scholarly journals Performance measurement for offline inspections under variable interactions and inspection errors in low-volume production

2021 ◽  
Author(s):  
Verna Elisa ◽  
Genta Gianfranco ◽  
Galetto Maurizio ◽  
Franceschini Fiorenzo
Keyword(s):  
Potential Interaction ◽  
Quality Inspection ◽  
Manufacturing Companies ◽  
Inspection Errors ◽  
Novel Approach ◽  
Inspection Process ◽  
Volume Production ◽  
Low Volume ◽  
Definition Of ◽  
Variable Interactions

AbstractThe assessment of the performance of inspection strategies is a crucial element in the design phase of product quality inspections of manufacturing companies. The aspects that inspection designers need to consider include: (1) the typology of quality inspection, (2) the inspection variables involved, (3) the potential interaction between variables and (4) the presence of inspection errors. In particular, low-volume inspection design is critical due to the lack of historical data and the inadequacy of traditional statistical approaches. By considering these issues, this paper proposes a novel approach to support inspection designers in the prediction of offline quality inspection performance. The development of a probabilistic model based on the analysis of the possible variable interactions and inspection errors and the definition of some performance measures may successfully help designers in the early design stages of inspection process planning. The approach is supported by a practical application in the Additive Manufacturing field.

scholarly journals Potential of the Application of Additive Manufacturing Technology in European SMEs

2021 ◽  
Vol 20 (2) ◽  
Author(s):  
Erwin Rauch ◽  
Marco Unterhofer ◽  
Wasawat Nakkiew ◽  
Adirek Baisukhan ◽  
Dominik T. Matt
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Research Team ◽  
Manufacturing Technology ◽  
Manufacturing Companies ◽  
On Demand ◽  
Delivery Times ◽  
Industrial Sectors ◽  
Volume Production ◽  
Low Volume

Production companies are forced to react quickly to increasing individualisation, a trend towards on-demand production and shorter delivery times. The key to deal with the new challenges is the ability to change to low volume production of customised artefacts. New manufacturing strategies and technologies are necessary to meet these specific requirements. The transition from traditional or centralised manufacturing systems to decentralised and distributed manufacturing systems shows a possible way to achieve local on-demand production and customisation of products. To enable economic low volume production, the implementation of additive manufacturing as manufacturing technology is becoming an interesting option for many manufacturing companies like small and medium-sized enterprises. In this work, the authors define key validation criteria for the assessment of the potential of additive manufacturing. Based on these criteria and the NACE classification of industrial sectors, the research team identifies potential industry sectors for additive manufacturing. Using statistical data from EUROSTAT database, the research team finally quantifies the potential of additive manufacturing in European SMEs.

scholarly journals Learning Curves: A Novel Approach for Robustness Improvement of Load Forecasting

2021 ◽  
Vol 5 (1) ◽  
pp. 38
Author(s):  
Chiara Giola ◽  
Piero Danti ◽  
Sandro Magnani
Keyword(s):  
Load Forecasting ◽  
Learning Curves ◽  
Machine Learning Techniques ◽  
Essential Information ◽  
Set Size ◽  
Novel Approach ◽  
Learning Techniques ◽  
Energy Plant ◽  
Definition Of ◽  
The Right

In the age of AI, companies strive to extract benefits from data. In the first steps of data analysis, an arduous dilemma scientists have to cope with is the definition of the ’right’ quantity of data needed for a certain task. In particular, when dealing with energy management, one of the most thriving application of AI is the consumption’s optimization of energy plant generators. When designing a strategy to improve the generators’ schedule, a piece of essential information is the future energy load requested by the plant. This topic, in the literature it is referred to as load forecasting, has lately gained great popularity; in this paper authors underline the problem of estimating the correct size of data to train prediction algorithms and propose a suitable methodology. The main characters of this methodology are the Learning Curves, a powerful tool to track algorithms performance whilst data training-set size varies. At first, a brief review of the state of the art and a shallow analysis of eligible machine learning techniques are offered. Furthermore, the hypothesis and constraints of the work are explained, presenting the dataset and the goal of the analysis. Finally, the methodology is elucidated and the results are discussed.

Territorial capital as a company intangible

2016 ◽  
Vol 17 (1) ◽  
pp. 148-167 ◽  
Author(s):  
Mariachiara Barzotto ◽  
Giancarlo Corò ◽  
Mario Volpe
Keyword(s):  
Great Difficulty ◽  
Future Research ◽  
Manufacturing Companies ◽  
Policy Makers ◽  
Content Type ◽  
Capital Growth ◽  
Multiple Case ◽  
Definition Of ◽  
A Company

Purpose – The purpose of this paper is twofold. First, to explore to what extent being located in a territory is value-relevant for a company. Second, to understand if a company is aware of, and how it can sustain, the territorial tangible and intangible assets present in the economic area in which it is located. Design/methodology/approach – The study presents an empirical multiple case-study, investigating ten mid-/large-sized Italian companies in manufacturing sectors. Findings – The results indicate that the sampled manufacturing companies are intertwined with the environment in which they are embedded, both in their home country and in host ones. The domestic territorial capital has provided, and still provides, enterprises with workers endowed with the necessary technical skills that they can have great difficulty in finding in other places. In turn, companies support territorial capital generation through their activities. Research limitations/implications – To increase the generalisability of the results, future research should expand the sample and examine firms based in different countries and sectors. Practical implications – Implications for policy makers: developing effective initiatives to support and guide a sustainable territorial capital growth. Implications for managers and investors: improving managerial and investors’ decisions by disclosing a complete picture of the enterprise, also outside the firm boundaries. Originality/value – The study contributes to intangibles/intellectual capital literature by shedding light on the importance of including territorial capital in a company’s report to improve the definition of the firm’s value. Accounting of the territorial capital would increase the awareness of the socio-economic environment value in which companies are located and its use.

Neue Fertigungssteuerung in der Ingenieurkeramik*/New approach to manufacturing control in engineering ceramics

2015 ◽  
Vol 105 (03) ◽  
pp. 109-114
Author(s):  
U. Bracht ◽  
F. Arzberger ◽  
F. Schulenburg
Keyword(s):  
Lean Manufacturing ◽  
Lean Production ◽  
Control Unit ◽  
Manufacturing Processes ◽  
Small Companies ◽  
Complex Environment ◽  
New Approach ◽  
Manufacturing Control ◽  
Volume Production ◽  
Low Volume

Auch kleinere Unternehmen mit komplexen Herstellungsprozessen müssen heute in der Kleinserie die Effizienz und Geschwindigkeit in der Produktion erhöhen. Zentraler Bestandteil ist dabei eine schlanke Fertigungssteuerung in einem ganzheitlichen Produktionssystem. Der Fachbeitrag zeigt, wie auch bei hoher Komplexität wesentliche Ansätze der „Lean Production“ genutzt werden, um die Produktion von Ingenieurkeramiken durch die intelligente Vernetzung bereichsspezifischer Methoden zu optimieren.   Today, even small companies with complex manufacturing processes in low-volume production have to improve efficiency and speed in manufacturing. A core aspect is lean manufacturing control within an overall production system. This article shows how the main approaches of Lean Production can be applied even to a highly complex environment. The intelligent integration of specific methods for each control unit helps to enhance the production of ceramics.

scholarly journals Lean & Green on Industry 4.0 Context – Contribution to Understand L&G Drivers and Design Principles

2021 ◽  
Vol 6 (5) ◽  
pp. 1214-1229
Author(s):  
Lenin John ◽  
Manuel Sampayo ◽  
Paulo Peças
Keyword(s):  
Industry 4.0 ◽  
Research Work ◽  
Design Principles ◽  
Manufacturing Companies ◽  
Bottom Line ◽  
Industrial Firms ◽  
Systems Implementation ◽  
Sustainability Concept ◽  
Proper Definition ◽  
Definition Of

The purpose of this paper is to demonstrate how the implementation of Lean & Green (L&G) in an Industry 4.0 (I4.0) environment can enhance the potential impact of the L&G approach and help manufacturing companies moving towards higher operational and sustainable performances. The research work developed here shows that although a proper definition of L&G is neither exposed worldwide nor explicitly implemented under that name, the current industrial firms are deeply concerned about the demanding challenge of keeping businesses flexible and agile without forgetting strategies to minimize the acceleration of climate change. So, one contribution of this paper is the identification and characterization of L&G drivers and design principles, supporting a robust and well-informed L&G systems implementation. As inferred from the research work, this challenge demands high quality and updated data together with assertive information. Thus, the implementation of L&G in I4.0 contexts is the answer to overcome the identified barriers. Likewise, an L&G system contributes to overcoming the challenges of I4.0 implementation regarding the triple bottom line sustainability concept. Consequently, another contribution of this paper is to depict why an L&G system performs better in the I4.0 context.

scholarly journals A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages

2017 ◽  
Author(s):  
Gorka Urbikain ◽  
Luis Norberto López De Lacalle ◽  
Mikel Arsuaga ◽  
Alvaro Alvarez ◽  
Miguel A. Alonso
Keyword(s):  
Machine Tools ◽  
Manufacturing Companies ◽  
Machining Processes ◽  
Knowledge Based ◽  
Direct Benefits ◽  
Definition Of ◽  
The Right ◽  
Machining Operations ◽  
Physical Laws ◽  
Manufacturing Problems

The future of machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy and reliability. Nowadays, distortion and vibration problems are easily solved in labs for the most common machining operations by using models based on equations describing the physical laws of the machining processes; however additional efforts are needed to overcome the gap between scientific research and the real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on “deep-knowledge and models” that aid machine designers, production engineers or machinists to get the best of the machine-tools. This article proposes a methodology to reduce problems in machining by means of a simulation utility, which uses the main variables of the system&process as input data, and generates results that help in the proper decision-making and machining planification. Direct benefits can be found in a) the fixture/clamping optimal design, b) the machine tool configuration, c) the definition of chatter-free optimum cutting conditions and d) the right programming of cutting toolpaths at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies and are explained in the paper.

scholarly journals A network-based approach to integrate nutrient environment in the prediction of synthetic lethality in cancer metabolism

2021 ◽  
Author(s):  
Iñigo Apaolaza ◽  
Edurne San José-Enériz ◽  
Luis Valcarcel ◽  
Xabier Agirre ◽  
Felipe Prosper ◽  
...  
Keyword(s):  
Cancer Metabolism ◽  
Synthetic Lethality ◽  
Experimental Proof ◽  
Synthetic Lethal ◽  
New Family ◽  
Novel Approach ◽  
Synthetic Lethal Interactions ◽  
Nutrient Environment ◽  
Tumor Environment ◽  
Definition Of

Synthetic Lethality (SL) is a promising concept in cancer research. A number of computational methods have been developed to predict SL in cancer metabolism, among which our network-based computational approach, based on genetic Minimal Cut Sets (gMCSs), can be found. A major challenge of these approaches to SL is to systematically consider tumor environment, which is particularly relevant in cancer metabolism. Here, we propose a novel definition of SL for cancer metabolism that integrates genetic interactions and nutrient availability in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase inhibition in different environments. Finally, our novel approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies.

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