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Automatic in-line process quality control plays a crucial role to enhance production efficiency in the injection molding industry. Industry 4.0 is leading the productivity and efficiency of companies to minimize scrap rates and strive for zero-defect production, especially in the injection molding industry. In this study, a fully automated closed-loop injection molding (IM) setup with a communication platform via OPC UA was built in compliance with Industry 4.0. The setup included fully automated inline measurements, in-line data analysis, and an AI control system to set the new machine parameters via the OPC UA communication protocol. The surface quality of the injection molded parts was rated using the ResNet-18 convolutional neural network, which was trained on data gathered by a heuristic approach. Further, eight different machine learning models for predicting the part quality (weight, surface quality, and dimensional properties) and for predicting sensor data were trained using data from a variety of production information sources, including in-mold sensors, injection molding machine (IMM) sensors, ambient sensors, and inline product quality measurements. These models are the backbone of the AI control system, which is a heuristic model predictive control (MPC) method. This method was applied to find new sets of machine parameters during production to control the specified part quality feature. The control system and predictive models were successfully tested for two groups of quality features: Geometry control and surface quality control. Control parameters were limited to injection speed and holding pressure. Moreover, the geometry control was repeated with mold temperature as an additional control parameter.

A brief note on quality loss functions used in quality engineering / Onur Köksoy -- The Taguchi loss and inverted loss functions / Matei Demetrescu, Adrian Stere Paris and Constantin Târcolea -- Physical model of the quality policy / Peter Eniko and Davorin Kramar -- From Taguchi's orthogonal arrays to full factorial designs and back / Zorica A. Veljković, Slobodan L.J. Radojević and Vesna K. Spasojević Brkić -- The quality loss function-based approach for discrete multiresponse process parameters optimization / Tatjana V. Sibalija.

This research investigates the control of thickness and weight in plastic extrusion vacuum-thermoforming products to identify optimal key parameters for cost reduction and energy savings. The initial step involves identifying crucial influencing factors. In this step, the Delphi technique was employed through a questionnaire administered to a panel of expert scholars to ensure minimal error and maximal reliability in determining key influencing factors. Consensus was sought to establish appropriateness and consistency. Subsequently, the Taguchi method was applied for quality design and planning of the extrusion vacuum-forming process. The experimental design parameters were selected using an L18 (21 × 37) orthogonal array, and the desired quality characteristics were determined. Comparative analysis of quantitative production data from two consecutive experiments was conducted, and based on F-values and contribution analysis, the combination of control factors maximizing the Signal-to-Noise (S/N) ratio was identified. The objective is to seek optimal parameters for improving the quality of the plastic polypropylene (PP cup lid) manufacturing process, reducing process variability, and identifying the most robust production conditions. Through multiple actual production prediction experiments, it was determined that five control factors, “polypropylene new material ratio,” “T-die lips adjustment thickness”, “mirror wheel temperature stability”, “molding vacuum pressure time”, and “forming mold area design”, contribute to the maximization of the S/N ratio, i.e., minimizing variability. Statistical validation confirms a significant improvement in product quality and weight control. Noteworthily, the quality control model and experimental design parameters established in this study are also applicable to other plastic products and bio-based materials, such as PET, HIPS, and biodegradable PLA lids with added calcium carbonate. The results of the experimental production demonstrate its ability to consistently control product weight within the range of 3.4 ± 0.1 g, approaching the specified tolerance limits. This capability results in approximately 2.6% cost savings in product weight, contributing significantly to achieving a company’s KPI goals for environmental conservation, energy efficiency, and operational cost reduction. Therefore, the findings of this study represent a substantial and tangible contribution.

Process control methods, in general, and quality, in particular, most often refer to the measures taken especially to the finished product, as well as to the technological process, in order to maintain its performance within certain statistical parameters. Genichi Taguchi is the first who developed a quality control approach, used first in Japan and later in industrialized economies, a procedure widespread in quality under the name Taguchi method. Within the Taguchi method, he imposed a key term average loss attached to a process /characteristic in case it deviates, compared to a target value/objective, considered optimal. The Taguchi methodology is especially oriented towards the design phase, different from the classic approach oriented towards the final control phase upon delivery, or towards the supervision of the processes. This new approach aims to design processes and products so that they are as insensitive as possible (robust) to the influence of external, disruptive factors of the processes. In our paper, the capability indicators of the processes and their connection with the Taguchi risk are also presented. A link is also made between the statistical measurement of uncertainty and the Taguchi risk with an example in a process from the mechanical industry.

This research employs the Taguchi method and analysis of variance (ANOVA) to investigate, analyze, and optimize the impact strength of tough polylactic acid (PLA) material produced through fused deposition modeling (FDM). This study explores the effect of key printing parameters—specifically, infill density, raster angle, layer height, and print speed—on Charpy impact strength. Utilizing a Taguchi L16 orthogonal array experimental design, the parameters are varied within defined ranges. The results, analyzed through signal-to-noise (S/N) ratios and ANOVA, reveal that infill density has the most substantial impact on Charpy impact strength, followed by print speed, layer height, and raster angle. ANOVA identifies infill density and print speed as the most influential factors, contributing 38.93% and 36.51%, respectively. A regression model was formulated and this model predicted the impact strength with high accuracy (R2 = 98.16%). The optimized parameter set obtained through the Taguchi method, namely, a 100% infill density, 45/−45° raster angle, 0.25 mm layer height, and 75 mm/s print speed, enhances the impact strength by 1.39% compared to the experimental design, resulting in an impact strength of 38.54 kJ/m2. Validation experiments confirmed the effectiveness of the optimized parameters.

In this study, a multi-objective optimization regarding the tribological characteristics of the hybrid composite with a base material of aluminum alloy A356 as a constituent, reinforced with a 10 wt.% of silicon carbide (SiC), size 39 µm, and 1, 3, and 5 wt.% graphite (Gr), size 35 µm, was performed using the Taguchi method, gray relational analysis (GRA), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision-making methods. Tribological tests were carried out on a “block on disc” type tribometer with lubrication. Load, sliding speed, and graphite mass concentration were analyzed as input parameters. As output parameters, wear rate and coefficient of friction were calculated. An analysis of variance (ANOVA) was conducted to identify all parameters that have a significant influence on the output multi-response. It was found that the normal load has the highest influence of 41.86%, followed by sliding speed at 32.48% and graphite addition at 18.47%, on the tribological characteristics of composites. Multi-objective optimization determined that the minimal wear rate and coefficient of friction are obtained when the load is 40 N, the sliding speed is 1 m/s, and the composite contains 3 wt.% Gr. The optimal combination of parameters achieved by GRA was also confirmed by the TOPSIS method, which indicates that both methods can be used with high reliability to optimize the tribological characteristics. The analysis of worn surfaces using scanning electron microscopy revealed adhesive and delamination wear as dominant mechanisms.

In this study, the performance of TiO 2 , ZnO, UiO-66-NH 2 and UiO-66-NH 2 /TiO 2 nanoparticles was investigated. They apply as photocatalysts for the destruction of organic reactive red dye 120 (RR120) under UV light. In order to determine the optimal conditions, effects of different catalysts and initial dye concentration, H 2 O 2 content and catalyst loading parameters were examined. Taguchi-designed experimental method was used to obtain optimal test conditions. The physical and chemical properties of synthetic photocatalysts were investigated by SEM, XRD, EDX, BET and DRS. SEM images show that the globular particles of titania are well placed on the surface of the metal-organic framework (MOF). XRD and EDX analyses also confirmed the presence of titania in the synthesised UiO-66-NH 2 /TiO 2 photocatalyst. Optimal values of H 2 O 2 , pH, the amount of catalyst, the dye concentration and the type of available photocatalyst to remove the RR120 dye, were obtained by 80 μl/l, 3 mg/l, 5 mg/l and 20 mg/l, UiO-66-NH 2 /TiO 2 catalyst, respectively. The required time for complete removal of RR120 dye under detection limit of 0.136 mg/l in optimal conditions was 10 min. The RR120 photocatalytic degradation followed the first-order kinetic equation according to the Langmuir–Hinshelwood model ( k app = 0.407 min −1 ). The result of optimisation showed the 20 wt% of the titania on MOF (UiO-66-NH 2 ) photocatalyst can be used in advanced oxidation processes, and it can be used as a suitable option for cleaning coloured effluent.

This study investigates the impact of arc length, current intensity, travel speed, and gas flow rate on surface hardness and line width during arc quenching process of S45C steel. The current intensity has the greatest influence on the surface hardness of S45C steel, followed by the travel speed, gas flow rate, and arc length. Using the Taguchi method, the optimal values of the parameters such as the arc length of 1.5 mm, the current intensity of 125 A, the travel speed of 250 mm/min and the gas flow rate of 12.5 l/min were calculated. The optimal surface hardness would be 379 HV, with a standard deviation of 46.4 HV. The current intensity is the most critical component in determining line width among these parameters. The arc length ranks second, followed by the TIG gun’s travel speed. The gas flow rate is the least significant factor. A longer arc length may result in a broader heat zone, which leads to a better line width. Increasing the arc length, current intensity, travel speed, and gas flow rate results in a similar pattern of surface hardness change caused by the low-heated and over-heated phenomena. The microhardness distribution showed a hardening zone of up to 2500 μm and a maximum hardness of 453 HV. The microstructure of arc quenching samples has three zones: hardening, heat-affected, and base metal. The hardening zone exhibits a martensite microstructure with a tiny needle shape and a residual austenite matrix.

Three-dimensional printing (3D printing) is one of the most common methods applied in rapid prototyping for manufacturing, medical, and education applications. In addition to the shape design and materials, the mechanical quality of 3D printing products also depends on printing parameters such as temperature, infill angle, infill density, and layer thickness. In this study, the mechanical characteristics of thermoplastic polyurethane (TPU) polymer are tested by examining these parameters. The Taguchi method is employed to design, analyze, and optimize the mechanical properties. To achieve maximum tensile strength, the parameters that should be applied are 210 °C, 45° infill angle, 100% infill density, and 0.1 mm thickness. Moreover, optimal parameters for maximum elastic modulus are 240 °C, infill angle 90°, 100% infill density, and 0.18 mm thickness. Finally, printing should be performed at 210 °C, with an infill angle of 45°, 15% infill density, and 0.1 mm thickness to achieve the highest elongation at break value. Scanning electron microscope (SEM) images of the sample reveal the stacking structure and the different breaking mechanisms between infill angles of 45 and 90°.

The Mahalanobis-Taguchi data handling and pattern recognition system is widely established-- built and extended from the original quality control precepts of Genichi Taguchi. But the MT system is not always well understood. This new book makes the system much more vivid and concrete with real-life applications in a wide variety of disciplines from industry to general commerce. The book offers a clear computational method to show the user how to actually apply the system to real manufacturing control problems. With the renowned international industry background of the three authors and their historic ties to Genichi Taguchi, this book will bring a unique insight into how to get the most benefits from the MT System. The book offers an overview of pattern recognition issues and the precepts of the MT system. explains the merits of the MT System and its computational methods. shows how to handle data with the MT System and extract useful information. provides a useful comparison of the advantages and disadvantages between traditional Artificial Intelligence systems and the MT system. provides case study examples of MT Systems applications.