Manufacturing Tools Research Articles

Identification, ranking and prioritization of Key Performance Indicators for evaluating greenness of manufactured products

Sustainability has become a paramount concern in the manufacturing industry, with a growing emphasis on eco-friendly products to preserve our planet’s life-support systems. However, evaluating the ”greenness” of a manufactured product is a complex task due to data availability, varying definitions of ”green” products, and diverse environmental impacts. This research addresses this challenge by identifying, classifying, and prioritizing Key Performance Indicators (KPIs) to assess green manufactured products, specifically within the Indian manufacturing context. A systematic approach was employed, incorporating Pareto analysis and the Full Consistency Method (FUCOM) to identify the most significant KPIs and assign them priority. Industry, academia, and customer perspectives were considered, resulting in distinct priorities. Industry experts prioritize economic aspects, while academia emphasizes health and safety. Customers focus on air quality and greenhouse gas emissions. This diversity of perspectives underscores the need for businesses to tailor their sustainability strategies to meet evolving customer demands while considering economic implications. The study identifies 30 vital KPIs crucial for evaluating green product manufacturing, encompassing dimensions like waste management, energy efficiency, workplace safety, and societal compliance. By considering these KPIs, businesses can make informed decisions to improve sustainability, reduce their environmental footprint, and align with consumer expectations. The findings provide a roadmap for businesses and policymakers to develop more effective strategies, targeting diverse stakeholder groups and promoting the production and use of green products. This research contributes to the growing body of knowledge in sustainable manufacturing and serves as a valuable tool for manufacturers in India and beyond, aiding them in navigating the complexities of manufacturing green products and meeting the rising demand from environmentally conscious consumers.

A review on molecular scissoring with CRISPR/Cas9 genome editing technology.

Genome editing is a technology to make specific changes in the DNA of a cell or an organism. It has significantly altered the landscape of life sciences, facilitating the establishment of exceedingly customized genetic modifications. Among various genome editing technologies, the CRISPR/Cas9 system, a specific endonuclease induces a double stranded DNA break and enabling modifications to the genome, has surfaced as a formidable and adaptable instrument. Its significance cannot be overstated, as it not only allows for the manipulation of genomes in model organisms but also holds great potential for revolutionary advances in medicine, particularly in treating genetic diseases. This review paper explores the remarkable journey of CRISPR/Cas9, its natural function, mechanisms, and transformative impact on genome editing and finally the use of artificial intelligence and other intelligent manufacturing tools used. The introduction provides the background on genome editing, emphasizing the emergence and significance of CRISPR/Cas9. Subsequent sections comprehensively elucidate its natural function, disease modeling, agriculture, and biotechnology, address therapeutic applications, and ongoing clinical trials while also discussing prospects and ethical implications. We summarized the key findings, indicating that CRISPR/Cas9 has empowered the creation of disease-specific animal models. This provides invaluable insights into pathogenic mechanisms and opens new avenues for drug discovery, reaffirming the transformative impact of CRISPR/Cas9 on genome editing. Finally we discussed the importance of continued research and collaboration for comprehensive utilization of the inherent capabilities of this molecular precision tool in shaping forthcoming advancements.

Strategi Pengembangan Ekonomi Kreatif Kriya Pada Pengrajin Kaligrafi Dalam Perspektif Ekonomi Islam (Studi Pada Toko Irjie Kaligrafi)

This research aims to find out the right strategy for developing the creative craft economy at the Irjie Calligraphy Shop from an Islamic economic perspective. This research approach method is qualitative descriptive research. The data collection methods used were interviews, observation and documentation. This research uses SOAR (Strengths, Opportunities, Aspirations, Results) and QSPM (Quantitative Strategic Planning Matrix) analysis in determining the matrix using the EFE, IFE, and EI matrices. After the analysis was carried out, the alternative strategy was concluded with the highest score of 2.020782. Renewing the design of calligraphy products that are more varied and innovative using quality raw materials using more modern manufacturing tools such as making embossed or 3D calligraphy which is carried out by human resources who have qualified skills. . The strategy for developing the creative craft economy at the Irjie Calligraphy shop from an Islamic economic perspective must be carried out by applying the characteristics of the Prophet Muhammad SAW, namely 1). Siddiq, 2). Be creative, brave and confident, 3). Tabligh, and 4). Istiqomah.

선삭 절삭력 예측을 위한 지식 기반 툴 개발

In this study, a knowledge-based tool is developed to enhance the manufacturing efficiency of mechanical components, particularly those requiring the removal of large components, such as the struts of airplane landing gears. Using this tool, knowledge is extracted and implemented during the turning process. The cutting forces and material-removal quantities are calculated based on the obtained knowledge. User-friendly graphical user interfaces are provided for user interactions. They can be used to input various parameters, such as the workpiece, cutting tool, and machining conditions, including the cutting material, rotational speed, and cut depth. The tool outputs essential parameters, such as the cutting forces, torque, power consumption, and material-removal rate, under different cutting conditions. More importantly, the tool enables users to conduct trade-off analyses, thus enabling the identification of near-optimal cutting conditions while accounting for factors such as tool life and production efficiency. This study contributes to the machining of high-strength materials by providing a useful tool for efficient industrial manufacturing.

ESTUDO EMPÍRICO SOBRE MODELAGEM E OTIMIZAÇÃO DO PROCESSO DEFRESAMENTO DE AÇO DUPLEX: UMA REVISÃO SISTEMÁTICA DE LITERATURA

One of the main production processes used in the metalworking industry is end milling. Thistechnique is used in the manufacture of automotive parts, molds and work tools in general. Duplex steel is a metallic alloy basically composed of 20% to 30% chromium and 5 to 10% nickel, with very low carbon content (less than 0.03%) and with additions of nitrogen, molybdenum, tungsten and copper. Response Surface Methodology (RSM) is astatistical technique used for modeling and analyzing problems in which the response variable is influenced by several factors, the objective of which is to optimize this response. The objective is to present a literature review focused on work that was carried out by modeling and optimizing the steel end milling process using RSM, seeking to identify the evolution of the topic over the years and gaps for improvement. To achieve this objective, the Systematic Literature Review was applied, which is a transparent, scientific and replicable method for carrying outthe literature review.

Analysis of Energy and Material Consumption for the Manufacturing of an Aeronautical Tooling: An Experimental Comparison between Pure Machining and Big Area Additive Manufacturing.

Additive manufacturing (AM) has been fully incorporated into both the academic and the industrial world. This technology has been shown to lower costs and environmental impacts. Moreover, AM-based technologies, such as wire arc additive manufacturing (WAAM), have been proven suitable for the manufacturing of large products with significant mechanical requirements. This study examines the manufacture of two aeronautical toolings: first, using conventional techniques, and second, using a big area additive manufacturing (BAAM) process, specifically WAAM technology, followed by second-stage hybrid machining. Both toolings can be considered interchangeable in terms of design and performance. Energy and material consumption were analysed and compared throughout both tooling procedures. The results show the important optimisation of both procedures in manufacturing WAAM tooling, encompassing the additive process and second-stage hybrid machining. Nevertheless, the time required for WAAM tooling manufacturing increased significantly compared to conventional manufacturing tooling. Moreover, based on metrology data from the AM process, a theoretical study was conducted to assess different design optimisations for WAAM tooling manufacturing and determine their influence on material and energy consumption. These theoretical results improve those already obtained regarding energy and raw material savings.

3.3 million years of stone tool complexity suggests that cumulative culture began during the Middle Pleistocene

Cumulative culture, the accumulation of modifications, innovations, and improvements over generations through social learning, is a key determinant of the behavioral diversity across Homo sapiens populations and their ability to adapt to varied ecological habitats. Generations of improvements, modifications, and lucky errors allow humans to use technologies and know-how well beyond what a single naive individual could invent independently within their lifetime. The human dependence on cumulative culture may have shaped the evolution of biological and behavioral traits in the hominin lineage, including brain size, body size, life history, sociality, subsistence, and ecological niche expansion. Yet, we do not know when, in the human career, our ancestors began to depend on cumulative culture. Here, we show that hominins likely relied on a derived form of cumulative culture by at least ~600 kya, a result in line with a growing body of existing evidence. We analyzed the complexity of stone tool manufacturing sequences over the last 3.3 My of the archaeological record. We then compare these to the achievable complexity without cumulative culture, which we estimate using nonhuman primate technologies and stone tool manufacturing experiments. We find that archaeological technologies become significantly more complex than expected in the absence of cumulative culture only after ~600 kya.

The use of point-impact techniques in stone processing (pecking) in the sites of forest Trans-Urals

For processing of stone and manufacture of tools, ancient man mastered a variety of methods and tech-niques — beating, splitting into flakes and plates, impact and spin retouching, grinding, sawing, drilling and some others. Of these, the least studied and insufficiently covered in the literature is the point-impact technique (pe-cking). Pecking could have been used both as an auxiliary technique and as the main one. When processing large tools (axes, adzes, pestles), which subsequently were polished, it was an auxiliary method. But when mak-ing a circular groove for tying and hollowing out a blind or through hole, it would have become the main one. In the forest Trans-Urals, the pecking technique was already known in the Mesolithic. A treasure hoard containing six stone axes has been found at the Mesolithic settlement of Ogurdino (Perm Krai). The axes were treated by beating and pecking techniques, followed by partial surface polishing. Also, two axe blanks with lugs (trunnions) from the site of Beregovaya IX in the Gorbunovsky peat bog (Sverdlovsk Region) belong to the Mesolithic. The edges of the tools and the protruding lugs were processed by pecking. A perforated disk-pommel in the shape of a kind of disguise was found in the Late Mesolithic layer of the 2nd Beregovaya site in the Gorbunovsky peat bog. A rounded hole 2.8–3.1 cm in diameter was made in the center of the disc by deep pecking on both sides. The majority of the items processed by pecking were found on mixed sites and date to a wide chronological range from the Neolithic to Bronze Age. Some of them represent accidental single finds. Stone sculptures, tops of maces, axes, adzes, plows, chisels, pestles, fishing sinkers, “ironings” were processed using point-impact re-touching. Tying lines on hammers made of massive pebbles were designed exclusively by pecking. During the Early Iron Age, the pecking technique only further expanded its application. Moreover, it was used not only for shaping products, but for making complex figured ornaments on sculptures and bas-reliefs.

Results of petrographic analysis of stone assemblage from the Kosa Mesolithic sites (Northern Kama region)

In this paper, the results of petrographic analysis of stone materials from the Mesolithic sites of Kosa 1–3 in the northern Kama River area (Perm Region) are discussed. The sites of Kosa 1 and 2 are located on the second terraces of the Lolog and Kosa Rivers, and Kosa 3 — on the first terrace of the Kosa River. Kosa 1 and 2 are considered chronologically similar and belong to the Late Mesolithic period. The site of Kosa 3 is younger, and it marks the transition from the Mesolithic to the Neolithic. For the analysis, aimed at determining the ancient raw material strategies, selected were five stone artifacts from Kosa 1, five stone artifacts from Kosa 2, and eight samples from Kosa 3. Polarised microscopy was used for the petrographic study of stones in thin sections. This method allows the precise diagnosis of minerals and rocks based on their optical characteristics. The results indi-cate that flint and jasper were applied as main raw materials at all analysed sites. The choice of material for the stone industry at the sites was limited. Sandstone and siltstone were used for production of stone macro tools. The difference in raw source materials was determined for the Kosa 1 and Kosa 2 sites on one hand, and Kosa 3 on another — the main raw sources at the Mesolithic sites were flint and jasper, while at the Kosa 3 transitional site coloured pebbly flint was used. The comparison of the results with petrographic data from other Mesolithic sites of this region demonstrates similarity in the main principles of the strategy of raw source choices such as using flint and jasper. Furthermore, a larger variety of source raw materials has been determined for the sites of the Middle Kama River basin. The comparison with the results from the Late Stone Age sites also revealed a greater variety in the materials used in comparison with the Kosinsk sites (platy flints, cherts, tuffs, gabbro, and others). All raw materials were of local origin such as alluvial deposits of the Lolog and Kosa Rivers. The results of the petrographic analysis suggest the situational choice of materials for the manufacture of tools by the local population, with the main factor being their easy accessibility.

Extendable machine tool wear monitoring process using image segmentation based deep learning model and automatic detection of depth of cut line

Automating the monitoring of machine tools poses a significant challenge, with previous studies relying on machine vision to address this issue, primarily focusing on measuring specific tool wear using custom algorithms. In this study, we introduce an automated tool wear monitoring process capable of assessing wear across various tools. Utilizing deep neural network models for image classification and segmentation, our proposed process effectively masks areas where tool wear occurs. The image classification model selects the best-performing backbone model based on the highest F1 score. To account for varying depths-of-cut lines among different tools in the masked area, we introduce an algorithm that utilizes the Hough transform to determine the horizontal angle with the cut line. By systematically measuring the maximum flank wear of diverse tools, including indexable, ball, and solid endmills, using this method, we can replace conventional manual processes. This approach can be extended and automated for various machine tools, offering substantial potential for enhancing manufacturing tool monitoring processes.

Cluster heterogeneity and efficiency of innovation network—Evidence from Shanghai and Taizhou in China

AbstractThere are debates about cluster heterogeneity, network structures, and innovations. The difference in the degree to which firms joined in the internal and external networks of heterogeneous clusters can affect innovation performance. There is still a lack of empirical evidence on how networks or spatial factors can differentially affect innovation efficiency due to cluster heterogeneity. China has endogenous clusters formed by small and medium‐sized firms and numerous clusters mainly based on government planning instrument, such as industrial zones and high‐tech parks. There are controversies over these planned cluster, such as insufficient firm connections and weak innovation effectiveness.Cluster innovation is a complex socio‐economic process that combines endogenous context, exogenous factors and interacts with multi‐spatial relationships. This perspective may explain the differences in which heterogeneous clusters improve efficiency. This paper draws on first‐hand data obtained from 188 questionnaires. The Lingang Equipment Manufacturing Cluster in Shanghai and the Taizhou Machine Tool Manufacturing Cluster in Zhejiang serve as examples of heterogeneous clusters. We combine the cluster's endogenous and exogenous characteristics, network size and strength of network ties, and local and non‐local innovation spaces to discuss the impact on innovation efficiency. Expecting to provide a reference for improving the innovation efficiency of heterogeneous clusters in developing economies. The results suggest that regardless of local or non‐local scales, exogenous clusters have a more pronounced effect of local network size and non‐local tie strength on innovation performance based on demand for proximity to customers and suppliers.

Simultaneous consideration of time and cost impacts of machine failures on cellular manufacturing systems

Cellular manufacturing systems (CMS) are essential for achieving efficient production processes, and their success relies on effective cell formation and layout design. This research paper presents a novel nonlinear mathematical programming model that employs the rectilinear distance notion to define the layout within a continuous space. The proposed model incorporates the stochastic nature of machine failures, considering the unreliability with the inclusion of a stochastic time between failures. With a bi-objective approach, the model aims to minimize inter and intra-cell movements of parts, as well as the associated costs related to exceptional elements (EEs), cell reconfigurations, and machine failures. To optimize the proposed model, several multi-objective meta-heuristic algorithms, including the multi-objective particle swarm optimization (MOPSO), multi-objective taboo search (MOTS), and non-dominated sorting genetic algorithm (NSGA-II), are introduced. The effectiveness of these algorithms is validated through numerical instances and a real case study. The results indicate that the MOPSO algorithm exhibits superior performance in optimizing various multi-objective criteria. The presented mathematical model and algorithms provide valuable tools for manufacturers to optimize their CMS, resulting in reduced costs, enhanced production efficiency, and increased competitiveness. By considering the simultaneous effects of time and cost associated with machine failures, this research offers practical insights and solutions for improving the performance of cellular manufacturing systems.

Deployment of Lean Manufacturing in Palm Oil Mill for Maximum Yield: A Case Study of Leading Palm Oil Producer in Nigeria

The deployment of lean manufacturing in the palm oil mill companyaimed at maximizing yield, was thoroughly investigated. The company has been experiencing a decline in palm oil production, adversely affecting productivity and customer satisfaction. The methodology employed for optimization encompassed a comprehensive system description, detailing the case study company's history, operational modalities, production machinery, and system challenges. To gather a holistic understanding of the company's current situation, data was meticulously collected through questionnaires, examination of company records, and other relevant sources. Microsoft excel software was then utilized to analyze the acquired data. The implementation of lean manufacturing tools, such as the fishbone diagram, identification of eight deadly wastesand takt-timeplayed a pivotal role in streamlining operations. In addition, a quantitative model of the supply chain was applied, utilizing critical path method (network analysis) to determine the optimal route. The results derived from the application of lean manufacturing tools and the supply chain quantitative model exhibited a remarkable improvement between (2012-2016) and (2018-2022). The average oil palm extraction increased substantially from 8.67% (2012-2016) to an impressive 30.52% (2018-2022). Furthermore, the management tools led to a significant reduction in machine downtimes, decreasing from an annual average of 5,498.6 hours to 2,472.4 hours. This improvement resulted in a considerable reduction in the annual total average cost of machine maintenance, plummeting from #1,899,578.6 to #261,853.8. The optimization efforts also extended to the supply chain, where selecting Path-2, exhibiting the least total supply time of 11 hours, significantly reduced the cost of supply from an average of #2,168,402 to #1,893,186. Overall, the net revenue of the company witnessed a remarkable surge of 71.99% post-implementation of these management tools. This comprehensive approach not only addressed production challenges but also had a substantial positive impact on the company’s financial performance. The strategic deployment of lean manufacturing emerged as a catalyst for sustainable growth and enhanced operational efficiency.

Special Tools in Spain

The Spanish TEMSA Metallurgical Group is a leading manufacturer of special tools for cold forming. The company operates cylindrical grinding machines from Studer in its production

Synthetic X-ray Image Generation for Non-Destructive Testing using Generative Adversarial Networks

Developing and optimizing a Machine Learning (ML) based Non-Destructive Testing (NDT) tool for automotive manufacturing can be challenging due to the need for a significant amount of annotated training data. Obtaining such an extent of real X-ray images of different defects can be challenging and expensive. Hence, generating synthetic images with adequate defects is a viable option to fill this gap. This contribution presents a novel approach that harnesses Generative Adversarial Networks (GANs) to simulate industrial X-ray images of specific parts of a car wheel (rim and spokes) with defects and their annotations. The presented method draws inspiration from techniques used in medical healthcare radiography as well as the NDT field for simulating synthetic X-ray images. We propose a novel deep neural network architecture for generating high-resolution X-ray images of size 1024 x 512 pixels with porous defects in specified locations. These images are automatically generated from edge masks of the desired structure. We conducted separate evaluations for both, the generated images and the included defects. To assess the generated image quality, we used image properties and structural similarity metrics. The generated images achieved an MSSIM index of over 0.90. We analyzed the local gray-scale profile of synthetic images to evaluate the quality of generated defects. A state-of-the-art defect detector (ISAR, developed by Fraunhofer EZRT) was used to estimate defect detection based on various IoU thresholds. In conclusion, the presented research demonstrates the feasibility of leveraging GANs to generate synthetic 2D X- ray image data, including defects for training ML-based NDT tools. This approach opens new possibilities for developing and optimizing robust NDT solutions by overcoming the challenges of obtaining real data with defects.

Route to Carbon-Neutral

Sandvik Coromant’s Gimo plant is aiming to be carbon-neutral by 2035 and the cutting tool manufacturer is utilising the benefits of CGTech software to reach that goal

Tooling Moves Pay Off

Volz Engineering has seen the benefits of a bespoke step drill from Ceratizit and has reaped the rewards of installation of a vending machine from the cutting tool manufacturer

Niche Machine Tool Manufacturer Finds Its Partner

UK micro machine provider Loxham Precision has partnered up with Open Mind to develop its machine solutions and market presence

Topology Optimization of Structural Drive-Train Component of an Electric Driven Vehicle for Additive Manufacturing

Additive Manufacturing (AM) is an emerging technology and an important alternative to conventional manufacturing methods as it enables the production of lighter parts that are potentially more durable. In this context, the design for additive manufacturing (DFAM) has been drawing a considerable amount of attention mainly in the aerospace, and automotive industries as well as in academia. On the other hand, the ability of additive manufacturing to manufacture complex topology is often the outcome of topology optimization, which makes topology optimization a good design tool for additive manufacturing. The main objective of the present work is to redesign a structural component of the drivetrain of the Shell Eco-Marathon vehicle, with the use of Altair Inspire™, an industrial generative design tool, by application of Topology Optimization for Additive Manufacturing aiming mass reduction and does not cover the print process.

Gamification Approach for Interactive Learning of Lean Manufacturing Activities

The gamification approach is popular method in education as it has been widely utilized in many fields. The project focuses on designing a game for interactive learning of lean manufacturing activities to create an alternative learning method that would gain interest and efficiency in learning. Integration of gamification approach and interactive learning method was used to illustrate a virtual lean manufacturing education and training by means of users’ interaction towards provided interactive learning materials and participation in playing the designed game. Input of the project which is learning materials was developed by analysing numerous articles regarding common lean manufacturing tools and practices. Interactive elements such as animation, sound effects, lecture voice, and videos were applied in each learning material to deliver an adequate understanding of each topic. Construction of the game platform is being developed using ActivePresenter software which includes simulation videos of the production line using FlexSim software to visualize a real production floor which provides a clearer picture of the production system for users. The game should include a basic assessment of lean manufacturing practices by considering its complexity and difficulty level for users with inadequate or does not have experience in the field. The outcome of the project is expected to successfully design a basic game platform for lean manufacturing education by means of interactive learning. Users will be able to learn lean manufacturing systems using the designed interactive learning materials included in the games and self-assess themselves as the game publishes the correct and best answer option in case they answer wrongly.