a major challenge for design engineers to ensure functionality as well as economic efficiency of mechanical assemblies. However, the underlying conflict "as tight as required, as wide as possible" is as old as the design, manufacturing of mechanical parts and their assembly itself – going back to times, when first plain tools were produced by hand using stones and wood. Since then, a long and challenging path has been taken by researchers and industrial experts to develop today’s effective methods and tools on tolerance engineering. In this paper a historical review on tolerance engineering of dimensional and geometrical tolerances in mechanical engineering is presented. Starting with the early beginnings during the age of individualized manufacture, four ages of tolerance engineering are analyzed and its major achievements are presented and discussed. Finally, we are looking ahead – focusing on current and upcoming trends in tolerance engineering.

The automotive industry is currently undergoing far-reaching structural changes. Automobile manufacturers are pursuing intensive scientific research and technological development in the field of alternative drive systems, such as electric powertrains. If electric car batteries are charged with regenerative generated electricity, their emission output is zero (from a well-to-wheel view). Furthermore, electric drives have very high efficiency. At cold temperatures, however, the battery power drops due to energy-intensive loads, such as the heating of the passenger compartment, and this consequently reduces the range dramatically. Therefore, the focus of this research work is external energy supply for the required heat capacity. The auxiliary energy may be generated by renewable energy technologies in order to further improve the CO₂ balance of electric vehicles. The paper deals with the design, application, and testing of a biofuel-operated heater to heat the passenger compartment of a battery-powered electric car (a Renault ZOE R240). The practical use of the heating system is analyzed in several test drives, performed during winter 2018. The results as well as the range extension of the electric car that can be achieved by substituting the on-board heating system by the fuel-operated heater are quantified herein.