Machine Running Time Research Articles

Optimizing Compressed Air Operations for Electrical Energy Savings: A Case Study in Pharmaceutical Packaging Manufacturing

This study in pharmaceutical packaging manufacturing focuses on improving compressed air efficiency through targeted strategies at both the source and user levels by establishing a baseline to analyze energy consumption patterns. Key measures, including minimizing air leaks, adjusting pressure, and optimizing compressor performance, aim to achieve a 20-50% increase in efficiency, thereby supporting environmental sustainability. The User Point and Source Point approaches are expected to lower Specific Power Consumption (SPC), with data collected from December 2020 to May 2022 providing insights into potential energy savings. Establishing this baseline, based on machine runtime and productivity, offers a solid foundation for evaluation. Results show a 23% reduction in compressor electricity usage and a 7-8% decrease in compressed air consumption. A structured improvement process and strong collaboration between engineering and management are essential for enhancing productivity and achieving sustainable energy efficiency in the industrial sector.

Comprehensive Review of Machine Learning Techniques for Condition-Based Maintenance

While most industrial maintenance strategies are centered on optimizing machine runtime and cost reduction, the condition-based maintenance (CBM) strategy distinguishes itself from others in its use of real-time operational data from machines to help engineers make informed decisions. The introduction of machine learning (ML) into a CBM strategy can increase its effectiveness, enabling more accurate predictions and making the decision-making process more efficient. In this review paper, we seek to provide a comprehensive overview of the role ML plays in modern CBM systems, beginning by outlining the core concepts and historical development of CBM and briefly introducing various ML techniques being employed in industry today. We then review numerous real-world cases where ML-based CBM systems have been implemented and discuss some of the technological, human, and ethical challenges faced by organizations seeking to integrate sophisticated ML models into existing CBM systems. We end by highlighting some of the current limitations of ML-based CBM systems, paving the way for a discussion on emerging trends and future research directions in this area.

A Multiple Attention Convolutional Neural Networks for Diesel Engine Fault Diagnosis.

Fault diagnosis can improve the safety and reliability of diesel engines. An end-to-end method based on a multi-attention convolutional neural network (MACNN) is proposed for accurate and efficient diesel engine fault diagnosis. By optimizing the arrangement and kernel size of the channel and spatial attention modules, the feature extraction capability is improved, and an improved convolutional block attention module (ICBAM) is obtained. Vibration signal features are acquired using a feature extraction model alternating between the convolutional neural network (CNN) and ICBAM. The feature map is recombined to reconstruct the sequence order information. Next, the self-attention mechanism (SAM) is applied to learn the recombined sequence features directly. A Swish activation function is introduced to solve "Dead ReLU" and improve the accuracy. A dynamic learning rate curve is designed to improve the convergence ability of the model. The diesel engine fault simulation experiment is carried out to simulate three kinds of fault types (abnormal valve clearance, abnormal rail pressure, and insufficient fuel supply), and each kind of fault varies in different degrees. The comparison results show that the accuracy of MACNN on the eight-class fault dataset at different speeds is more than 97%. The testing time of the MACNN is much less than the machine running time (for one work cycle). Therefore, the proposed end-to-end fault diagnosis method has a good application prospect.

Fuzzy clustering for feature extraction in wavelet-based fault gear identification of electrical machines

Fault detection and diagnosis in electrical machines are periodical for preventing operational interruptions and unexpected shutdowns. However, a Wavelet Feature-dependent Clustering Technique (WFCT) is introduced to address the cyclic fault detection between successive operation intervals. This technique identifies override features from the time-frequency operational wavelets throughout the machine running time. This grouping binds time and operational frequency for identifying override exceeding shutdown/ failure instances. Based on their revamping time, the identified instances are further grouped to prevent overrides in successive operational hours. The fuzzy clustering prevents variation features based on conventional to high-fuzzified extractions.

Manufacturing cost reduction techniques for automobile crankcase mould inserts

The manufacturing of automobile moulds is itself a challenging job and very tedious task requiring high accuracy of component profiles. The required complicated and complex profiles are generated when the mould inserts are relinquished from several numbers of workstations, which run for a longer process time. As the accuracy needed for the component area is very high and it (insert) has to pass through several bottleneck workstations, the production time and increased buffer time are observed. This chapter focuses on providing economical methods for manufacturing hot die steel inserts. These techniques are implemented on a pressure die-casting insert component. The present solution is derived for manufacturing time reduction and minimisation of the die-casting inserts (moving and fixed die) at every stage. Implementing combined electrodes, assembly machining and cluster plate machining/sparking techniques are critical development advancements in the research. In the combined electrode concept, one electrode is designed in the same area to cover multiple areas that were initially covered by separate individual electrodes. Furthermore, this concept is outstretched by the cluster plate concept. Those electrodes that cannot be covered by a single electrode are machined together to act as a single electrode and later separated while individual sparking. Combined electrode design, assembly machining and cluster machining/sparking of the electrodes reduce the total machining time of the die and the machining centre. Reducing the machine run time will reduce the production cost and increase profit. The results achieved show that the combined electrode design setup shows 75% savings in the combined sparking setup, whereas the cluster plate machining setup shows 35% savings in the tool list generation (from software used) of the net savings. The proposed solution opens up new avenues for similar automobile components by setting benchmarks to decrease the rope length associated with manufacturing and increase profit.

Improving Energy Performance in Flexographic Printing Process through Lean and AI Techniques: A Case Study

Flexographic printing is a highly sought-after technique within the realm of packaging and labeling due to its versatility, cost-effectiveness, high speed, high-quality images, and environmentally friendly nature. A major challenge in flexographic printing is the need to optimize energy usage, which requires diligent attention to resolve. This research combines lean principles and machine learning to improve energy efficiency in selected flexographic printing machines; i.e., Miraflex and F&K. By implementing the 5Why root cause analysis and Kaizen, the study found that the idle time was reduced by 30% for the Miraflex machine and the F&K machine, resulting in energy savings of 34.198% and 38.635% per meter, respectively. Additionally, a multi-linear regression model was developed using machine learning and a range of input parameters, such as machine speed, production meter, substrate density, machine idle time, machine working time, and total machine run time, to predict energy consumption and optimize job scheduling. The results of the research exhibit that the model was efficient and accurate, leading to a reduction in energy consumption and costs while maintaining or even improving the quality of the printed output. This approach can also add to reducing the carbon footprint of the manufacturing process and help companies meet sustainability goals.

Functional quality assessment of whole-body vibration training devices based on instantaneous amplitude and frequency of photogrammetric vibration measurements.

The practical use of whole-body vibration training (WBVT) and such research may be negatively influenced by generated vibrations with amplitudes, frequencies, and/or patterns that deviate from preset adjustments on WBVT devices. This study examined whether prolonged regular use can generate respective deviations. Four WBVT devices, used for 19 months in a research project on the effects of WBVT, were analyzed using photogrammetry before start of the research project and after 19 months. Divergences between preset and measured amplitudes and frequencies were calculated for all measurements. To quantify how well the output of devices correlates with the target setting, the vibration characteristics were calculated. In particular, exact long-term measurements related to the vibration amplitude is conducted and analyzed for the first time, which has been found as an important measure of the device functional quality. One device had a significantly (p<0.01) larger machine run time than the other three. This one showed the most pronounced signs of functional impairments concerning instantaneous amplitudes, frequencies and the mode of vibration after prolonged use. These results based on photometric measurements underline again that prolonged use can result in divergences between preset and actual applied amplitudes, frequencies, mode of vibration and other accuracy measurement metrics.

Evaluation methodology for preventive maintenance in multi-state manufacturing systems considering different costs

ABSTRACT In a multi-state manufacturing system, preventive maintenance is performed on other machines once the performance of a machine falls below a threshold state. Many investigations have been performed on the maintenance of manufacturing systems. However, studies on preventive maintenance for manufacturing systems that account for the different costs involved are limited. This paper focuses on the analysis of multi-state machines in a manufacturing system considering different costs. Subsequently, a cost-based preventive maintenance prioritisation method for multi-state machines is proposed. Based on the buffer capacity, three maintenance policies on the machines are discussed. Different machine failures lead to different maintenance priorities. The real-time buffer capacity greatly influences the selection of machines for preventive maintenance. The set of the optimal machines for preventive maintenance is determined based on the constraints of machine running time and different preventive maintenance costs. Finally, a numerical example is used to demonstrate the effectiveness of the proposed method.

Getting more out of FLAG-Tag co-immunoprecipitation mass spectrometry experiments using FAIMS

Co-immunoprecipitation of proteins coupled to mass spectrometry is critical for the understanding of protein interaction networks. In instances where a suitable antibody is not available, it is common to graft synthetic tags onto a target protein sequence thereby allowing the use of commercially available antibodies for affinity purification. A common approach is through FLAG-Tag co-immunoprecipitation. To allow the selective elution of protein complexes, competitive displacement using a large molar excess of the tag peptides is often carried out. Yet, this creates downstream challenges for the mass spectrometry analysis due to the presence of large quantities of these peptides. Here, we demonstrate that Field Asymmetric Ion Mobility Spectrometry (FAIMS), a gas phase ion separation device prior to mass spectrometry analysis can be applied to FLAG-Tag co-immunoprecipitation experiments to increase the depth of protein coverage. By excluding these abundant tag peptides, we were able to observe deeper coverage of interacting proteins and as a result, deeper biological insights, without the need for additional sample handling or altering sample preparation protocols. SignificanceWe have shown that application of FAIMS separation in the gas phase can increase the proteome coverage of Flag-Tagged co-immunoprecipitation mass spectrometry experiments versus one without FAIMS. We were able to observe deeper coverage of interacting proteins and as a result, deeper biological insights, without additional sample handling, fractionation, machine run time or modifying the sample preparation protocol.

A single-step, high throughput, and highly reproducible method for measuring IgM quantity and avidity directly from fish serum via biolayer interferometry (BLI)

Quantification of specific antibody responses is critical in determining activation of MHCII-dependent immune memory and is generally performed by enzyme-linked immunosorbent assay (ELISA). Antibody avidity for a particular antigen is also informative of the quality of the adaptive immune response following vaccination. Avidity can be determined by chaotropic elution ELISA, pre-absorption ELISA, or surface plasmon resonance (SPR), although multimeric antibodies such as IgM are problematic for SPR. ELISA-based assays are very time consuming, require secondary antibody reagents, and are poorly repeatable. Here we demonstrate that biolayer interferometry (BLI) using an Octet HTX instrument can robustly and reproducibly quantify and determine avidity of specific IgM for an antigen directly from fish serum in a single step. We collected sera from giant grouper (Epinephelus lanceolatus) that had been vaccinated with the hapten 2,4-dinitrophenol conjugated to keyhole limpet hemocyanin (DNP-KLH) and from control fish injected with phosphate buffered saline. The specific IgM in the serum and its avidity for DNP were quantified via ELISA and BLI. BLI was precise and highly repeatable for determination of the quantity and avidity of antibody in the serum compared to ELISA. The wet-lab preparation and machine running time for BLI was 3–5 times faster than ELISA to generate the same amount of data. The ELISA inter-plate variation significantly affected reproducibility while BLI was consistent and repeatable between samples and plates. Indeed, the consistency of BLI data indicated that technical triplicates were redundant. Biological replication alone was sufficient to elucidate the effect of treatments. However, BLI required a lower serum dilution than ELISA for similar sensitivity, and thus more serum was required to produce high resolution data. BLI is an extremely high-throughput assay, providing teleost serum IgM quantification and avidity data as a single-step, agile alternative to ELISA.

Role of sublimation kinetics of ammonia borane in chemical vapor deposition of uniform, large-area hexagonal boron nitride

Hexagonal boron nitride (h-BN) is a critical 2D insulator used as a substrate, gate dielectric, or encapsulation layer for graphene and other 2D materials and their van der Waals heterostructures. It is also promising as an active layer in single-photon emitters and other photonic devices. With the chemical formula H3N-BH3, ammonia borane is the most attractive precursor for up-scalable growth of large-area h-BN, using chemical vapor deposition given its stoichiometric B:N ratio, high stability under ambient conditions, nontoxicity, and high solubility in common solvents. Here, the synthesis of large-area (100 × 150 mm2) crystalline hexagonal boron nitride layers by thermal activation and decomposition of the precursor ammonia borane is presented. We describe two different reaction pathways for h-BN synthesis, providing evidence for dissimilarities in the sublimation kinetics of ammonia borane and how these differences critically influence the growth of h-BN. This understanding helps us accelerate h-BN production, reuse precursors, and reduce machine runtime, paving the way for upscalability. Moreover, our work provides a consistent unified view explaining the diverse deposition conditions reported in the literature for h-BN grown by CVD using ammonia borane as a precursor.

Design and Modeling of Automatic Cow Dung Log Making Machine

This paper presents automatic cow dung log making machine. Cow dung logs used as fuel for many purposes such as cooking, heating boilers, chambers and many other purposes. The main aspect is to automate the cow dung log making process. The purpose of study is to replace manual production to semi-automated process and also to increase production rate. The advantage of the process is to increase the production rate of cow dung logs and bhasma at minimum runtime of machine with respect to the environmental condition.

ПРОЦЕС БАЛАНСУВАННЯ ШПИНДЕЛІВ ШЛІФУВАЛЬНИХ ТА ШВИДКІСНИХ ФРЕЗЕРУВАЛЬНИХ ВЕРСТАТІВ У ДВОХ ПЛОЩИНАХ КОРЕКЦІЇ

Urgency of the research. In connection with the creation of new types of Metalworking equipment and other machines that increase labor productivity, there is a problem of centering and balancing unbalanced technological systems during their manufacture and regime change of imbalances during operation. Currently, most grinding machines and high-speed milling machines provide manual balancing of the grinding wheel and the milling tool. The disadvantage of this method is the need to stop the equipment or change the technological and operational modes of operation. A more effective way is to integrate balancing devices into the kinematic structure of a grinding or milling machine and perform balancing automatically during the machine's running time. Target setting. When implementing automatic balancing of the spindles of grinding and high-speed milling machines on the move of the machine in two planes of correction, expensive, complex vibration measuring equipment, electronic control units and comparison of machine vibrations are necessary. In addition, the technology of automatic balancing on the move of the machine to compensate for moment and dynamic imbalance requires solving the problem of reducing residual imbalance while increasing the reliability of the drive and other devices. Analysis of recent research and publications. The work considered the latest publications on this topic, which are presented in the public domain, including the Internet. Uninvestigated parts of general matters defining. A significant proportion of current approaches to balancing the spindles of grinding and milling machines during operation require stopping the machine and balancing in manual mode. In determining the load on the spindle due to an imbalance, we also should note that the dynamic cutting forces (e.g., caused by intermittent operation of the cutter or grinding wheel wear) are often much higher than centrifugal forces caused by admissible residual imbalances. When automatically balancing the spindles of grinding and high-speed milling machines, it is necessary to correctly select the position of the correction planes relative to the grinding and milling tools. Therefore, determining the parameters of the imbalance in the two planes of correction and the condition of automatic balancing is an urgent part of the overall problem that is being solved in this scientific study. The research objective. The purpose of this scientific work is to develop a method for automatic balancing of spindles of grinding and high-speed milling machines in two correction planes with the determination of the main imbalance vector and representation of the geometric (mass) characteristics of the rotor through equivalent components in two correction planes. The statement of basic materials. When processing workpieces on high-precision grinding machines and high-speed milling machines, deviations from the geometric accuracy and quality of a given surface are largely determined by the amount of imbalance that occurs when the spindle rotates. It is known that the technological imbalance is the result of such objective reasons as mismatch of the workpiece axis and the spindle axis, uneven mass distribution in local areas of the workpiece, radial runout of the workpiece and tool, different material density. On modern grinding and milling machines, the spindle speed is several thousand revolutions per minute, so each newly installed workpiece must be subjected to balancing, usually together with a chuck, mandrel or collet. Conclusions. As a result of the research, the directions of vectors of static and dynamic imbalance of spindles of grinding and high-speed milling machines after balancing in two correction planes are determined. By moving the correction plane along the axis of the machine spindle, the reactions in the supports increase or decrease, thereby regulating the compensation forces from the correcting masses, which allows automating the process of compensation for moment and dynamic imbalance.

Chemical Heat Packs as an Intervention to Prolong Ultrasound Battery Runtime

Point-of-care ultrasonography is a burgeoning field of practice and study. Although point-of-care ultrasonography has started to penetrate the field of wilderness medicine, its use in cold environments is often limited by poor battery performance. In the current study, we investigated the effect of chemical foot warmers on battery runtime of handheld ultrasound machines in cold weather. This prospective study used a balanced, crossover randomization design to compare handheld ultrasound machines exposed to cold weather with and without the application of chemical foot warmers. Time to power off, ambient temperature, air humidity, and wind speed were recorded for each trial and compared in a mixed-effects model analysis of variance. Ultrasound machines showed significantly decreased functional battery life with decreasing temperature (P<0.01) and increasing wind speed (P=0.01). Addition of the chemical foot warmer resulted in a statistically significant increase in handheld ultrasound machine runtime compared with controls (P<0.001). Overall machine runtime was increased by approximately 21.8 min (95% CI 16.2-27.3). The application of chemical foot warmers to handheld ultrasound machines exposed to cold environments prolongs usable battery life and, consequently, may allow for extended scanning time in austere and remote environments.

DESIGN AND IMPLEMENTATION OF A LANGUAGE SCANNER GENERATOR (KnitAutoGen)

The need for fast, efficient and simple scanner generator that has the primary responsibility to perform efficiently gave rise to this paper. This is due to the fact that, on daily basis new technologies arise which brings a great improvement on the design of computer architecture. However, attention was given to speed, run time and resource availability of the design machine to be used since lexical analysis has an impact on how the compiler works. This paper seeks to develop a lexical analyzer (scanner generator) automatically by specifying the lexemes patterns to a lexical analyzer generator and compiling those patterns into a code that functions as a lexical analyzer. The scanner accepts characters as input and breaks them down to produce tokens by grouping the characters and not deviating from specifications. The project employs one of the different methods of lexical analyzer generator to perform pattern-matching on text using regular expression over a global character set. The paper shows how input is matched and specifies what to do when a pattern is matched. This was achieved with the use of regular expressions (RE) which were converted to non-deterministic finite automata (NFA) or deterministic finite automata (DFA). The regular expression and the regular grammar were thus joined together mathematically. Various results are presented and further work on micro compilers was proposed. Keyword: Scanner generator, Regular expression, KnitAutoGen, DFA, NFA. DOI : 10.7176/CTI/8-02

ABC Analysis of an Aerospace Business Case on ALM Technologies in Aerospace and Defence Supply Chain

This article provides an analysis of an aerospace business case on additive layer manufacturing (ALM) process. Using activity-based costing (ABC), we can breakdown the cost drivers and understand the cost levers associated with ALM process. This can help us in finding the innovative ways to enhance the ALM process operation’s efficiency. We have considered an aircraft bracket manufacturing using the direct metal laser sintering process as a business case for our ABC analysis on additive layer manufacturing technologies in aerospace and defence supply chain. The additive layer manufactured components associated cost can be divided into design cost, production cost, post-processing cost and qualification cost. Each step in the additive manufacturing process performs at one organisation or across a supply chain based on the various internal and external suppliers. Also, there are overhead costs associated with each step of this additive manufacturing process. This article explains the cost involved in the component design and its relevance to supply chain. It explains the cost involved in the component production such as materials cost, machine set-up cost, machine runtime cost and final build plate removal cost. It also explains the cost involved in the post-processing such as heat-treating cost, machining cost and surface finish cost. The component process cost is required to qualify based on its end use. The component qualification cost is required to add significantly to the component overall cost including the labour cost and computer tomography scan cost. The overhead cost is depending on the individual operation and it assigns these costs appropriately. It computes the ABC for an aerospace bracket and concludes with analysis to make a convinceable business case for the use of innovative technology and to enable to identify the cost of drivers and levers, so it can drive business with more efficiently.

Self-learning Calculation for Selective Laser Melting

Selective laser melting (SLM) is increasingly used in the industrial production of metallic parts. This creates the need for an efficient and accurate quotation costing. The manufacturing costs of a part mainly result from the machine running time for coating and exposure. At the time of the offer calculation the final orientation of the part in the build chamber and the composition of the build job are typically not known. Addressing this need, this paper presents and evaluates different statistical based methods for an automated and self-learning calculation for SLM given a part's CAD data.

Deviations in frequency and mode of vibration in whole-body vibration training devices with long-term and regular use

Research regarding whole body vibration training (WBVT) and its practical use may be hindered by the fact that WBVT devices generate frequencies and/or modes of vibration different from their preset adjustments. This research aimed to clarify whether prolonged regular use can generate such deviations in frequency and mode of vibration.Three WBVT devices, each used for approximately 13 months in two research projects, were tested with an accelerometer before start of the 1st study, after four months, and after 13 months (the completion of the 2nd study). Divergences between the preset and measured frequencies were calculated for all measurements. Furthermore, the total harmonic distortion (THD), an index for signal deviations from a perfect sine wave, and the sum signal-to-modulation-noise-ratio (SMNR), an indicator of fidelity, were recorded.One device had a significantly larger machine run time than the other two, and it displayed the most pronounced signs of impaired function concerning frequency, mode of vibration, and random variability (SMNR) after prolonged use.These results indicate that prolonged use will result in divergences between the preset and actual applied frequencies as well as in the mode of vibration and other accuracy measurements.

Optimizing Progenitor Cell Mobilization in Patients with Myeloma: Effect of a Pre-Emptive Day 4 Plerixafor-Based Mobilization Algorithm

BACKGROUND: Peripheral blood progenitor cell (PBPC) mobilization strategies vary considerably. Optimization of this critical aspect of autologous hematopoietic stem cell transplantation (ASCT) requires efficient use of resources including apheresis kits, machine run time, nursing and cell processing time and consumables. Equally important is the patient experience, which is strongly influenced by collection days and caregiver time that reduces their economic productivity and raises their expenses. We developed a mobilization algorithm designed to collect the target number of cells on day 1 of collection. The algorithm utilizes pre-emptive day 4 plerixafor to maximize collection day peripheral blood (PB) CD34+ cell numbers. METHOD: We analyzed data on all patients with multiple myeloma undergoing PBPC mobilization between March 2014 and July 2016. All patients were mobilized with the intent of collecting sufficient numbers of progenitor cells to permit two ASCT procedures. Patients received filgrastim 10 mcg/kg daily x 4 days. Patients in whom the PB white blood cell (WBC) count was RESULTS: We identified 105 patients with MM. Median age was 61 years (range, 25 - 76) and 57% were female. Disease status at mobilization included: 7 CR, 14 stringent CR, 5 unconfirmed CR, 52 very good PR (VGPR), and 27 PR. The median day 4 PBCD34+ cell number was 19.9/uL (range, 0.8 - 118.7/uL), and median day 5 PBCD34+ count was 107.8/uL (range, 15.6 - 307.8/uL). Ninety percent of patients required plerixafor of which 16% (n = 17) received the 12 mg fixed dose. The median increase in day 4 to day 5 PBCD34+ cell count for patients receiving plerixafor was 6.6 fold (range, 1.5 - 56.4 fold). In patients not receiving plerixafor, the median increase was 1.8 fold (range, 1.5 - 2.6 fold). The median collection yield was 10.95 million CD34+ cells/Kg (range, 2.9 - 22.5 million CD34+ cells/Kg) no significant difference between patients who received standard dose or fixed dose plerixafor. By the criteria outlined above 96.2% of patients had an optimal mobilization i.e. ≥ 6 million CD34+ cells/Kg sufficient for 2 ASCT procedures with 94.2% achieving this with only 1 day of collection. Per our institutional criteria, 71.4% achieved a successful collection (> 8 million CD34+ cells/Kg) in 1 day. There was no significant difference in days of collection among patients receiving 4 or less cycles of lenalidomide versus those receiving more than 4 cycles (p value = 0.104). The median duration of collection was 399 minutes (range, 180 - 495 minutes) with a median collection efficiency (CE2) of 42.3%. The mean number of days of collection was 1.23 days (range, 1 - 2 days). The median transplanted CD34+ cell dose was 5.48 million CD34+ cells/Kg. All patients had hematopoietic recovery with median neutrophil and platelet engraftment of 11 days and 18 days, respectively. CONCLUSION : The pre-emptive use of plerixafor on day 4 is effective and results in a high percentage of optimal day 1 collections. The cost of a more liberal plerixafor algorithm is offset by the savings incurred from reduced collection day numbers with the majority of patients requiring only 1 day of collection. Cost savings include limiting days away from work and family for the patient and caregiver and decreasing expenses for travel and overnight stays. Reducing collection day numbers also reduces the impact on quality of life for patients, caregivers and family members. Collectively, these data demonstrate utility of the pre-emptive day 4 plerixafor-based mobilization algorithm described here. Disclosures Usmani: Amgen: Consultancy, Research Funding, Speakers Bureau; Sanofi: Consultancy, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Consultancy, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Onyx: Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Membership on an entity9s Board of Directors or advisory committees, Research Funding; BioPharma: Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Pharmacyclics: Research Funding; Britsol-Myers Squibb: Consultancy, Research Funding; Skyline: Membership on an entity9s Board of Directors or advisory committees, Speakers Bureau; Array: Research Funding; Millenium: Membership on an entity9s Board of Directors or advisory committees, Speakers Bureau; Novartis: Speakers Bureau. Jacobs: Magellan Health: Consultancy; Pharmacyclics: Consultancy, Speakers Bureau. Bhutani: Onyx, an Amgen subsidiary: Speakers Bureau; Bristol-Myers Squibb: Speakers Bureau; Takeda Oncology: Research Funding, Speakers Bureau; Prothena: Research Funding. Avalos: Seattle Genetics: Membership on an entity9s Board of Directors or advisory committees.

On the existence of a unique class of yield and failure criteria comprising Tresca, von Mises, Drucker–Prager, Mohr–Coulomb, Galileo–Rankine, Matsuoka–Nakai and Lade–Duncan

In this paper, it is mathematically demonstrated that classical yield and failure criteria such as Tresca, von Mises, Drucker–Prager, Mohr–Coulomb, Matsuoka–Nakai and Lade–Duncan are all defined by the same equation. This can be seen as one of the three solutions of a cubic equation of the principal stresses and suggests that all such criteria belong to a more general class of non-convex formulations which also comprises a recent generalization of the Galileo–Rankine criterion. The derived equation is always convex and can also provide a smooth approximation of continuity of at least class C 2 of the original Tresca and Mohr–Coulomb criteria. It is therefore free from all the limitations which restrain the use of some of them in numerical analyses. The mathematical structure of the presented equation is based on a separate definition of the meridional and deviatoric sections of the graphical representation of the criteria. This enables the use of an efficient implicit integration algorithm which results in a very short machine runtime even when demanding boundary value problems are analysed.