One of the common problems of organizations with turn-key projects is the high scrap rate. There exist such traditional methods as Lean Six Sigma (LSS) and DMAIC tools that analyze causes and suggest solutions. New emerging intelligent technologies should influence these methods and tools as they affect many areas of our life. The purpose of this paper is to present the innovative Small Mixed Batches (SMB). The standard set of LSS tools is extended by intelligent technologies such as artificial neural networks (ANN) and machine learning. The proposed method uses the data-driven quality strategy to improve the turning process at the bakery machine manufacturer. The case study shows the step-by-step DMAIC procedure of critical to quality (CTQ) characteristics improvement. Findings from the data analysis lead to a change of measurement instrument, training of operators, and lathe machine set-up correction. However, the scrap rate did not decrease significantly. Therefore the advanced mathematical model based on ANN was built. This model predicts the CTQ characteristics from the inspection certificate of the input material. The prediction model is a part of a newly designed process control scheme using machine learning algorithms to reduce the variability even for input material with different properties from new suppliers. Further research will be focused on the validation of the proposed control scheme, and acquired experiences will be used to support business sustainability.

The sensorless control of the permanent magnet synchronous motor (PMSM) has attracted wide attention due to its high reliability, economic and safety benefits. A fast and high-precision rotor-position estimation is inevitable for the implementation of sensorless control. Sliding-mode observer (SMO) is a preferred solution for sensorless control by many industrial companies. This article addresses the comparison of different switching functions employed in the control structure of sensorless field-oriented control with SMO. The switching functions are classified and their influence on the performance of the PMSM is verified for different shaping coefficients (SC). In addition, a statistical evaluation of the switching functions is provided to find the optimal values of SC. An experimental and statistical evaluation validated the substitutability of signum and hyperbolic switching functions and optimal values of SC have been found.

The motivation for research on 3D printing of protective face shields was the urgent societal demand for healthcare in the fight against the spread of COVID19 pandemic. Research is based on a literature review that shows that objects produced by additive technologies do not always have consistent quality suitable for the given purpose of use. Besides, they have different effects on the environment and leave different footprints. The overall goal of the research was to find out the most suitable thermoplastic material for printing shield frames in terms of mechanical properties, geometric accuracy, weight, printing time, filament price, and environmental sustainability. Fused deposition modeling (FDM) technology was used for 3D printing, and three different filaments were investigated: polylactic acid (PLA), polyethylene terephthalate (PETG), and polyhydroxyalkanoate (PHA). The weighted sum method for multi-objective optimization was used. Finally, PHA material was chosen, mainly due to its environmental sustainability, as it has the most negligible impact on the environment.

In the modern high-performance drive applications, a high-precision and efficient control of the induction motor depends on the accuracy of parameter values. However, the motor parameters may change due to the winding temperature fluctuations, flux saturation and skin effect. Any discrepancy between the values of the motor’s actual parameters and the ones used for the design of the controllers may result in degradation of the drive performance. In this work, a new identification method of hardly measurable internal quantities of the induction motor, such as components of the magnetic flux vector and electromagnetic torque, is outlined. Commonly, the measurable quantities of the induction motor like stator currents, stator voltage frequency and mechanical angular speed are used to determine a feedback effect of the rotor flux vector on the vector of the stator currents of induction motor. Based on this feedback, it is also possible to identify the actual value of the rotor resistance, which may alter during the induction motor operation. This has a significant impact on the precision of the identified quantities as well as on the master control of the induction motor. Stability of the identification structure is guaranteed by the position of roots of characteristic equation of its linear transfer function. Simulation and experimental results are given to highlight the quality, effectivity, feasibility, and robustness of the proposed identification method, which is working reliably within the whole range of the motor angular speed.

In this paper, a comparison of the simple firing angle modulation method (FAM) and the more advanced torque sharing function (TSF)-based control of switched reluctance motor (SRM) is presented. The off-line procedure to tailor and optimize the parameters of chosen methods for off-the-shelf SRM is explained. Objective functions for optimization are motor efficiency, torque ripple, and integral square error. The off-line optimization uses a finite element method (FEM) model of the SRM. The model was verified by measurement on the SRM. Simulation results showed that FAM has comparable efficiency to TSF, but has a much higher value of torque ripple. The presented off-line procedure can be used for single or multi-objective optimization.

The variability of the material properties of steel from different suppliers causes problems in achieving the required surface quality after turning. Therefore, the manufacturer needs to estimate the resulting quality before starting production, especially if it is an expensive, small-batch production from stainless steel. Predictive models will make it possible to estimate the surface roughness from the mechanical properties of steel and thus support decision making about supplier selection or acceptance of a material supply. This research presents a step-by-step decision-making procedure, which enables the trained staff to make quick decisions based on commonly available information in the Mill Test Certificate (MTC). A new multivariate second-order polynomial model and feedforward backpropagation artificial neural network (ANN) models have been developed using input variables from the MTC: Tensile Strength, Yield Strength, Elongation, and Hardness. Models were used to enhance the methodological robustness in formulating the decision if the predicted surface roughness is outside the required range, even before accepting the delivery. Both models can accurately predict surface roughness, while the ANN model is more accurate than the polynomial model; however, the predictive model is sensitive to the accuracy of the input data, and the model’s prediction is valid only under precisely defined conditions.